Systems and methods to improve in-plane image resolution in computed tomography

Abstract

The described technology is generally directed towards improving in-plane image resolution in a computed tomography (CT) system. A combination of system geometry arrangement in conjunction with a multi-integrator design on the detector data-acquisition system can be utilized, in conjunction with information available in the signal processing stages to recover resolution while ensuring minimal impact to image noise. Furthermore, focal spot position(s) can be incorporated in the image reconstruction algorithm to further alleviate factors that degrade image resolution and hence achieve enhanced in-plane image resolution. The various embodiments enable an improvement in the image resolution (e.g., in-plane) of a system without any change in the tube / detector design / specification.

Description

SYSTEMS AND METHODS TO IMPROVE IN-PLANE IMAGE RESOLUTION IN COMPUTED TOMOGRAPHY CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to India Provisional Patent Application No. 202341066974 filed on October 6, 2023, entitled “SYSTEMS AND METHODS TO IMPROVE IN-PLANE IMAGE RESOLUTION IN COMPUTED TOMOGRAPHY”. The entireties of the aforementioned application are incorporated by reference herein. TECHNICAL FIELD

[0002] This disclosure generally relates to an imaging system and more particularly to system and method for improving the in-plane image resolution or modulation transfer function (MTF) of a Computed Tomography (henceforth referred to as CT) imaging system. BACKGROUND

[0003] A computed tomography scan (CT scan) is an imaging technique used to obtain detailed sectional images of a patient, an animal, or an object. In an aspect, CT scans can be three-dimensional (3D) constructions based on two-dimensional (2D) scans / slices. A conventional CT system comprises an X-ray tube and a detector positioned opposite each other in a gantry, wherein, the X-ray tube and the detector rotate about an object, with the detector capturing X-ray attenuations resulting from different tissues, masses, structures, etc., of which the object is formed. Computer processing of the multiple X-ray measurements obtained from different angles of rotation produces tomographic (cross-sectional) images providing 2D virtual slices of the 3D object being scanned.

[0004] The above-described background description is merely intended to provide a contextual overview regarding X-ray CT imaging systems and is not intended to be exhaustive. SUMMARY

[0005] The following presents a simplified summary of the disclosed subject matter to provide a basic understanding of one or more of the various embodiments described herein. This summary is not an extensive overview of the various embodiments. It is intended neither to identify key or critical elements of the various embodiments nor to delineate the scope of the various embodiments. The sole purpose of the Summary is to present some concepts of the disclosure in a streamlined form as a prelude to the more detailed description that is presentedlater.

[0006] In one or more embodiments described herein, systems, devices, computer- implemented methods, configurations, apparatus, and / or computer program products are presented improve in-plane image resolution in a computed tomography (CT) system.

[0007] According to one or more embodiments, a CT system is presented, wherein the CT system comprises at least one processor, and a memory coupled to the at least one processor and having instructions stored thereon. In response to the at least one processor executing the instructions, the instructions facilitate performance of operations, comprising acquiring a set of x- ray projections at a detector, wherein the set of x-ray projections are acquired during a rotation of a gantry in the CT system imaging an object, and the set of x-ray projections are acquired based on at least one of an integration angle or effective integration time per projection. In an embodiment, the operations can further comprise generating a line spread function (LSF) model depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter of the CT system and a defined sampling schema. In a further embodiment, the operations can further comprise recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space, and further incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in-plane resolution of a reconstructed image while simultaneously improving image noise characteristics.

[0008] In another embodiment, the operations can further comprise modifying operation of two or more charge integrators at the detector, wherein the one or more charge operators are configured to incorporate multiple integrators configured in parallel to one or more detector elements and be read out at a same frequency but with a uniformly distributed phase shift over the two or more integrators to increase a number of view samples per one turn of gantry rotation while maintaining the integration time per view sample, wherein the modification of the operation of the two or more charge integrators improves view sampling.

[0009] In another embodiment, the operations can further comprise retrospectively summing consecutive views in an overlapping moving window fashion while maintaining the total number of view samples per one turn of the gantry, to improve the integration angle.

[0010] In a further embodiment, the sampling schema is implemented in conjunction with at least one system parameter relating to a detector aperture and an integration arc length to facilitate computation of an energy spread of a line integral relating to a LSF in the projection direction.

[0011] In another embodiment, the operations can further comprise recovering afrequency of the measured data in conjunction with the LSF model to increase data resolution in a sinogram space.

[0012] In an embodiment, the recovering of the frequency is implemented in at least one of: a data acquisition hardware configured to process a stream of consecutive views in a field- programmable gate array (FPGA), an algorithm implemented by the at least one processor, or a deep learning algorithm trained on the LSF model to recover the frequency of the measured data to increase the data resolution in sinogram space.

[0013] In another embodiment, the operations can further comprise implementing a resolution recovery algorithm as a filter in the frequency domain, wherein the resolution recovery algorithm is configured to match with the LSF model.

[0014] In an embodiment, the operations can further comprise implementing a resolution recovery algorithm as a deconvolution algorithm in the spatial domain.

[0015] In another embodiment, the operations can further comprise implementing a resolution recovery algorithm as an unsharp masking algorithm using a deconvolution kernel derived from the LSF model in the spatial domain.

[0016] In a further embodiment, the system parameters utilized as part of the reconstruction process can comprise incorporated knowledge of the sampling positions of the conjugate line integrals, wherein the operations can further comprise generating an up-sampled sinogram data, wherein the up-sampled sinogram data is configured for subsequent resolution recovery.

[0017] In another embodiment, the operations can further comprise up-sampling the sinogram based on interlacing of conjugate line integral samples.

[0018] In a further embodiment, the operations can further comprise up-sampling based on a constrained least squares solver configured to iteratively estimate a higher resolution sample from low resolution measurements using the conjugate line integral samples in conjunction with the LSF model to minimize estimation error of an initial measurement.

[0019] In a further embodiment, the system parameters utilized as part of the reconstruction process can be updated, during the reconstruction process, with the focal spot position per projection sample, to improve the fidelity of the computed tomographic reconstruction and restore any blur and resolution loss induced by assuming fixed focal spot position.

[0020] In another embodiment, the focal spot position can be determined by at least one of: experimental modeling estimated over multiple data acquired of a known object to deduce the focal spot position model as a function of projection angle over multiple gantry rotation times; acomputational model deduced by mechanical forces on the tube to derive the focal spot positional variation as a function of gantry angle over multiple gantry rotation times; or a system comprising of beam blocker placed close to the source near collimator aperture in the fan direction without any interference from any object in the beam path to derive the focal spot motion as a proportional consequence of the measured signal variation on the detector due to projected shadow from the beam blocker.

[0021] In other embodiments, elements described in connection with the disclosed systems can be embodied in different forms such as computer-implemented methods, computer program products, or other forms. In an embodiment, the computer-implemented method can comprise automatically modifying, by a device comprising at least processor, the ISO-center alignment geometry of a detector relative to an X-ray source focal spot in a CT system, wherein the modification causes improved conjugate sampling over one turn of gantry rotation, further automatically recovering, by the device, a frequency of measured data obtained by a sampling schema to increase data resolution in a sinogram space, and further automatically incorporating, by the device, one or more system parameters into an image reconstruction process to propagate the restored data resolution into an improvement in the in-plane resolution of a reconstructed image while ensuring minimal impact to image noise in the reconstructed image.

[0022] In an embodiment, the one or more system parameters utilized as part of the reconstruction process comprise incorporated knowledge of the sampling positions of the conjugate line integrals, wherein the computer-implemented method can further comprise automatically generating, by the device, an up-sampled sinogram data, wherein the up-sampled sinogram data is configured for subsequent resolution recovery.

[0023] In another embodiment, modification of the ISO-center alignment geometry of the detector relative to the X-ray source focal spot can be achieved using a pair of actuators, wherein the pair of actuators are programmed at least one of prior to a scan acquisition or dynamically during a scan acquisition to generate a motion on the detector position to facilitate improved conjugate sampling over one turn of gantry rotation, wherein the detector can be placed with an edge detector offset alignment or a programmable offset.

[0024] Another embodiment can further comprise a computer program product stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein, in response to being executed, the machine-executable instructions cause a system to perform operations, comprising (a) acquiring a set of x-ray projections at a detector, wherein the set of x-ray projections are acquired during a rotation of a gantry in the CT system imaging an object, and the set of x-ray projections are acquired based on at least one of an integration angleor effective integration time per projection, (b) generating a line spread function (LSF) model depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter of the CT system and a defined sampling schema, (c) recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space, and (d) incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in-plane resolution of a reconstructed image while simultaneously improving image noise characteristics.

[0025] In a further embodiment, the machine executable instructions can further comprise implementing an edge detector offset alignment schema, deriving a sampling schema configured to improve radial view sampling while independently controlling the view integration angle, determining a focal spot deviation, implementing resolution recovery to reduce image blur of a reconstructed image, or identifying one or more image frequencies producing blur in the LSF model.

[0026] In another embodiment, the machine executable instructions can further comprise implementing at least one resolution recovery process during conversion of measured signals received from the detector to imaging data, wherein the conversion process applies a view sampling schema to the measured signals, and further implementing at least one resolution recovery process during sharpening of the image data, wherein the sharpening process applies at least one of a LSF model, system geometric parameters, or focal spot estimation data to the image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] One or more embodiments are described below in the Detailed Description section with reference to the following drawings:

[0028] FIGS.1A and 1B present schematics of an example CT system configured to perform CT imaging in accordance with one or more embodiments presented herein.

[0029] FIG.2 presents a schematic of an example CT system configured to control and monitor various mechanisms and process data, e.g., to achieve higher in-plane resolution imaging, in accordance with one or more embodiments.

[0030] FIG.3A-C present schematics of a CT detector assembly, in accordance with one or more embodiments.

[0031] FIG.4 presents a schematic illustrating a geometry of an example CT system, in accordance with one or more embodiments.

[0032] FIGS.5A and 5B present schematics illustrating a geometry of an example CT system and respective positions of an X-ray source and a detector, in accordance with one or more embodiments.

[0033] FIGS.6A-C present various schematics depicting sampling schema, in accordance with an embodiment presented herein.

[0034] FIGS.7A-D present schematics illustrating focal spot position behavior during a scanning operation.

[0035] FIGS.8A-F present schematics utilizing resolution recovery to reduce blur in conjugate data to improve resolution of a reconstructed image, in accordance with one or more embodiments presented herein.

[0036] FIG.9 presents a chart illustrating plots of modulation transfer function (MTF) in percentage (normalized) versus frequency as a factor of Nyquist units across multiple images, in accordance with one or more embodiments presented herein.

[0037] FIG.10 presents a plot illustrating a plot of normalized noise to the baseline image versus the normalized MTF 5% as a factor of Nyquist limit of the system.

[0038] FIG.11, per an example flowchart, presents a computer-implemented method for increasing in-plane image resolution, according to one or more embodiments.

[0039] FIG.12 presents an example computer-implemented method for increasing in- plane image resolution in CT system, in accordance with an embodiment.

[0040] FIG.13 presents an example computer-implemented method for increasing in- plane image resolution in CT system, in accordance with an embodiment.

[0041] FIG.14 presents an example computer-implemented method for increasing in- plane image resolution in CT system, in accordance with an embodiment.

[0042] FIG.15 presents an example environment for implementing various embodiments presented herein.

[0043] FIG.16 illustrates an example wireless communication system, in accordance with one or more embodiments described herein.

[0044] It is to be appreciated that the figures illustrated in the drawings are not to scale. Wherever possible, the same reference numbers have been used throughout the drawings and the accompanying written description to refer to the same or like parts. DETAILED DESCRIPTION

[0045] The following detailed description is merely illustrative and is not intended to limit embodiments and / or application or uses of embodiments. Furthermore, there is no intentionto be bound by any expressed and / or implied information presented in any of the preceding Background section, Summary section, in the Detailed Description section, and / or the Abstract.

[0046] One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.

[0047] As used herein, “data” can comprise metadata. Further, ranges A-n are utilized herein to indicate a respective plurality of devices, components, signals etc., where n is any positive integer.

[0048] 1. OVERVIEW

[0049] A conventional CT scan system comprises an x-ray source and a detector array both configured to rotate on a gantry within an imaging plane and around an object to be imaged such that the angle at which an X-ray beam intersects the object constantly changes. A group / set of X-ray attenuation measurements, i.e., projection data, from the detector array at one gantry angle is referred to as a view or projection. A scan of the object comprises a set of views generated at different gantry angles, or view angles, during a revolution of the X-ray source and detector around the object. The angle that a line joining the focal spot to the ISO-center makes with the y-axis is referred to as the view or projection angle, β. A scan of an object comprises a set of views made at different gantry angles, or view angles (β), during one revolution of the X- ray source and detector. The projection data can be processed to reconstruct an image corresponding to a two-dimensional (2D) slice taken through the object.

[0050] In-plane resolution of the reconstructed image is measured using Modulation Transfer Function (MTF) and is a desired property of CT scanner for resolving capability to visualize anatomy and pathology in greater detail / resolvable distance between two objects. Resolution of a reconstructed image can depend on CT system geometry, e.g., finite focal spot size and finite detector aperture size, source to detector distance, and the like, and implementation of one or reconstruction algorithms. Various developments / approaches are available with CT technologies to impact the in-plane resolution.

[0051] Regarding X-ray tube side technologies, a reduction in focal spot size can improve the in-plane resolution but such reduction is bound by physical limitations, constraining the degree to which improvements can be achieved. In an aspect, target temperature rise is proportional to the flux and flux per unit area of the track, e.g., at a detector element. Higher fluxfrom a smaller focal spot region can give rise to higher thermal capacity making it challenging for thermal management of the tube target. Also, as the charge concentration increases at the target location, the probability of dispersion is increased, as electrons repel each other. Accordingly, challenges exist regarding focusing with a reduction in focal spot size.

[0052] Another design approach on the X-ray tube side can utilize wobbling / oscillating the focal spot to improve sampling. Focal spot wobble in the x-direction can increase the sampling density by deflecting the X-ray focal spot, and by further increasing the trigger frequency, it is possible to capture intermediate samples.

[0053] The above approaches involve respective configurations of the tube and additional changes in the CT system design to control the focal spot and sampling. The available approaches can additionally involve maintaining an integration time for signal-to-noise ratio (SNR) at the dual sampling positions and in turn reducing the gantry rotation time to maintain the SNR.

[0054] Per recent advancements in detector technologies, the highest possible spatial resolution of a CT system is primarily determined by the Nyquist of the detector size. Reducing the size of the detector element can increase the dead space fraction, however, such an approach fundamentally decreases the dose efficiency while improving resolution. Hence, there is a physics limitation to lowering / reducing the physical size of a detector.

[0055] Another approach employed to improve in-plane resolution MTF involves alignment modification between a source focal spot and the detector with reference to each other and the ISO-center of the rotational system. Quarter detector offset is an approach comprising a shift of the detector from the ISO-center by a quarter of a detector width to enable improved sampling and reduce aliasing in the resultant back-projected image after a filtered back projection (FBP)-based reconstruction.

[0056] In-plane resolution is accordingly governed and impacted by multiple system parameters like the focal spot size, distance from source to detector and ISO-center alignment, detector aperture size. and other signal processing algorithmic parameters like the filtration kernels employed to reconstruct the image. The reconstruction algorithms in conjunction with other signal conditioning algorithms tend to primarily trade-off the resolution measured as MTF with image noise. The best achievable MTF is still limited by the system parameters.

[0057] The in-plane resolution in a CT system is also governed by radial sampling density. This is the number of projections acquired per turn of the gantry rotation.

[0058] Another aspect impacting the resolution is the per projection integration angle, this governs the region of angular spread between two read outs from a detector during the X-rayON phase with continuous rotation of the gantry. This leads to what is referred to as projection blur due to continuous X-ray integration during rotation of the gantry.

[0059] The amount of time that it takes to integrate a projection between two readouts is referred to as the integration time of a projection. Integration time of a projection is an important parameter that governs the image quality for a given dose flux from the X-ray tube. Longer integrations time give improved SNR, however, longer integration times leads to an increase in the rotation time if required to maintain sufficient radial sampling density in a single rotation. Hence there is a tradeoff between projection integration angle and number of projection samples measured per turn of gantry rotation. This leads to a natural tradeoff between in-plane resolution limited by the number of radial projection samples and the image noise governed by the integration time.

[0060] Larger detector apertures are preferred as they are inherently more dose efficient but lead to poorer resolution limits as opposed to smaller detector aperture systems that provide improved resolution but suffer from poor noise characteristics for a similar detector material and signal conditioning chain.

[0061] While a large number of views with sufficient integration time give good image quality on resolution and image noise characteristics, the longer rotation time impacts the temporal resolution of an image as the time to acquire one turn of projections increases. Faster gantry rotations are preferred for quicker acquisitions, workflow management and snapshotting the body parts at small time interval to minimize the motion artifacts. These improve the temporal resolution of an image and thus pose a challenge to image quality from limited projection sample density to maintain minimum integration time for acceptable image noise levels.

[0062] Conventional CT systems are limited with regard to the maximum achievable sampling in the radial direction, e.g., due to detector quantum efficiencies and detector aperture effects that govern the minimal integration time required to meet image noise characteristics. This in turn coupled with the need to have faster rotation times for enhanced temporal resolution pose a challenge to improve in-plane image resolution under these constrained acquisition settings.

[0063] The various embodiments presented herein relate to systems and methods for increasing in-plane resolution of a given detector in a CT system, wherein the in-plane resolution is at the Nyquist achievable limit and further extends into the supra-nyquist region of the CT system without compromising the integration time, and further providing improved noise characteristics.

[0064] The various embodiments presented herein provide a combination of system modification in conjunction with improved super-resolution sampling techniques and computational resolution recovery techniques to achieve higher in-plane resolution and improve the noise characteristics.

[0065] In an embodiment, a CT system is presented to improve the in-plane resolution to the detector Nyquist achievable limit and into the supra-nyquist region of the system without compromising the integration time. The CT system allows for independent control over the number of samples of X-ray projections collected over one turn of gantry rotation and integration angle and / or effective integration time per projection. By incorporating the previously mentioned system geometry parameters and sampling schema, a line spread function (LSF) model of the energy distribution of the line integral in the view sampling direction can be generated. At least one resolution recovery component can be configured to perform a recovery of a frequency of the measured data in conjunction with the LSF model, thereby increasing the data resolution in sinogram space that when incorporated with the system parameters in the image reconstruction process propagates this restored data resolution into an improvement in the in-plane resolution of a reconstructed image while simultaneously improving image noise characteristics. Further, the CT system alignment geometry can be modified to improve the sampling of conjugates / conjugate rays, for example. Improvement in view sampling can also be achieved with a modification to the charge integrators at a detector data acquisition system to incorporate multiple integrators which can be configured in parallel to the detector photodiodes and further read out at the same frequency but with a uniformly distributed phase shift over the number of integrators to increase number of view samples per one turn of gantry rotation while maintaining the integration time per view sample.

[0066] Per the various embodiments presented herein, various systems and computer- implemented methods are presented to process measurements generated by the CT system to recover and restore the high frequency content of an object embedded in the low frequency content of the measurements. In an embodiment, a deconvolution algorithm can be implemented in the view direction. In another embodiment, view support for at least two adjacent views can be obtained. The view data of each detector can then be deconvolved by a computed LSF model to regenerate the respective view data. A LSF model for a view direction enables recovery of the view direction blur and achieve in-plane resolution in accordance with the detector aperture limitation of the Nyquist. The various embodiments enable an existing X-ray tube with a finite focal spot and an existing detector with large finite aperture to achieve the highest achievable resolution, and only limited by CT system geometry parameters.

[0067] The various embodiments presented herein enable dynamically managing view sampling schema, further enabling independent control over the number of samples of x-ray projections collected over / during one turn of gantry rotation, integration angle, and / or effective integration time per projection,

[0068] Various system geometry parameters and sampling schema can be utilized to generate the LSF model of the energy distribution of the line integral in the view sampling direction.

[0069] One or more recovery components can be configured to perform a recovery of a frequency of the measured data in conjunction with the LSF model to increase the data resolution in sinogram space.

[0070] Further, the various system parameters can be utilized during an image reconstruction process to propagate the restored data resolution into an improvement in the in- plane resolution of a reconstructed image while simultaneously improving image noise characteristics.

[0071] The various embodiments presented herein relate to how to increase the in-plane resolution of a CT system. As mentioned, the size of each cell in a z-direction is m (width), and can govern the slice sensitivity profile, a.k.a., the z-resolution. Resolution in the x-y plane, the in-plane resolution, is governed by n, the horizontal measure of width of a cell, wherein n is in the fan direction of which an x-ray spreads. Typically, the greater number of cells in the x-y plane, potentially the finer the sampling, the finer the in-plane resolution, per the aforementioned MTF for a particular field of view (FOV). In-plane resolution governs the smallest structure that can be resolved in the x-y direction of a CT image. During one rotation of the gantry, an image is obtained, of concern is the level of resolution between two points on the image, wherein the resolution (e.g., per the MTF) is typically measured in line pairs per centimeter (lp / cm). A CT system can be considered high resolution once the lp / cm is in the order of 15-25 lp / cms, while a typical CT system may operate at 5-8 lp / cm. Hence, MTF, or the in-plane resolution governs the smallest structure that can be resolved in the image between two points on the image. In an aspect, a scan may include many samples, but if fine structures cannot be resolved, it does not matter the number of samples or number of pixels (e.g., in a matrix). Accordingly, the various embodiments presented herein relate to increasing the in-plane resolution of a CT system having a finite detector size limited by Nyquist considerations.

[0072] In one or more embodiments, an edge detector offset alignment configuration and schema can be utilized to obtain measured data, whereby, with edge detector offset alignment, the central ray in the beam passes at the edge of a detector, rather than at a quarter from the edge, pera conventional operation utilizing a quarter detector offset (e.g., per FIGS.5A-B) The conjugates resulting from the edge detector offset alignment can be subsequently interlaced / resolution recovered during generation of respective sinograms (e.g., per FIGS.8A-F). Further, a sampling schema should also take into consideration issues pertaining to radial view sampling and independently controlling the view integration angle (e.g., per FIGS.6A-C), e.g., by utilizing a line spread function (LSF) to deconvolve data included in the respective sinograms. Further (per FIGS.7A-D), when generating a high-resolution image / sinogram, consideration is also taken regarding focal spot deviation to compensate / address issues arising from gravity / motion of the tube and / or detector. The combination of respective embodiments, operations, processes enable improved in-plane image resolution to be achieved compared with that achievable with one or more conventional approaches that do not utilize the combination presented herein, as shown in FIGS.9 and 10.

[0073] It is to be appreciated that while the various embodiments presented herein are directed towards medical or healthcare systems, the embodiments are not so limited and are applicable to any suitable imaging technologies and techniques.

[0074] 2. CT SYSTEM OVERVIEW

[0075] FIG.1A presents a schematic of an example CT system 100A configured to perform CT imaging in accordance with one or more embodiments presented herein.

[0076] As shown, CT system 100A can be configured to image a subject 106. Subject 106 can be a human, an animal, a living entity, an inanimate object, etc. For example, subject 106 can be a human patient, one or more manufactured parts, as well as foreign objects such as dental implants, stents, and / or contrast agents present within the body of subject 106. CT system 100 can also be employed to scan three-dimensional (3D) structures with pre-defined geometries and material properties, known as phantoms.

[0077] In various embodiments, CT system 100 can include a gantry 102, wherein gantry 102 can be further configured to include at least one X-ray source 104 (a.k.a. X-ray tube) configured to project X-ray radiation rays / beam(s) 110A-n (per FIG.1B) to perform CT imaging of subject 106. A table 108 (a.k.a., cradle or bed) can be utilized for positioning subject 106 in the CT system 100 during CT imaging. X-ray source 104 can be configured to project X-ray radiation beam(s) 110A-n towards a detector 112 (per FIG.1B) positioned on an opposite side of gantry 102 from the X-ray source 104. It is to be appreciated that while FIG.1A only depicts a single X-ray source 104, multiple X-ray sources 104A-n and detectors 112A-n can be utilized to project a plurality / series of X-ray radiation beams 110A-n to acquire projection data at different energy levels corresponding to subject 106. For example, in one or more embodiments, two setsof X-ray sources 104A / B and detectors 112A / B can be employed to generate dual-energy projections, with a first set 104A / 112A operating at a low kilovoltage (kVp) value, and a second set 104B / 112B operating at a high kVp value. Accordingly, the various embodiments presented herein are equally applicable to single energy acquisition techniques / technologies as well as dual energy acquisition techniques / technologies. In an example embodiment, X-ray source 104 can enable dual-energy gemstone spectral imaging (GSI) by rapid peak kilovoltage (kVp) switching. In another embodiment, detector 112 can be a photon-counting detector capable of differentiating X-ray photons of different energies.

[0078] In an example configuration, X-ray source 104 can project a cone-shaped X-ray radiation beam (e.g., X-ray radiation beams 110A-n) which can be collimated to lie within an x- y-z plane of a Cartesian coordinate system, also known as an "imaging plane”. The x, y, and z axes of the imaging plane are illustrated at FIGS.1B, 4, etc., with the references to the individual x, y, and z axes throughout this specification are consistent with the orientation illustrated in FIG. 1B. In various embodiments, X-ray radiation beam 110A-n can pass through a subject 106 being imaged, and after being attenuated by the subject 106, X-ray radiation beams 110A-n can impinge / be incident upon detector 112. The intensity of the attenuated X-ray radiation beams 110A-n received at detector 112 can be dependent upon the attenuation of X-ray radiation beams 110A-n by the subject 106. In an embodiment, detector 112 can produce an electrical signal (e.g., as data 114A-n) indicative of measurements of the X-ray beam attenuation at detector 112. The CT imaging system 100A can be configured to measure attenuation along the path of an X- ray beam 110A-n between the X-ray source 104 and the detector 112.

[0079] As shown, CT system 100 can further include a computer system 180 (a.k.a., computer device) comprising at least one processor 182 and a memory 184, as further described.

[0080] FIG.1B presents a schematic of an example CT system comprising a CT scanning machine coupled to an operator console in accordance with one or more embodiments described herein. FIG.1B further expands on elements and concepts described in the various embodiments presented with system 100A. The terms CT system 100A, CT system 100B, CT system 100, and the like, are used interchangeably herein regarding components and functionality available across the CT system 100 / 100A / 100B. Further, the terms CT system, CT scanning system, CT scanner, and the like, are used interchangeably herein.

[0081] In accordance with one or more embodiments of the subject application, FIG.1B illustrates an example, CT system 100B configured to perform CT imaging of subject 106. CT system 100B can include, or be of similar configuration to, CT system 100A presented in FIG. 1A. As previously mentioned, subject 106 can comprise one or more of a patient, a phantom,and / or other contrast agents present within the volume (e.g., body of a subject 106). As further previously mentioned, CT system 100B can include a detector 112. Detector 112 can further comprise an array of detectors comprising a plurality / set / group / modules / collection of detector elements 120A-n (a.k.a., cells, photodiodes) (e.g., per FIG.3) that can be configured to collectively sense X-ray radiation beam 110A-n, e.g., after X-ray radiation beam 110A-n passes through subject 106, to acquire the corresponding projection data (e.g., data 114A-n). Detector elements 120A-n can have a charge integrator 122A-n respectively attached thereto (e.g., per FIG.3), wherein charge integrators 122A-n can be configured to convert X-ray charge (e.g., from X-ray beams 110A-n) at the detector element(s) 120A-n into measured data 114A-n. The set of detector elements 120A-n can be arranged in a single monotonous curve or a piecewise linear pattern along the X-axis with reference to the imaging plane. In an example configuration, detector 112 can be fabricated in a multi-slice configuration comprising multiple rows of detector elements 120A-n. In such a configuration, one or more additional rows of detector elements 120A-n can be arranged in a parallel configuration. As further described, the respective position of rows of detector elements 120A-n, portions of rows of detector elements 120A-n, and / or individual detector elements 120A-n can be configured / adjusted during initial fabrication of the detector 112, and / or during operation of the CT system 100.

[0082] During imaging, X-ray source 104 and detector 112 can rotate in conjunction with gantry 102, can be configured to traverse different angular positions around entity 106, with detector 112 configured to collect / acquire projection data 114A-n of the attenuated X-ray beams 110A-n. The data 114A-n collected by detector 112 can undergo pre-processing and calibration (e.g., via a computer system 180 and various included sub-components) to condition the received data 114A-n and generate new data 115A-n, wherein the new data 115A-n can represent line integrals of attenuation coefficients associated with subject 106 being scanned. The processed data 115A-n is commonly referred to as one or more projections to form sinograms (per configurations 800A-F, as further described).

[0083] In an example scenario of use, gantry 102 and respective components mounted to gantry 102 can be configured to rotate about center of rotation 125 to acquire the projection data 114A-n, for example, at different energy levels.

[0084] In another example scenario of use, where a projection angle of X-ray radiation beams 110 relative to entity 106 can vary as a function of time, the components mounted to gantry 102 can be configured (e.g., per directions / instructions 197A-n) to move along a general curve / curved trajectory rather than along a segment of a circle. The curved trajectory can indicate a spiral trajectory of data acquisition. That is, although gantry 102 can remain stationaryalong the z-axis while rotating around the z-axis in a circular path (e.g., with reference to a unit circle), the trajectory of gantry 102 along the z-axis can be spiral in nature, due to movement of table 108 along the z-axis, generating a helical operation mode of CT system 100. Accordingly, the trajectory of gantry 102 does not have to be circular as in an axial step and shoot mode, or cine mode, of operation of CT system 100, whereby, gantry 102 can rotate without movement of the table 108.

[0085] In an aspect of operation, the individual detector elements 120A-n of detector 112 can comprise photon-counting detectors configured to register the interactions of individual photons (per X-rays 110A-n) into one or more energy bins of detector 112. However, it is to be appreciated that the various embodiments presented herein can also be implemented with a detector 112 comprising an energy-integrating detector(s).

[0086] In an embodiment, CT system 100 can utilize one or more control mechanisms 128 to control movement of various components of CT system 100, such as the rotation of gantry 102, operation of X-ray source 104, movement of table 108, etc. Control mechanism 128 can utilize an X-ray controller 132 configured to provide / control power and timing signals to X-ray source 104. Control mechanism 128 can further utilize a gantry motor controller 130 configured to control rotational speed and / or position of gantry 102 based on one or more imaging requirements. Control mechanism 128 can function based on one or more instructions 197A-n generated by / received from control component 170 / computer system 180 and / or an associated component / sub-component.

[0087] In an embodiment, control mechanism 128 can be configured to operate in conjunction with a data acquisition system (DAS) 116, wherein DAS 116 can be configured to sample data 114A-n received from detector elements 120 and convert the data 114A-n to processed signals / data 115A-n for subsequent processing / imaging. DAS 116 can be further configured to selectively aggregate data 114A-n from a subset of detector elements 120A-n. DAS 116 can be configured to transmit data 115A-n can be transmitted to computer system 180. In an aspect, data 114A-n can be considered to be measured data generated and provided by DAS 116, while data 115A-n can be considered to be processed data generated from the measured data 114A-n.

[0088] An image component 144 (a.k.a., an image reconstructor component) can be configured to receive measured data 114A-n (.e.g., in the form of a sinogram 800A, per FIG.8) and further process the measures data 114A-n to form the processed data 115A-n and further generate respective sinograms, e.g., sinograms 800B-F, high resolution image(s) 290A-n, and such). The image component 144 can be further utilized to implement respective imagingschema and algorithms (e.g., data blending schema 230, image reconstruction algorithm(s) 195A- n, processes 197A-n, and the like). The image component 144 can further include imaging- related subcomponents (e.g., first resolution recovery component 240, second resolution recovery component 250, image reconstruction component 248, and the like).

[0089] In various embodiments, computer system 180 can store data 114A-n / 115A-n, images 290A-n, sinograms 800A-n, etc., in a storage device or memory 184. Additionally, computer system 180 can include a control component 170 configured to provide commands and parameters to one or more of the DAS 116, an X-ray controller 132, and gantry motor controller 130 for controlling system operations such as data acquisition and / or processing. In an example scenario, control component 170 can control system operations of CT system 100 based on one or more instructions received as a function of operator input. For example, control component 170 can receive operator input, including commands and / or scanning parameters, via an operator console 188 operatively coupled to computer system 180 and display 189. Operator console 188 can be a human-machine interface (HMI) including a keyboard (not illustrated) or a touchscreen to allow the operator to specify the commands and / or scanning parameters for operation of CT system 100. While FIG.1B only illustrates one operator console 188, any number of operator consoles 188A-n can be coupled to CT system 100, for example, for inputting or outputting system parameters, requesting examinations, plotting data, and / or viewing images (e.g., generated from data 114A-n, data 115A-n, and the like). In another example, CT system 100 can be coupled to multiple displays, printers, workstations, and / or similar devices located either locally or remotely, for example, within an institution or hospital, or in an entirely different / remote location via one or more configurable wired and / or wireless networks such as the Internet and / or virtual private networks, wireless telephone networks, wireless local area networks, wired local area networks, wireless wide area networks, wired wide area networks, etc.

[0090] Various communications 197A-n can be utilized across CT system 100, between control component 170 (and subcomponents further described, per FIG.2), table motor controller 142, X-ray controller 132, gantry motor controller 130, DAS 116, PACS 140, image component 144, console 188, display 189, memory 184, detector 112, actuators 270A-n, focal spot position estimator component 211, image reconstruction component 248, and computer system 180. Communications 197A-n can include notifications, instructions, status updates, selections, data (e.g., data 114A-n, 115A-n, images 290A-n, sinograms 800A-n, and the like), information (e.g., positional information regarding actuators 270A-n, gantry 102, X-ray source 104, detector 112 position, table 108 position, and such), and the like.

[0091] As shown, CT system 100 can further include, or is coupled to, a picture archiving and communications system (PACS) 140. In an example scenario of implementation, PACS 140 can be further coupled to a remote system such as a radiology department information system, hospital information system, and / or to an internal or external network (not shown) to allow technicians / operators at different locations to supply commands and parameters and / or gain access to the image data (e.g., data 114A-n / 115A-n, images 290A-n, sinograms 800A-n, and the like). Control component 170 can be configured to use operator-supplied and / or system-defined commands and parameters to operate a table motor controller 142, wherein table motor controller 142 can be configured to control positioning of table 108 (e.g., table 108 is a motorized table) and image component 144. Table motor controller 142 may move the table 108 for appropriately positioning the subject 106 in the gantry 102 for acquiring projection data 114A-n corresponding to the target volume of the subject 106. Gantry 102 can be rotated by 360 degrees (360°), and data 114A-n from detector 112 can be acquired T times to generate P projections, wherein T and P represent positive numeric value.

[0092] FIG.2 presents a schematic of an example CT system 200 configured to control and monitor various mechanisms and process data, e.g., to achieve higher in-plane resolution imaging, in accordance with an embodiment. As further described, CT system 200 can be configured as a control and data flow system to demonstrate resolution recovery framework as further described herein. CT system 200 can be incorporated into CT systems 100A and 100B.

[0093] As shown, system 200 comprises various communicatively coupled components, e.g., coupled to computer system 180, including a control component 170, X-ray detector assembly 112 (e.g., comprising detector elements 120A-n), DAS 116, gantry drive system 270, focal spot position estimator component 211, an image reconstruction component 248, etc.

[0094] As shown, and previously described, FIG.2 includes a control component 170, and associated sub-components configured to control and implement various techniques and technologies are presented herein, e.g., any of the focal spot position estimator 211, the image reconstruction component 248, DAS 116, and the like, can be included at the control component 170, or communicatively coupled thereto, e.g., with data, instructions, etc., being exchanged via communications 197A-n. As previously mentioned, control component 170 can be incorporated in, or communicatively coupled, to computer system 180, wherein computer system includes a processor 182 to implement various operations and commands, e.g., as described regarding operation of control component 170 and associated sub-components.

[0095] As further described, control component 170 can be configured to implement independent sampling control. Description of CT system 200 is presented in a series of steps / operations to enable understanding of data creation, imaging, etc.

[0096] At 2.1, control component 170 can be configured to receive / implement system geometry / geometric parameters 202A-n, wherein the system geometry parameters 202A-n can describe the various dimensions and operations pertaining to CT system 100. System geometry parameters 202A-n can be received from any suitable source, e.g., as an input via HMI 188.

[0097] At 2.2, control component 170 can further include a view sampling control component 205. The view sampling control component 205 can be configured, based on the system geometry parameters 202A-n, operator instructions 197A-n, etc., to generate a view sampling acquisition schema / plan 215A-n, wherein the view sampling acquisition plan 215A-n can be generated prior to a scan operation, to enable acquisition of sufficient view-sampling to, for example, maximize resolution of radial sampling while independently increasing the view- integration angle / integration time. Simultaneous higher view sampling and integration time is achieved using the principle of overlapping view samples, as further described in FIGS.6A-C. In an embodiment, the view sampling schema 215A-n can further include multiple detector ISO- central ray sampling geometries, e.g., regarding ISO-center 125 / ISO-axis 304, etc.

[0098] At 2.3, the view sampling control component 205 can be configured to provide the sampling schema / inputs to a LSF model estimator component 210. The LSF model estimator component 210 can be configured to utilize the acquisition schema 215A-n to synthesize / generate a view LSF model 212A-n, wherein, in an embodiment, the LSF model 212A-n can be configured to capture / represent the amount of blur that is introduced in the view- sampling data 114A-n (e.g., by operation of CT system 100, etc.). LSF model 212A-n can also be configured to provide MTF information (e.g., as a function of Fourier transform).

[0099] At 2.4, the view sampling control component 205 can also be configured to provide the sampling inputs (e.g., in the acquisition schema 215A-n) to the detector data acquisition system (DAS) 116 for data acquisition control.

[0100] At 2.5, the geometric parameters 202A-n can also be provided by the control component 170 to an X-ray detector control component 113, wherein the X-ray detector control component 113 can include the detector 112, X-ray controller 132, etc. The geometric parameters 202A-n can be utilized by the X-ray detector control component 113 to control operation of the detector 112, position actuators 270A-n, etc.

[0101] At 2.6, DAS 116 can also be configured to manage view sampling schema 215A- n, and further forward / implement the view sampling schema 215A-n on the X-ray detectorcontrol component 113 / X-ray detector system 112. The X-ray detector control component 113 can be configured to implement the sampling scheme 215A-n to plan / configure one or more data readouts of measured data 114A-n from the detector system 112, e.g., from charge integrators 208A-n associated with the respective detector elements 120A-n. Measured data 114A-n comprises measured data from a readout component 230, as received from the X-ray detector 112.

[0102] At 2.7A, DAS 116 can be further include a first resolution recovery component 240. The first resolution recovery component 240 can be configured to process the measured data 114A-n received from readout 230 in conjunction with information included in the view sampling schema 215A-n to generate a sharpened view data 115A-n, wherein prior to transmitting the sharpened view data 115A-n (sharpened view data 115A-n can be stored (e.g., temporarily) in a data block 245). The first resolution recovery component 240 can be configured to apply respective techniques / methods to sharpen the measured projection data 114A-n, or can further comprise hardware specialized circuits such as an FPGA array for performance critical requirements. Alternatively at 2.7B., the data 114A-n can be stored at the data block 245, wherein the data 114A-n is re-termed data 115A-n.

[0103] At 2.8, control component 170 can be configured to receive data 115A-n from the DAS 116, wherein data 115A-n (e.g., raw measured data or sharpened data) can be provided to a second resolution recovery component 250, wherein the first resolution recovery component 240 and the second resolution recovery component 250 can be a common, single component, or the first resolution recovery component 240 and the second resolution recovery component 250 can be separate components configured to perform respective data processing. The second resolution component 250 can be configured to receive any of the sampling schema 215A-n, the geometric parameters 202A-n, and / or data 115A-n, and further apply any of the sampling schema 215A-n and / or the geometric parameters 202A-n to further sharpen / refine data 115A-n.

[0104] At 2.9, system 200 can further include a gantry drive component 270, e.g., configured to operate gantry motor controller 130, rotate gantry 102 (including source 104 and detector 112), etc. The gantry drive component 270 can further include a view angle encoder 272, whereby, based on signals generated by / received from the view angle encoder 272, the gantry drive component 270 can be further configured to generate / provide gantry angle data 274A-n. Gantry angle data 274A-n can be utilized to assist with the respective processes depicted in FIGS.5A-B, 6A-C, 7A-D, 8A-F, etc.

[0105] At 2.10, system 200 can further include a focal spot position estimator 211, wherein the focal spot position estimator 211 can be configured to generate / determine focal spotdata 219A-n. Focal spot data 219A-n can be generated based on the received gantry angle data 274A-n in conjunction with data 114A-n (e.g., generated by the readout component 230), e.g., as a function of the angle of the gantry 102, as depicted in FIG.7D (as further described). Focal spot estimator 211 can be configured to utilize a focal spot model 218A-n to generate the focal spot data 219A-n based on the gantry angle data 274A-n, etc.

[0106] At 2.11, system 200 can further include an image reconstruction component 248, wherein the image reconstruction component 248 can be configured to receive, from the second resolution recovery component 250, any of the system geometric parameters 202A-n, the LSF model 212A-n, and / or data 115A-n.

[0107] At 2.12, the image reconstruction component 248 can be further configured to operate in conjunction with focal spot data 219A-n generated by, and forwarded from, the focal spot position estimator component 211.

[0108] At 2.13, the image reconstruction component 248 can be configured to generate a high resolution image(s) 290A-n from data 115A-n in conjunction with any of system geometric parameters 202A-n, the LSF model 212A-n, focal spot data 219A-n, and / or data 114A-n.

[0109] As mentioned, during generation of the high resolution image(s) 290A-n, the image reconstruction component 248 can be configured to incorporate details regarding the system geometric parameters 202A-n. By incorporating the system geometric parameters 202A- n, essential characteristics related to system geometry of the CT system 100 can be captured, wherein, such information can include, in a non-limiting list, ISO-central ray position, source 104 to detector 112 distance, source 104 to ISO-center 125 distance, detector aperture information, focal spot size information, and the like. Based on the various information provided to the image reconstruction component 248, and the various processes performed by the image reconstruction component 248, the image reconstruction component 248 can be configured to generate / output one or more high-resolution images 290A-n.

[0110] In an embodiment, the focal spot estimator component 211 can be configured to provide a focal spot positional estimate (e.g., in focal spot data 219A-n) for every gantry angle, e.g., in angle data 274A-n. In an embodiment, the focal spot positional estimates can be generated based on experimental modeling estimated over multiple data (e.g., angle data 274A-n) acquired of a known object 106 utilized to generate a focal spot position model 218A-n, wherein, for example, model 218A-n is generated as a function of projection angle over multiple gantry rotation times. In another embodiment, focal spot positional estimates can be generated by a computational model 218A-n configured to represent mechanical forces on the X-ray tube 104 to determine the focal spot positional variation as a function of gantry angle 274A-n over multiplegantry rotation times, e.g., per FIGS.6A and 6B (as further described). In a further embodiment, the focal spot positional estimates can be generated based on a system comprising a beam blocker placed close to the source 104, e.g., near collimator aperture in the fan direction without any interference from any object in the beam path 110A-n to derive the focal spot motion as a proportional consequence of the measured signal variation (e.g., as detected by charge integrators 208A-n, per measured data 114A-n at the detector 112 due to projected shadow from the beam blocker.

[0111] The first resolution recovery component 240 and the second resolution recovery component 250 can be configured to implement any suitable technology / technique, such as deconvolution, unsharp masking, a deep learning algorithm configured to determine / learn the LSF model 212A-n based on multiple measurements obtained of known object phantom data, and the like.

[0112] The first resolution recovery component 240 can be configured to be implemented in a FPGA firmware, wherein the FPGA firmware can be configured to process the measured signal (e.g., data 114a-n) and implement a deconvolution in logic-gates of an FPGA array to improve the sampling prior to transmission to the control component 170.

[0001] As further shown in FIG.2, any of CT system 100, CT system 200, control component 170, computer system 180, and the like, can further comprise a process component 178, wherein the process component 178 can be configured to implement various processes 179A-n. Processes 179A-n can comprise artificial intelligence (AI) and machine learning (ML) technologies, as further described. Processes 179A-n can be utilized to determine information, make predictions, etc., regarding improving in-plane resolution, reducing image blur, and suchlike. Processes 179A-n can include position prediction / determination, SNR enhancement, resolution recovery, focal spot determination, system configuration, blur reduction, LSF modeling, conjugate ray configuration, motion prediction / determination, and suchlike, to enable the respective improvement in in-plane image resolution to be achieved, etc., per the various embodiments presented herein.

[0113] In an embodiment, any of the respective components in CT system 100 can operate in conjunction with the process component 178 / processes 179A-n, to enable, in a non- limiting list, (a) implementing a configuration / identify issues relating to edge detector offset alignment scheme(s), e.g., quarter detector offset alignment scheme, and suchlike (e.g., per FIGS. 5A-B), (b) derive a sampling schema which can be used to improve radial view sampling while independently controlling the view integration angle (e.g., per FIGS.6A-C), (c) determine focal spot deviation (per FIGS.7A-D), (d) utilize resolution recovery to reduce blur improve resolutionof a reconstructed image (e.g., per FIGS.8A-F), (e) identify one or more image frequencies / blur in a LSF / MTF model (e.g., LSF model 212A-n) and further adjust operation of CT system 100 to achieve the desired resolvable frequency, etc., in accordance with one or more embodiments presented herein.

[0114] 3. DETECTOR ASSEMBLY

[0115] FIGS.3A-C present respective schematics of a CT detector assembly 300, in accordance with one or more embodiments. As shown in FIG.3A, a detector 112 can include assembly 300A comprising a group of X-ray detecting elements 120A-n (a.k.a. detector cells). An X-ray detecting element (e.g., detector element 120A) is also known as a detector cell or a detector channel. Each detector element 120A-n can comprise dimensions width m × length n in the respective z- × x- axes of the cartesian coordinate system. Each detector element 120A-n can be of a common size / dimension m×n, where dimensions m and n can be in millimeters (mm). The length n of the detector elements 120A-n in the x- direction determine the resolution of detector 112 in the in-plane direction / x-axis / x- direction, while the width m of the detector elements 120A-n in the z-direction determine the resolution of detector 112 in the z- direction / axis. The size of m and n can also determine SNR and geometric efficiency of a detector 112 / CT system 100. Generally, for a given detector material forming an element 120A- n, the smaller the size of m and n, the better is the image resolution, but the poorer is the SNR of the detector element 120A-n / detector 112 for a given dose / exposure.

[0116] As shown in FIG.3A, the elements 120A-n can be arranged in a grid comprising M rows in the z-axis and N columns in the x-axis. Per the conventional configuration, the N columns of elements 120A-n are arranged symmetrically about the central-axis 304, respectively forming a first, left-side detector sub-array 306A (a.k.a., first module) of elements 120A-n and a second, right-side detector sub-array 306B (a.k.a., second module). In the example configuration presented in FIG.3A, sub-arrays 306A and 306B can be arranged symmetrically about the central-axis 304, such that sub-arrays 306A and 306B both contain M rows and N / 2 columns of elements 120A-n. The rows M are also referred to as slices, hence, with two rows of elements 120A-n, example detector configuration 300A is a two-slice detector with sixteen columns.

[0117] Central axis 304 is also known as, comprises, a detector isocenter (ISO, a.k.a., ISO-center line), where central axis 304 represents a central beam. The detector ISO 304 can be defined as a point, or a plurality of points, along the Z-axis about which detector subarrays 306A and 306B can be symmetrically arranged with respect to each other. That is, the detector ISO 304 can fall under the ISO-center of rotation 125 (e.g., per FIG.1B) in the X-Y plane, and the ISO axis 304 can be a virtual location in space about which gantry 102, and included componentsmounted on gantry 102, can spin / rotate about during imaging. Center of rotation 125 can be located in the hollow part of the central bore of gantry 102 within which subject 106 can be positioned during CT imaging.

[0118] While FIG.3A presents configuration 300A comprising one charge integrator 122A-n per detector cell 120A-n, FIGS.3B and 3C present further configurations 300B and 300C, wherein configuration 300B comprises two charge integrators 122A-n and 322A-n per detector cell 120A-n. Further, configuration 300C comprises multiple charge integrates 122A-n, 322A-n, and 323A-n per detector cell 120A-n.

[0119] It is to be appreciated that the various example detector configurations presented herein are presented for understanding, and the detector 112 can comprise any number of detector elements 120A-n in rows M and columns N per the design / requirements of a particular CT system 100.

[0120] 4. SYSTEM GEOMETRY & BEAM CONJUGATION

[0121] The resolutions of the CT system in the z-direction and the x-direction can be dependent on the width m and the length n of detector elements 120A-n, respectively. In accordance with an aspect of the present disclosure, the coverage per axial scan in the z-direction and in the x-direction can be determined by (M×m) and (N×n), respectively. In order to scan the entity for a distance L along the z-axis, S axial scans can be conducted, wherein S =^^^^× ^^^^.

[0122] The size, in terms of m x n, of a detector cell 120A-n can govern the achievable resolution for detector 112 and the detector geometric efficiency (DGE) of detector 112, for a given detector material. DGE can be defined as a ratio of an active area (i.e., detector elements exposed to X-rays) of a detector 112 of a CT system 100 to the total area of the detector 112 exposed to the X-rays. Lower values of m and n can generate better z-axis and in-plane resolutions with a poor DGE, whereas higher values of m and n can generate poor z-axis and in- plane resolutions with a good DGE. The DGE of detector 112 can determine the signal-to-noise ratio (SNR) of the resultant CT images, and there can be trade-off between resolution and SNR for a given detector material.

[0123] In various embodiments, L can be computed with reference to a position on the entity 106, commonly referred as landmarking position, and the position of table 108 can be automatically transformed with reference to the landmarking position on the entity 106, before L can be assigned. The landmarking position can correspond to a zero (0) position along the z-axis. After determining the landmarking position as part of the workflow for the patient 106, the starting and ending position for a scan can be defined by an operator (e.g., hardware, software, AI, machine, neural network and / or user) of the CT system 100, with reference to thelandmarking position. The CT system 100 can internally encode the starting and ending positions on table 108 and feed the information to table motor controller 226 that can control table 108 to reposition the subject by moving the starting position on the patient 106 to the zero (0) position, along the z-axis. After repositioning the patient, the CT scanning can begin wherein X-rays can be emitted from X-ray source 104, and table 108 can move along the z-axis to feed the patient 106 into gantry 102. The movement of table 108 and the X-ray emission can stop once table 108 reaches the ending position.

[0124] The CT system 100 can employ different modes of operations, involving different respective mechanisms with respect to the movement of table 108 and the X-ray control on gantry 102 to acquire different types of data for different clinical purposes. For example, the CT system 100 can operate in an axial step and shoot mode, where the entity and table 108 can remain stationary during the CT imaging, and gantry 102 can turn the X-ray emissions on and off. X-ray source 104 can turn at a particular angle (e.g., between 0° and 360°) and complete one turn of scanning, after which gantry 102 can turn off the X-ray emission to stop the scanning. Gantry 102 can continue to spin at a constant rotational rate and table 108 can be controlled (e.g., by table motor controller 226) to feed the subject through gantry 102 in defined distance increments along the z-axis, based on a distance programmed into the CT system100. Thereafter, gantry 102 can turn the X-ray emission on and X-ray source 104 can complete an additional turn of scanning. The process can be repeated until the scanning is completed. In the axial step and shoot mode, data (e.g., data 114A-n) from the scanning can be acquired in slabs and combined to generate CT images (e.g., data 115A-n, sinograms 800A-D, images 290A-n, and the like) for a defined length of L.

[0125] The CT system can also operate in a helical or spiral mode, wherein the X-ray emission can be turned on at the beginning position of table 108 (e.g., based on the landmarking position), and table 108 can be gradually fed (e.g., by table motor controller 142) through gantry 102 during the scanning. The X-ray emission can remain on throughout the scan until the scanning is completed and table 108 has reached the ending position. The helical or spiral mode can result in the helical trajectory of data acquisition as described elsewhere herein.

[0126] Further, the CT system can operate in the cine mode or fluoroscopy mode described elsewhere herein. In the cine mode, gantry 102 can rotate during scanning and table 108 can remain stationary. Additionally, the X-ray emission can remain on beyond one turn of scanning, such that the same location on the entity can be repeatedly scanned to check for any movement of a contrast through an area of interest of the entity. Thus, the contrast can flow into and wash through a portion of the entity’s anatomy before flowing out, and the evolution of thecontrast over time can be observed via the cine mode. The same portion of the entity’s anatomy can be repeatedly imaged.

[0127] In various embodiments, with reference to the three modes of CT imaging discussed herein, the pre-patient collimation can be set independently of the scan length L. Accordingly, in various embodiments, the X-ray detector can be reconfigured for different lengths (M × m) and different types of collimations to achieve different values for S (i.e., the number of scans) for a defined value of L.

[0128] FIG.4 presents a schematic illustrating a geometry 400 of an example CT system, in accordance with one or more embodiments. As shown, CT system 100 comprises an X-ray source 104 and a detector 112 both rotating about a virtual point, the ISO-center 125. In accordance with one or more aspects of the present disclosure, FIG.4 illustrates a scan plane 400 of a CT imaging system 100, X-ray tube 104, and a bowtie filter 406. Bowtie filter 406 can be a pre-patient filter. As mentioned, in a detector array 112, a single detector cell (e.g., a detector element 120A) can be referred to as a channel.

[0129] In this regard, a channel angle, γ, can be defined as an angle formed between a line (e.g., line 410) extending from a focal spot 404 (or X-ray tube focal spot) of X-ray tube / X- ray source 104 to a detector cell 120A of an array of detector 112 and a line (e.g., line 420) extending from the focal spot 404 of X-ray tube 104 through the ISO-center 125 to an ISO- point / center 304 projected onto the surface of detector 112, of the CT system 100.

[0130] A projection angle or view angle, β, can be defined as an angle formed between a line (e.g., line 420) joining the focal spot 404 of X-ray tube 104 to the ISO line 304 of the CT system 100 and the Y-axis.

[0131] The channel angle γ and projection angle β are illustrated in FIG.4. Every row of detector elements 120A-n of detector 112 can collect a sample (e.g., data 114A-n) for all views (X-ray projections) during a complete 2π radian rotation (i.e., a 360° rotation) of gantry 102, with the sample termed as a sinogram (e.g., sinograms 800E) generated by the CT system 100. The sinogram can be utilized in FBP techniques to reconstruct CT images (e.g., sinograms 800A-n, images 290A-n, etc.) of a scanned object 106 using an axial scan of 2π radians. In an aspect, the maximum γ angle determines the Scanning Field of View (SFOV) 412 of the CT system 100.

[0132] FIGS.5A and 5B present schematics 500A and 500B illustrating a geometry of an example CT system and respective positions of an X-ray source and a detector, in accordance with one or more embodiments.

[0133] FIG.5A illustrates alignment of a CT system 100 where a source 104 is aligned with a detector 112 via an ISO-center 125. As shown on FIG.5A, a scan field of view (SFOV) isdefined by region 412, wherein region 412 identifies movement of the combination of the source 104 and detector 112 in the trajectory 504 swept by motion of the source 104 and detector 112, and further referred to as view angle ^^^^^^^^ ^^^^ ^^^^ ^^^^. As shown, detector 112 is partitioned into two sides comprising modules 306A and 306B comprising respective detector elements 120A-n, partitioning is by the imaginary line 304 joining the focal spot 404 with the detector 112 via the ISO-center 125 into what is referred to as the ± ^^^^^^^^ℎ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^, e.g., where module 306A is the ^^^^^^^^ℎ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^and module 306B is the − ^^^^^^^^ℎ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^. ± ^^^^^^^^ℎ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^capture the respective angle subtended by the source focal spot 404 at every position of detector 112, with γ being measured in both the positive and negative directions with reference to the ISO-central line 304. When the source- detector 112 rotates about the ISO-center 125, two distinct positions are marked in FIG.5A with source 104 at positions 104A at ^^^^^^^^ ^^^^ ^^^^ ^^^^= 0°, ^^^^ ^^^^ ^^^^ 104B ^^^^ ^^^^ ^^^^^^^^ ^^^^ ^^^^ ^^^^= 180°, with corresponding positions of detector 112 at 112A at ^^^^^^^^ ^^^^ ^^^^ ^^^^= 0°, ^^^^ ^^^^ ^^^^ 112B ^^^^ ^^^^ ^^^^^^^^ ^^^^ ^^^^ ^^^^= 180°. As mentioned, detector 112 can comprise a plurality of detector elements 120A-n, whereby, detector elements marked 120R and 120S indicate immediately adjacent detector elements, wherein elements 120R and 120S are positioned either side / about the ISO-central line 304. The configuration of CT system 100 presented in FIG.5A can be referred to as an edge detector offset alignment scheme.

[0134] As shown in FIGS.5A and 5B, respective ends of detector 112 are connected to a pair of actuators 270A and 270B, wherein, as shown, actuators 270A and 270B rotate in conjunction with rotation of source 104 and detector 112. Location of the detector 112, and the respective detector elements 120A-n, can be controlled in the x-direction / in-plane direction using the positional actuators 270A-n attached to detector 112. System geometric parameters 202A-n in control component 170 can be utilized to generate multiple detector ISO-central ray sampling geometries (e.g., regarding ISO-center 125 / ISO-axis 304 in view sampling schema 215A-n), wherein the system geometric parameters 202A-n and / or the multiple detector ISO-central ray sampling geometries can be determined / defined based on one or more requirements of a scan operation, per view sampling schema 215A-n. The system geometric parameters 202A-n and / or the multiple detector ISO-central ray sampling geometries can be determined / programmed either prior to a scan or can be dynamically controlled during a scan acquisition to improve the in-plane sampling of the measured data 114A-n.

[0135] FIG.5B illustrates a system alignment scheme with actuators 270A and 270B positionally shifted by an offset distance d, wherein, in the example, d = a quarter of the width w (e.g., ¼ w) of a detector element, e.g., of element 120S in detector elements 120A-n. Accordingly, source 104 is aligned with the detector 112 via the ISO-center 125 such that the central ray 304 joining the source focal spot 404 falls at a position ¼ w of detector element 120S.The configuration of CT system 100 presented in FIG.5B can be referred to as a quarter detector offset alignment scheme. It is to be appreciated that any offset d of a detector element 120A-n can be utilized, e.g., wherein d can be between zero (0w) and one (1w), wherein, the offset d can give rise to the formation of conjugate rays and conjugate measurements 114A-n, as further described in FIGS.8A-F.

[0136] FIGS.6A-C present various schematics 600A-C depicting sampling schema, in accordance with an embodiment presented herein. FIGS.6A and 6B illustrate different plans or view sampling schema which can be used to improve radial view sampling while independently controlling the view integration angle. FIGS.6A and 6B illustrate motion of source 104 about the ISO-center of rotation 125, with a single detector element 120A (of detector 112) juxtaposed opposite source 104. As the system of source 104 and detector 112 rotate about ISO-center 125, multiple readouts, measurement data 114A-n, are generated at different intervals of view angles ^^^^^^^^ ^^^^ ^^^^ ^^^^. As shown, arrows 610, 612 and 614 indicate / depict views at an arbitrary uniform spacing interval ^^^^^^^^ ^^^^ ^^^^ ^^^^= ^^^^° , while samples 616 and 618 illustrate the sample obtained by integrating the line integral for ^^^^^^^^ ^^^^ ^^^^ ^^^^= 2 ^^^^°.

[0137] Corresponding to the integration angle, the amount of view / image blur is presented in FIG.6C, per lines 622 and 624. Line 622 corresponds to a lower integration angle (e.g., angle swept in 610, 612, 614) and line 624 corresponds to a relatively higher integration angle (e.g., angle swept in 616 and 618). As shown, the view samples obtained may or may not contain overlapping angular regions between the respective samples, e.g., sample 616 overlaps sample 618, however samples 610, 612 and 614 are non-overlapping.

[0138] In an embodiment, the improved projection or view sampling can be retrospectively performed by data aggregation after measurement of finer samples.

[0139] In another embodiment, the improved projection or view sampling can be achieved by having a multitude of charge integrators per detector cell, 122A-n and 124A-n located on the detector 112 to ensure that the charge integrators 122A-n and 124A-n / detector 112 accumulate the charges in parallel configuration and are readout at the same frequency, however with a relative phase difference in the angle of readout. This enables, that while more density of samples are obtained based on the number of charge integrators 122A-n and 124A-n, the integration time is maintained at the same integration time as would be obtained with a single integrator 122A-n. Such an approach enables for greater sampling radially but with overlapping integration angular region between adjacent projection samples.

[0140] FIGS.7A-D present schematics 700A-D illustrating focal spot position behavior during a scanning operation, in accordance with one or more embodiments presented herein.FIGS.7A-D illustrates the behavior of the focal spot position 404 and the variation in the position of the focal spot 404 as a function of gantry 102 rotation g-forces across positions 702, 706, and 708 of the X-ray source 104. Position 702 represents the source position or the focal spot position 404 (e.g., position of source 104) and position 704 depicts a single detector element 120A (e.g., in detector 112). Respective positions 706 and 708 are two respective positions of the focal spot 404 (e.g., of x-ray tube 104) and detector 112 positions during the rotation of gantry 102 for acquiring tomographic projections. FIGS.7B and 7C present zoomed representations illustrating the angle of the view in 706 at 2 ^^^^ − ^^^^^^^^ ^^^^ ^^^^ ^^^^and 708 at ^^^^^^^^ ^^^^ ^^^^ ^^^^, whereby the deviation of the focal-spot position from its nominal position as expected by the view-angle is a function of the gantry angle and is further depicted in FIG.7D as ± ^^^^^^^^. FIG.7D, chart 700D, illustrates the respective focal spot deviations (in length units) at positions 702, 704, 706, and 708, and the ^^^^^^^^ ^^^^ ^^^^ ^^^^(in angle units). As mentioned, positional deviation of the focal spot can occur during rotation of gantry 102, and the g-forces generated during the gantry rotation, such that when the focal spot positional deviation is unaccounted for in an image reconstruction process, the positional deviation of the focal spot between expected and actual can lead to inaccurate back-projection of the measured line integrals leading to degradation of in-plane resolution in a reconstructed image, e.g., image 290A-n.

[0141] FIGS.8A-F, schematics 800A-F, depict using resolution recovery to reduce blur in measured data to improve resolution of a reconstructed image, in accordance with one or more embodiments presented herein. The respective stages presented in FIGS.8A-F depict a conjugate interlaced up-sampling technique.

[0142] FIG.8A illustrates the measured data 802 and 804 (e.g., of data 114A-n) obtained by the detector 112 at two separate angles within a single turn of 2 ^^^^ at ^^^^^^^^^^^^ ^^^^ ^^^^ ^^^^^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^. FIG. 8A depicts the gamma of detector 112 to be respectively ^^^^^^^^^^^^ ^^^^ ^^^^− ^^^^^^^^. Further, the relationship between the conjugate view angle is given by ^^^^^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^ ^^^^= ^^^^^^^^+ ^^^^ + 2 ^^^^ . The two measurements 802 and 804 represent the object 108 that is measured twice in the 2 ^^^^ turns of gantry 102, wherein the measurements 802 and 804 can be referred to as conjugates as they are similar in the region of the object 108 through which the measurement data 114A-n is obtained but for interchanging positions of the source 104 and the detector 112. With reference to FIGS. 5A and 5B, regarding the fan geometry of measurement and the system alignment (e.g., of source 104 and detector 112), the two conjugate samples 802 and 804 are located near each other as shown in FIG.8B. As further shown in FIG.8C, the two conjugate samples 802 and 804 can be placed in an interlaced fashion, per 808. The respective placement of the conjugate samples 802and 804, per FIG.8C, can introduce image blur due to the overlapping LSF of the conjugate samples 802 and 804, whereby plot 806 illustrates the image blur effect.

[0143] The samples / data 802 and 804 interlaced in FIG.8C can undergo resolution recovery. Resolution recovery components 240 and 250 can be configured to perform resolution recovery on the samples 802 and 804. Resolution recovery components 240 and 250 can implement any suitable resolution recovery techniques / technology, e.g., any of deconvolution, unsharp masking, frequency boost filtering, or deep-learning techniques using the view LSF models, and the like, as previously mentioned.

[0144] During resolution recovery, the higher resolution up-sampled values 802 and 804 depicted in FIG.8C / 808 can be reduced, per the sample / image 812 presented in FIG.8D. The reduction between values 806 and 810 and image 812 presented in FIG.8D is represented by the improved LSF depicted in plot 810 versus prior plot 806. The change between plots 810 versus 806 illustrates / indicates the process of up-sampling to estimate the higher resolution sinogram 816 depicted in FIG.8F from the lower resolution sinogram 814 measured from the system in FIG.8E.

[0145] The progress / sequence of the measurements / data 114A-n, 115A-n, and respective sinograms, as originally obtained in detector cells 802 and 804 (e.g., 120R and 120S), and as depicted through FIGS.8A-F indicate the cells 802 and 804 to be each other’s conjugate measurements, with respective processing (e.g., per FIGS.8A-F) enables generation of finer up- sampled measurements with lower channel size, per final high-resolution sinogram 816.

[0146] FIG.9, chart 900, illustrates a plot of modulation transfer function (MTF) in percentage (normalized) versus frequency as a factor of Nyquist units across multiple images, in accordance with one or more embodiments presented herein. The Nyquist unit can be computed as the system resolution limit obtained by computing the smallest resolvable structure that is based on the projection of the detector aperture at ISO-center 125 based on the system geometrical parameters such as source-to-ISO-center (e.g., source (104) -to-ISO-center (125)) and source-to-detector distances (e.g., source (104)-to-detector (112)). The Nyquist is obtained as half of the projection width at the ISO-center 125 based on the well-known understanding of the fractional sampling achieved by conjugates using quarter-detector offset alignment (e.g., per FIG.5B) conventionally used in system designs to improve sampling and reduce aliasing.

[0147] Chart 900 is partitioned into three spaces / regions respectively marked 902, 904, and 908. The portioning is a consequence of curve 906, which is referred to as the ideal / best theoretical MTF that can be obtained / desired out of a system limited by detector Nyquist sampling. Region 902 can be referred to as the Nyquist region, region 904 as the supra-nyquistor Super-Resolution region, and region 908 is the forbidden undesirable region that is considered to have the phenomena of “ringing” where the MTF rises above unity.

[0148] The x-axis of chart 900 is in frequency units, generally marked in line pairs per centimeter (lp / cm). Per chart 900, the x-axis is marked as a relative factor with reference to the system Nyquist to make the plot dimensionless, and thus, the results naturally hold for a CT system 100 having any size of detector 112.

[0149] The various embodiments presented herein relate to / enable improving the in-plane image resolution to greater than the limitations of the Nyquist into the Super-resolution or Supra- nyquist mode. The Nyquist of the system resolution can be limited by the detector aperture, and further, improved view or radial sampling can further improve the resolution. Advanced resolution enhancement techniques like focal-spot wobble during acquisition and high frequency sampling in view direction (e.g., increased view samples per turn) can be employed to improve sampling over and above the quarter detector offset system alignment geometry. However, such techniques have never led to any strong supra-nyquist resolution in the reconstructed images.

[0150] The various embodiments presented herein exploit the view direction blur, the modified system alignment geometry such as edge-detector offset (e.g., per FIGS.5A-B), along with the conjugate up-sampling technique using conjugate interlacing (e.g., per FIGS.8A-F), ensuring that improvements in samplings are appropriately supported with corresponding improvements in signal conditioning and data processing schema to ensure a maximization of the benefits of the resolution as theoretically anticipated. Respective system limitations furthermore include slight deviations in the focal spot position with gantry 102 rotational g-forces that cause degradation in the reconstructed image resolution as the reconstruction assumes constant position of the focal spot.

[0151] Furthermore, simulations have been performed to demonstrate the full capabilities of the higher view samplings and the partial recovery of resolution possible with overlapped view samplings such that appropriate trade-offs between resolution and noise can be obtained based on system needs.

[0152] The multitude of curves depicted in FIG.9 can be better appreciated by first describing the process used to obtain the final image from the measurements. All of the plots are representational of the MTF curves and are obtained from visual CATPHAN 600 MTF grating section measurements for curves 910, 912, 914, and 916. CATPHAN 600 visual MTF grating section supports measurements up to 21 lp / cm. For curves 918 and 920, numerical simulations were used to synthesize sinogram from digital CATPHAN MTF grating section mimickingphantom capable of up to twice as many line pairs available in the CATPHAN 600 MTF grating section (21 lp / cm).

[0153] Curve 910 (QDO_ZIUS) is obtained from the measured data from a CT system 100 with quarter detector offset alignment as illustrated in FIGS.5B. This represents the baseline image obtained from a quarter-detector offset system alignment measurement using a standard FBP algorithm using 2X up-sampling using zero value interlacing during the convolution filtering stage.

[0154] Curve 916 (QDO_VS_ZIUS) is obtained from the same measured data with a view-sharpening stage (e.g., by first resolution recovery component 240 and / or second resolution recovery component 250) as depicted in FIGS.6A-C using the view LSF model 212A-n in FIG. 2. The data used in obtaining curve 916 is the same measured data used to obtain 910.

[0155] Curve 914 (QDO_VS_CJUS) is obtained by processing the measured data with a view-sharpening stage (e.g., by first resolution recovery component 240 and / or second resolution recovery component 250) as depicted in FIGS.6A-C and conjugate up-sampling technique illustrated in FIGS.8A-F to obtain the high-resolution sinogram 816 to reconstruct the image. The data used in obtaining curve 914 is the same measured data used to obtain 910.

[0156] Curve 912 (EDO_VS_CJUS) is obtained from the measured data from a CT system 100 with edge detector offset alignment as illustrated in FIGS.5A. This data is processed with a view-sharpening stage (e.g., by first resolution recovery component 240 and / or second resolution recovery component 250) as depicted in FIGS.6A-C using the view LSF model 212A- n in FIG.2. Additionally, curve 912 utilizes the conjugate up-sampling technique illustrated in FIGS.8A-F to obtain the high-resolution sinogram 816 to reconstruct the image.

[0157] Curve 920 (2N(views)_Sim) is obtained from a numerically simulated CATPHAN phantom to generate projection data with twice as many view samples as those obtained with the real phantom in the system measurements. These view samples are then processed using the view-sharpening stage (e.g., by first resolution recovery component 240 and / or second resolution recovery component 250) as depicted in FIGS.6A-C using the LSF model 212A-n in FIG.2 along with the conjugate up-sampling technique illustrated in FIGS.8A-F to obtain the high- resolution sinogram to reconstruct the image.

[0158] Curve 918 (2N(overlap_views)_Sim) is obtained from a numerically simulated CATPHAN phantom to generate projection data with twice as many view samples as those obtained with the real phantom in the system measurements. The key difference between the data used in 920 and in 918 is the increased view integration angle or overlapped views that additionally lead to view blur. These view samples are then processed using the view-sharpeningstage (e.g., by first resolution recovery component 240 and / or second resolution recovery component 250) as depicted in FIGS.6A-C using the LSF model 212A-n in FIG.2 along with the conjugate up-sampling technique illustrated in FIGS.8A-F to obtain the high-resolution sinogram 816 to reconstruct the image.

[0159] The primary results obtained and depicted in FIG.9 are not meant to be indicative of the final performance capabilities of the respective embodiments presented herein, rather the results obtained and presented in FIG.9 indicate the relative performance benchmarking while depicting one or more potentials of the various embodiments presented herein.

[0160] The resultant curves 918, 920, etc., depicted in FIG.9 indicate that view- directional sharpening performed using a multitude of techniques (e.g., deconvolution, unsharp masking, frequency boost filtering, or deep-learning techniques using the view LSF models, and the like) restore a large region of resolution under the MTF curve that is not achievable in the baseline processed image 910.

[0161] As shown, when there is increase in view samples there is further increase in resolution in the supra-nyquist or super-resolution region. Overlapped view sampling while having lower resolution than non-overlapped sampling, still provides a good benefit to improve resolution while not sacrificing integration time.

[0162] Integration times directly govern resolution and noise properties in an image for a given gantry rotation time. Integration times can also impact the signal to noise ratio (SNR) and can be governed by the detector aperture and the scintillator material in conjunction with the detector signal chain.

[0163] The conjugate interlaced up-sampling technique as described in FIGA.8A-F, when used to synthesize a higher resolution sinogram 816 from lower native resolution sinogram 814 measured on a CT system 100, provides for improved resolution in the reconstructed image with significantly lower “ringing” properties of plots 912 and 914 as compared to similar data processed using conventional frequency up-sampled FBP reconstruction methods in curve 916.

[0164] A modified system alignment geometry like the edge-detector offset as described in FIGS.5A-B allows for improved resolution curves, e.g., plot 912, in conjunction with the improved view-sharpened processing as compared to plots 914 and 916 that have relatively larger “ringing”.

[0165] FIG.10, plot 1000, illustrates a plot of normalized noise to the baseline image versus the normalized MTF 5% as a factor of Nyquist limit of the system. Plot 1000 is partitioned into three regions by the normalized maximum noise of unity and the vector 1010 indicating the natural trajectory of increase in MTF and corresponding amplification and increasein noise that is a well-known behavior in FBP reconstruction with filtering kernels used for multiple clinical use cases. Region 1002 is within the Nyquist resolution limits of a system. Region 1004 is in the supra-nyquist or super-resolution region. Region 1008 is above the presently acceptable noise region of clinical acceptability.

[0166] The various embodiments presented herein, along with various methods to generate images, as previously described per FIGS.8A-F, are further plotted as points in the joint plot of FIG.10, to depict the relative behavior of resolution and image noise that jointly govern the usability of the image for clinical acceptability.

[0167] The conventional FBP reconstruction techniques lead to the outcomes that come at a price of high noise increase with corresponding increase in resolution, as depicted in points marked 1012 and 1014.

[0168] The overall noise as perceived in the reconstructed image plays a critical role in acceptability of image quality for clinical use purposes. Conventional techniques of frequency domain kernel filters used during FBP reconstructions naturally trade-off resolution with noise. This indicates that higher resolution always pushes noise to very high levels, as indicated in methods 1012 and 1014.

[0169] The benefit of conjugate interlaced up-sampling scheme as presented in FIGS. 8A-F is primarily in lowering the noise in the final reconstructed image as depicted in methods marked as 1018, 1016, 1020 and 1022, since the respective method produces a more conditioned up-sampled higher resolution sinogram 816 as compared to conventional frequency domain up- sampling during convolution filtering in a standard FBP reconstruction technique.

[0170] FIG.11, per example flowchart 1100, presents a computer-implemented method for increasing in-plane image resolution, according to one or more embodiments.

[0171] At 1110, system geometry parameters (e.g., system geometry / geometric parameters 202A-n) can be determined (e.g., by control component 170), wherein the system geometry parameters pertain the physical and operational characteristics of a CT system (e.g., CT system 100). The system geometry parameters can be defined and received from any suitable source, e.g., input by an external entity (e.g., via HMI 188), configured in a memory (e.g., memory 184) at the CT system, and the like.

[0172] At 1115, a sampling schema (e.g., view sampling acquisition schema / plan 215A- n) can be generated from the system geometry parameters (e.g., by view sampling control component 205). The sampling schema can be generated prior to a scan operation, to enable acquisition of sufficient view-sampling to, for example, maximize resolution of radial sampling while independently increasing the view-integration angle / integration time. Simultaneous higherview sampling and integration time is achieved using the principle of overlapping view samples (e.g., per FIGS.6A-C).

[0173] At 1120, the view sampling control component can be configured to provide the sampling schema (e.g., schema 215A-n) to a LSF model estimator component (e.g., LSF model estimator component 210). The LSF model estimator component can be configured to utilize the sampling schema to synthesize / generate a view LSF model (e.g., LSF model 212A-n). The LSF model can be configured to capture / represent the amount of blur that is introduced in view- sampling data (e.g., data 114A-n / data 115A-n by operation of CT system 100, etc.). Step 1120 can advance to step 1150, as further described.

[0174] At 1130, the view sampling control component can also be configured to provide the sampling inputs (e.g., in the acquisition schema 215A-n) to a DAS (e.g., DAS 116), wherein DAS can be configured to control operation of the CT system, e.g., for data acquisition control (e.g., of data 114A-n). The DAS can be configured to manage the sampling schema (e.g., schema 215A-n), and further forward / implement the sampling schema on an X-ray detector control component (e.g., X-ray detector control component 113).

[0175] At 1135, the geometric parameters (e.g., parameters 202A-n) can also be provided by the control component to the X-ray detector control component, wherein the X-ray detector control component can be communicatively coupled to a detector (e.g., detector 112), position actuators (e.g., position actuators 270A-n), a readout component (e.g., readout 230), and suchlike. The X-ray detector control component can be configured to apply / implement the geometric parameters to control operation of the detector, position actuators, readout component, etc.

[0176] At 1140, the X-ray detector control component can be configured to implement the sampling schema to control operation of the X-ray detector system.

[0177] At 1145, the X-ray detector can be configured to implement the sampling schema to configure one or more data readouts of measured data (e.g., measured data 114A-n) generated by the detector system, e.g., from the charge integrators (e.g., charge integrators 208A-n) associated with a respective detector element (e.g., any of detector elements 120A-n). The measured data comprises measured data from the readout component, as received from the X-ray detector.

[0178] At 1150, the DAS can further include a first resolution recovery component (e.g., first resolution recovery component 240). The first resolution recovery component can be configured to process the measured data received from the readout component. The measured data can be processed in conjunction with information included in the view sampling schema, togenerate a sharpened view data (e.g., sharpened view data 115A-n), wherein prior to transmitting the sharpened view data, the sharpened view data can be stored in a data block (e.g., in a data block 245). Per FIG.2, the measured data can be received from the readout component and stored directly in the data block, or the measured data can be processed by the first resolution recovery component and then stored at the data block. The first resolution recovery component can be configured to sharpen the measured projection data, or can further comprise hardware specialized circuits such as an FPGA array for performance critical requirements.

[0179] At 1155, the control component (e.g., control component 170) can be further configured to receive the sharpened data (e.g., data 115A-n in raw measured or sharpened form) from the DAS. The sharpened data can be applied to a second resolution recovery component 250 (e.g., a second resolution recovery component 250). The second resolution component 250 can be configured to receive any of the sampling schema (e.g., sampling schema 215A-n), the geometric parameters (e.g., the geometric parameters 202A-n), and / or the data, (e.g., data 115A- n), and further apply any of the sampling schema and / or the geometric parameters to further sharpen / refine the data (e.g., data 115A-n).

[0180] At 1160, the CT system can further include a gantry drive component (e.g., gantry drive component 270), e.g., configured to rotate a gantry (e.g., gantry 102, including source 104 and detector 112). The gantry drive component can further include a view angle encoder (e.g., a view angle encoder 272), whereby, based on position signals generated by / received from the view angle encoder, the gantry drive component can be further configured to generate / provide gantry angle data (e.g., gantry angle data 274A-n).

[0181] At 1170, the CT system can further include a focal spot position estimator (e.g., a focal spot position estimator), wherein the focal spot position estimator can be configured to generate / determine focal spot data (e.g., focal spot data 114A-n). The focal spot data can be generated in conjunction with the LSF model (per step 1120 advancing to step 1170), as well as being based on the system geometry parameters (e.g., system geometry / geometric parameters 202A-n).

[0182] At 1175, the respective gantry angle data can be applied to the focal spot data (per step 1160 advancing to step 1175).

[0183] At 1180, the focal spot estimate can be generated based on the received gantry angle data.

[0184] At 1185, the focal spot estimate can be applied to the sharpened data (per step 1155 advancing to step 1185), e.g., as a function of the angle of the gantry (e.g., as depicted in FIG.7D). The CT system can further include an image reconstruction component (e.g., imagereconstruction component 248), wherein the image reconstruction component can be configured to receive, from the second resolution recovery component, any of the system geometric parameters (e.g., system geometric parameters 202A-n), the LSF model (e.g., LSF model 212A- n), and / or the sharpened data (e.g., data 115A-n). The image reconstruction component can be further configured to operate in conjunction with focal spot data generated by, and forwarded from, the focal spot position estimator component (e.g., focal spot position estimator component 211).

[0185] At 1190, the image reconstruction component can be configured to generate a high resolution image(s) (e.g., high resolution image(s) 290A-n) from the sharpened data (e.g., 115A-n) in conjunction with any of system geometric parameters (e.g., system geometric parameters 202A-n), the LSF model (e.g., LSF model 212A-n), the focal spot data (e.g., focal spot data 219A-n), and / or information / metadata included in the sharpened data (e.g., in data 114A-n or 115A-n).

[0186] FIG.12 presents an example computer-implemented method 1200 for increasing in-plane image resolution in CT system, in accordance with an embodiment. At 1210, process 1200 can comprise a CT system (e.g., CT system 100 / 100A / 100B), comprising: at least one processor (e.g., processor 182A-n); and at least one memory (e.g., memory 184A-n) coupled to the at least one processor and having instructions stored thereon, wherein, in response to the at least one processor executing the instructions, the instructions facilitate performance of operations, comprising acquiring a set of x-ray projections (e.g., beams 110A-n / measured data 114A-n) at a detector (e.g., detector 112), wherein the set of x-ray projections are acquired during a rotation of a gantry (e.g., gantry 102) in the CT system imaging an object (e.g., object 106), and the set of x-ray projections are acquired based on at least one of an integration angle (e.g., ^^^^^^^^ ^^^^ ^^^^ ^^^^, angles 616, 618, or angles 610, 612 and 614) or effective integration time per projection (e.g., time to sweep angles 616, 618). At 1220, method 1200 further comprises generating a line spread function (LSF) model (e.g., LSF model 212A-n) depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter (e.g., parameters 202A-n) of the CT system and a defined sampling schema (e.g., schema 215A-n). At 1230, method 1200 further comprises recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space (e.g., sinograms 800B-n). At 1240, method 1200 further comprises incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in- plane resolution of a reconstructed image (e.g., images 290A-n) while simultaneously improving image noise characteristics of the reconstructed image.

[0187] FIG.13, via flowchart 1300, presents an example computer-implemented method for increasing in-plane image resolution in a CT imaging system, in accordance with an embodiment. At 1310, process 1300 can comprise modifying, by a device comprising at least processor (e.g., processor 182A-n), the ISO-center alignment geometry of a detector (e.g., detector 112) relative to an X-ray source focal spot (e.g., focal spot 4040 in a CT system (e.g., CT system 100 / 100A / 100B), wherein the modification causes improved conjugate sampling over one turn of gantry rotation (e.g., gantry 102). At 1320, process 1300 can comprise recovering, by the device, a frequency of measured data obtained by a sampling schema (e.g., schema 215A-n) to increase data resolution in a sinogram space (e.g., sinograms 800B-n). At 1330, process 1300 can comprise incorporating, by the device, one or more system parameters (e.g., parameters 202A-n) into an image reconstruction process to propagate the restored data resolution into an improvement in the in-plane resolution of a reconstructed image (e.g., images 290A-n) while ensuring minimal impact to image noise in the reconstructed image.

[0188] FIG.14, via flowchart 1400, presents an example computer-implemented method for increasing in-plane image resolution in a CT imaging system, in accordance with an embodiment. At 1410, the process 1400 can be performed by a computer program product stored on a non-transitory computer-readable medium (e.g., memory 184A-n) and comprising machine- executable instructions, wherein, in response to being executed (e.g., by a processor 182A-n), the machine-executable instructions cause a system (e.g., CT system 100 / 100A / 100B) to perform operations, comprising: acquiring a set of x-ray projections (e.g., beams 110A-n / measured data 114A-n) at a detector(e.g., detector 112), wherein the set of x-ray projections are acquired during a rotation of a gantry (e.g., gantry 102) in the CT system imaging an object (e.g., object 106), and the set of x-ray projections are acquired based on at least one of an integration angle (e.g., ^^^^^^^^ ^^^^ ^^^^ ^^^^, angles 616, 618, or angles 610, 612 and 614) or effective integration time per projection (e.g., time to sweep angles 616, 618). At 1420, process 1400 can further comprise generating a line spread function (LSF) model (e.g., LSF model 212A-n) depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter (e.g., parameters 202A-n) of the CT system and a defined sampling schema (e.g., schema 215A-n). At 1430, process 1400 can further comprise recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space (e.g., sinograms 800B-n). At 1440, process 1400 can further comprise incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in- plane resolution of a reconstructed image (e.g., images 290A-n) while simultaneously improving image noise characteristics.

[0189] The system geometric parameters 202A-n can include characteristics related to system geometry of the CT system 100, wherein, such information can include, in a non-limiting list, ISO-central ray position 125, source 104 to detector 112 distance, distance from source 104 to ISO-center 125, detector aperture information, focal spot size information, and the like.

[0190] Per the various embodiments presented herein, a CT system is presented enabling (a) independent control over the number of samples of x-ray projections collected over one turn of gantry 102 rotation and integration angle and / or effective integration time per projection, (b) incorporating the system geometry parameters (e.g., parameters 202A-n) and sampling schema (e.g., schema 215A-n) employed above to generate the LSF model (e.g., model 212A-n) of the energy distribution of the line integral in the view sampling direction, (c) a recovery component (e.g., second recovery component 250) configured to perform a recovery of a frequency of the measured data in conjunction with the LSF model (e.g., model 212A-n) to increase the data resolution in sinogram space (e.g., of sinograms 800A-n, data 115A-n), and further, (d) incorporating the system parameters in the image reconstruction process to propagate this restored data resolution into an improvement in the in-plane resolution of a reconstructed image (e.g., high resolution image 290A-n) while simultaneously improving image noise characteristics.

[0191] In an embodiment, one or more CT systems and computer implemented methods are presented regarding: (a) modification in ISO-center alignment geometry (e.g., ISO-center 125) of a detector (e.g., detector 112) to focal spot (e.g., focal spot 404) in the CT system to ensure improved conjugate sampling over one turn of gantry rotation (e.g., per FIGS.6A-C), (b) a recovery component (e.g., recovery component 240 or 250) configured to perform a recovery of a frequency of the measured data (e.g., data 114A-n) obtained by the previously presented improved sampling schema to increase the data resolution in sinogram space (e.g., sinograms 800A-n), (c) incorporating the system parameters (e.g., parameters 202A-n) in the image reconstruction process to propagate the restored data resolution (e.g., of sharpened data 115A-n) into an improvement in the in-plane resolution of the reconstructed image (e.g., high resolution image 290A-n) while ensuring minimal impact to image noise that is otherwise difficult to achieve in conventional FBP approaches where filter kernels are employed.

[0192] In an embodiment, the CT system enables improvement in view sampling to be achieved with a modification to the charge integrators (e.g., charge integrators 208A-n) on the detector data acquisition system to incorporate multiple integrators that can be configured in parallel to the detector photodiodes (e.g., in detector elements 120A-n) and can be read out at the same frequency but with a uniformly distributed phase shift over the number of integrators toincrease number of view samples per one turn of gantry rotation while maintaining the integration time per view sample.

[0193] In another embodiment, the CT system enables improved integration angle to be achieved by retrospectively summing consecutive views in an overlapping moving window fashion while maintaining the total number of view samples per one turn of the gantry (e.g., per angle data 274A-n).

[0194] In another embodiment, the CT system enables a sampling schema (e.g., schema 215A-n) to be utilized in conjunction with system parameters (e.g., parameters 202A-n) such as detector aperture and integration arc length to compute the energy spread of the line integral also known as LSF in the projection direction.

[0195] In a further embodiment of the CT system, a recovery component (e.g., first resolution recovery component 240) can be configured to perform a recovery of a frequency of the measured data (e.g., data 114A-n) in conjunction with the LSF model (e.g., model 212A-n) to increase the data resolution in sinogram space (e.g., of sinograms 800A-n). In an embodiment, the recovery component can be implemented in hardware as part of a data acquisition system configured to process a stream of consecutive views in FPGA prior to transfer to a computer (e.g., control component 170).

[0196] In another embodiment of the CT system, a recovery component (e.g., a second recovery component) can be configured to perform a recovery of a frequency of the measured data (e.g., data 114A-n or 115A-n) in conjunction with the LSF model (e.g., model 212A-n) to increase the data resolution in sinogram space (e.g., of sinograms 800A-n). The recovery component can be implemented in software as part of the data processing system (e.g., control component 170) on a computer configured to process data for image generation process. In an embodiment, the recovery component can be further configured to utilize a pre-trained deep learning process / algorithm (e.g., in processes 179A-n) running on the computer (e.g., control component 170, computer system 180), wherein the pre-trained deep learning algorithm can be trained on the LSF model (e.g., model 212A-n) to recover the frequency of the measured data to increase the data resolution in sinogram space (e.g., of sinograms 800A-n). In another embodiment, the recovery component can be configured to implement a resolution recovery algorithm / process (e.g., in processes 179A-n) as a filter in the frequency domain that is implemented to be a matched with the LSF model (e.g., model 212A-n). In another embodiment, the recovery component can be configured to implement a resolution recovery algorithm / process (e.g., in processes 179A-n) as a deconvolution algorithm in the spatial domain. In a further embodiment, the recovery component can be further configured to implement a resolutionrecovery algorithm (e.g., in processes 179A-n) as a unsharp masking algorithm using the deconvolution kernel derived from the LSF model in the spatial domain.

[0197] In an embodiment, generation of the system parameters utilized as part of the reconstruction process can involve incorporating knowledge of the sampling positions of the conjugate line integrals (e.g., of angle data 274A-n, of the conjugate process described in FIGS. 8A-F) and generating an up-sampled sinogram data that can be further processed with a resolution recovery component (e.g., the resolution recovery component 240 or 250). In another embodiment, an up-sampling method can be applied involving interlacing of conjugate line integral samples (e.g., per FIGS.8A-F). In a further embodiment, an up-sampling method can be utilized further comprising a constrained least squares solver configured to iteratively estimate the higher resolution sample (e.g., image 290A-n, data 115A-n) from low resolution measurements (e.g., data 114A-n) using the conjugate line integral samples in conjunction with the LSF model (e.g., model 212A-n) such that the estimation error from measurement is minimized.

[0198] In another embodiment, the system parameters utilized as part of the reconstruction process can involve incorporating knowledge of the focal spot position per projection sample (e.g., per focal spot model 218A-n, focal spot data 219A-n) in the reconstruction process to improve the fidelity of the CT reconstruction and restore any blur and resolution loss induced by assuming fixed focal spot position.

[0199] In another embodiment, the modification in ISO-center alignment geometry of the detector to focal spot in the scanner can be achieved using a pair of actuators (e.g., actuators 270A-n) than can be programmed prior to, or dynamically during a scan acquisition, to generate a motion on the detector position to ensure improved conjugate sampling over one turn of gantry rotation.

[0200] In another embodiment, information can be incorporated regarding the focal spot position (e.g., per focal spot data 219A-n) that could be achieved by: (a) experimental modeling (e.g., per process 179A-n) estimated over multiple data acquired of a known object to deduce the focal spot position model (e.g., focal spot model 218A-n) as a function of projection angle (e.g., in angle data 274A-n) over multiple gantry rotation times, (b) a computational model (e.g., in processes 179A-n) deduced by mechanical forces on the tube to derive the focal spot positional variation as a function of gantry angle over multiple gantry rotation times, or (c) a system comprising of beam blocker placed close to the source near collimator aperture in the fan direction without any interference from any object in the beam path to derive the focal spotmotion as a proportional consequence of the measured signal variation on the detector due to projected shadow from the beam blocker.

[0201] APPLICATION / IMPLEMENTATION OF AI & ML

[0202] As mentioned, processes 194A-n can include AI, ML, and reasoning techniques / technologies that employ probabilistic and / or statistical-based analysis to prognose or infer an action that an entity desires to be automatically performed for carrying out various aspects thereof, e.g., automatically identifying and adjusting operating conditions to improve in- plane image resolution, reduce blur, and suchlike, which can be facilitated via an automatic classifier system and process. A classifier can be a function that maps an input attribute vector, x = (x1, x2, x3, x4, xn), to a class label class(x). The classifier can also output a confidence that the input belongs to a class, that is, f(x) = confidence(class(x)). Such classification can employ a probabilistic and / or statistical-based analysis to prognose or infer an action that a user desires to be automatically performed.

[0203] As used herein, the terms “predict”, “infer”, “inference”, “determine”, and suchlike, refer generally to the process of reasoning about or inferring states of the system, environment, and / or user from a set of observations as captured via events and / or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic–that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and / or data. Such inference results in the construction of new events or actions from a set of observed events and / or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources.

[0204] A support vector machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs that splits the triggering input events from the non-triggering events in an optimal way. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches include, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein is inclusive of statistical regression that is utilized to develop models of priority.

[0205] As will be readily appreciated from the subject specification, the various embodiments can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (as further described below). For example, SVM’s are configured via a learning or training phase within a classifier constructor and feature selection module, e.g., included in process component 178. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to, determining according to predetermined criteria, e.g., identifying one or more operational settings / parameters giving rise to image blur and further mitigation of the image blur, and suchlike.

[0206] In an example embodiment, processes 179A-n can be trained / fine-tuned with previously obtained / generated data (e.g., in historical data 195A-n, previously implemented LSF models 212A-n, measured data 114A-n, data 115A-n, focal spot data 219A-n, sampling schema 215A-n, sinograms 800A-n, high resolution images 290A-n, and such). Fine-tuning of a process 179A-n can comprise application, to processes 179A-n, of previously implemented LSF models 212A-n, measured data 114A-n, data 115A-n, focal spot data 219A-n, sampling schema 215A-n, sinograms 800A-n, high resolution images 290A-n, and such. Processes 179A-n can be correspondingly adjusted by the ability of the processes 179A-n (e.g., by process component 178, and any associated component across CT system 100 utilizing processes 179A-n) to successfully improve the in-plane resolution. For example, weightings in the processes 179A-n are adjusted based on the ability of the respective process 179A-n to accurately determine one or more operational factors / conditions of CT system 100 giving rise to blur, etc., and improvement in the in-plane image resolution. During training, prior decisions, prior observations, determinations, etc., can be applied to the processes 179A-n, enabling the processes 179A-n to be trained regarding enhancing in-plane image resolution. Accordingly, when new information is provided (e.g., a new system parameter 202A-n, a new object 106, a new sampling schema 215A-n, and suchlike), processes 179A-n can be retrained accordingly.

[0207] It is to be appreciated that the various processes 179A-n and operations presented herein are simply examples of respective AI and ML operations and techniques, and any suitable technology can be utilized in accordance with the various embodiments presented herein. In an example embodiment, process component 178 / processes 179A-n can be applied to previously utilized LSF models 212A-n and resulting high resolution images 290A-n, in historical data 195A-n, frequencies in the LSF model 212A-n, and such, in conjunction with new system parameters 202A-n, focal spot data 219A-n, and the like. Wherein, process component 193 / processes 194A-n can include a vector component to apply any suitable vectoring technology, such as, in a non-limiting list, bag of words (BOW) text vectors, Euclidean distance,cosine similarity, vector representation via term frequency-inverse document frequency (tf–idf) capturing term / token frequency (e.g., common terms across prior / current / future knowledge), neural network embedding layer vector representation of terms / categories (e.g., common terms having different tense), a transformer neural network, bidirectional and auto-regressive transformer (BART) model architecture, a bidirectional encoder representation from transformers (BERT) model, long short term memory network (LSTM) operation(s), a sentence state LSTM (S-LSTM), a deep learning algorithm, a sequential neural network, a sequential neural network that enables persistent information, a recurrent neural network (RNN), a convolutional neural network (CNN), a neural network, capsule network, a machine learning algorithm, a natural language processing (NLP) technique, sentiment analysis, bidirectional LSTM (BiLSTM), stacked BiLSTM, regular pattern expression matching, and suchlike. Language models, LSTMs, BARTs, etc., can be formed with a neural network that is highly complex, for example, comprising billions of weighted parameters.

[0208] Accordingly, in an embodiment, implementation of CT system 100 and included / associated components, with processes 179A-n, enables natural language processing (NLP) (e.g., utilizing vectors) to identify a previously implemented operation on CT system 110, determine any associated imaging issues (e.g., blur) and how the issue was mitigated / image resolution was improved, apply the previously implemented operation to a current or future operation at CT system 100 regarding in-plane resolution, and further make a recommendation on how the image resolution can be improved.

[0209] EXAMPLE ENVIRONMENTS OF USE

[0210] For simplicity of explanation, the computer-implemented and non-computer- implemented methodologies provided herein are depicted and / or described as a series of acts. It is to be understood that the subject innovation is not limited by the acts illustrated and / or by the order of acts, for example acts can occur in one or more orders and / or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be utilized to implement the computer-implemented and non-computer-implemented methodologies in accordance with the described subject matter. Additionally, the computer-implemented methodologies described hereinafter and throughout this specification are capable of being stored on an article of manufacture to enable transporting and transferring the computer-implemented methodologies to computers. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage media.

[0211] The systems and / or devices have been (and / or will be further) described herein with respect to interaction between one or more components. Such systems and / or componentscan include those components or sub-components specified therein, one or more of the specified components and / or sub-components, and / or additional components. Sub-components can be implemented as components communicatively coupled to other components rather than included within parent components. One or more components and / or sub-components can be combined into a single component providing aggregate functionality. The components can interact with one or more other components not specifically described herein for the sake of brevity, but known by those of skill in the art.

[0212] In order to provide additional context for various embodiments described herein, FIG.15 and the accompanying description are intended to provide a brief, general description of a suitable computing environment 1500 in which the various embodiments described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules or as a combination of hardware and software.

[0213] Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

[0214] The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0215] Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non- removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data.

[0216] Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

[0217] Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

[0218] Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

[0219] With reference again to FIG.15, the example environment 1500 for implementing various embodiments of the aspects described herein includes a computer 1502, the computer 1502 including a processing unit 1504, a system memory 1506 and a system bus 1508. The system bus 1508 couples system components including, but not limited to, the system memory 1506 to the processing unit 1504. The processing unit 1504 can be any of various commercially available processors. Dual microprocessors and other multi processor architectures can also be employed as the processing unit 1504.

[0220] The system bus 1508 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory1506 includes ROM 1510 and RAM 1512. A basic input / output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 1502, such as during startup. The RAM 1512 can also include a high-speed RAM such as static RAM for caching data.

[0221] The computer 1502 further includes an internal hard disk drive (HDD) 1514 (e.g., EIDE, SATA), one or more external storage devices 1516 (e.g., a magnetic floppy disk drive (FDD) 1516, a memory stick or flash drive reader, a memory card reader, etc.) and a drive 1520, e.g., such as a solid state drive, an optical disk drive, which can read or write from a disk 1522, such as a CD-ROM disc, a DVD, a BD, etc. Alternatively, where a solid state drive is involved, disk 1522 would not be included, unless separate. While the internal HDD 1514 is illustrated as located within the computer 1502, the internal HDD 1514 can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment 1500, a solid state drive (SSD) could be used in addition to, or in place of, an HDD 1514. The HDD 1514, external storage device(s) 1516 and drive 1520 can be connected to the system bus 1508 by an HDD interface 1524, an external storage interface 1526 and a drive interface 1528, respectively. The interface 1524 for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.

[0222] The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 1502, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.

[0223] A number of program modules can be stored in the drives and RAM 1512, including an operating system 1530, one or more application programs 1532, other program modules 1534 and program data 1536. All or portions of the operating system, applications, modules, or data can also be cached in the RAM 1512. The systems and methods describedherein can be implemented utilizing various commercially available operating systems or combinations of operating systems.

[0224] Computer 1502 can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system 1530, and the emulated hardware can optionally be different from the hardware illustrated in FIG.15. In such an embodiment, operating system 1530 can comprise one virtual machine (VM) of multiple VMs hosted at computer 1502. Furthermore, operating system 1530 can provide runtime environments, such as the Java runtime environment or the .NET framework, for applications 1532. Runtime environments are consistent execution environments that allow applications 1532 to run on any operating system that includes the runtime environment. Similarly, operating system 1530 can support containers, and applications 1532 can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application.

[0225] Further, computer 1502 can be enabled with a security module, such as a trusted processing module (TPM). For instance, with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer 1502, e.g., applied at the application execution level or at the OS kernel level, thereby enabling security at any level of code execution.

[0226] A user can enter commands and information into the computer 1502 through one or more wired / wireless input devices, e.g., a keyboard 1538, a touch screen 1540, and a pointing device, such as a mouse 1542. Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit 1504 through an input device interface 1544 that can be coupled to the system bus 1508, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.

[0227] A monitor 1546 or other type of display device can be also connected to the system bus 1508 via an interface, such as a video adapter 1548. In addition to the monitor 1546, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0228] The computer 1502 can operate in a networked environment using logical connections via wired or wireless communications to one or more remote computers, such as a remote computer(s) 1550. The remote computer(s) 1550 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1502, although, for purposes of brevity, only a memory / storage device 1552 is illustrated. The logical connections depicted include wired / wireless connectivity to a local area network (LAN) 1554 or larger networks, e.g., a wide area network (WAN) 1556. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

[0229] When used in a LAN networking environment, the computer 1502 can be connected to the local network 1554 through a wired or wireless communication network interface or adapter 1558. The adapter 1558 can facilitate wired or wireless communication to the LAN 1554, which can also include a wireless access point (AP) disposed thereon for communicating with the adapter 1558 in a wireless mode.

[0230] When used in a WAN networking environment, the computer 1502 can include a modem 1560 or can be connected to a communications server on the WAN 1556 via other means for establishing communications over the WAN 1556, such as by way of the Internet. The modem 1560, which can be internal or external and a wired or wireless device, can be connected to the system bus 1508 via the input device interface 1544. In a networked environment, program modules depicted relative to the computer 1502 or portions thereof, can be stored in the remote memory / storage device 1552. It will be appreciated that the network connections shown are examples and other means of establishing a communications link between the computers can be used.

[0231] When used in either a LAN or WAN networking environment, the computer 1502 can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices 1516 as described above, such as but not limited to a network virtual machine providing one or more aspects of storage or processing of information. Generally, a connection between the computer 1502 and a cloud storage system can be established over a LAN 1554 or WAN 1556 e.g., by the adapter 1558 or modem 1560, respectively. Upon connecting the computer 1502 to an associated cloud storage system, the external storage interface 1526 can, with the aid of the adapter 1558 or modem 1560, manage storage provided by the cloud storage system as it would other types of external storage. Forinstance, the external storage interface 1526 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1502.

[0232] The computer 1502 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

[0233] FIG.16 is a schematic block diagram of a sample computing environment 1600 with which the disclosed subject matter can interact. The sample computing environment 1600 includes one or more client(s) 1610. The client(s) 1610 can be hardware or software (e.g., threads, processes, computing devices). The sample computing environment 1600 also includes one or more server(s) 1630. The server(s) 1630 can also be hardware or software (e.g., threads, processes, computing devices). The servers 1630 can house threads to perform transformations by employing one or more embodiments as described herein, for example. One possible communication between a client 1610 and a server 1630 can be in the form of a data packet adapted to be transmitted between two or more computer processes. The sample computing environment 1600 includes a communication framework 1650 that can be employed to facilitate communications between the client(s) 1610 and the server(s) 1630. The client(s) 1610 are operably connected to one or more client data store(s) 1620 that can be employed to store information local to the client(s) 1610. Similarly, the server(s) 1630 are operably connected to one or more server data store(s) 1640 that can be employed to store information local to the servers 1630.

[0234] Various embodiments may be a system, a method, an apparatus or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of various embodiments. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: aportable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0235] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device. Computer readable program instructions for carrying out operations of various embodiments can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand- alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readableprogram instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform various aspects.

[0236] Various aspects are described herein with reference to flowchart illustrations or block diagrams of methods, apparatus (systems), and computer program products according to various embodiments. It will be understood that each block of the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart or block diagram block or blocks.

[0237] The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0238] While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer or computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that various aspects can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of this disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0239] As used in this application, the terms “component,” “system,” “platform,” “interface,” and the like, can refer to and / or can include a computer-related entity or an entity related to an operational machine with one or more specific functionalities. The entities disclosed herein can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but may not be limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized on one computer and / or distributed between two or more computers. In accordance with an aspect of the disclosure, respective components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and / or remote processes such as in accordance with a signal having one or more data packets / data sets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems via the signal). In accordance with another aspect of the disclosure, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which may be operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts, wherein the electronic components can include a processor or other means to execute software or firmware that confers at least in part the functionality of the electronic components. In accordance with another aspect of the disclosure, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system.

[0240] In addition, the term “or” may be intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” may be intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” may be satisfied under any of the foregoing instances. Moreover, articles “a” and “an” as used in the subject specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. As used herein, the terms “example” and / or “exemplary” are utilized to mean serving as an example, instance, or illustration and are intended to be non-limiting. For the avoidance of doubt, the subject matter disclosed herein may not be limited by such examples. In addition, any aspect or design described herein as an “example” and / or “exemplary” may not necessarily to be construed as preferred or advantageous over other aspects or designs, nor it may be meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0241] The herein disclosure describes non-limiting examples. For ease of description or explanation, various portions of the herein disclosure utilize the term “each,” “every,” or “all” when discussing various examples. Such usages of the term “each,” “every,” or “all” are non- limiting. In other words, when the herein disclosure provides a description that is applied to “each,” “every,” or “all” of some particular object or component, it should be understood that this is a non-limiting example, and it should be further understood that, in various other examples, it can be the case that such description applies to fewer than “each,” “every,” or “all” of that particular object or component.

[0242] As it may be employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to, single- core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complexprogrammable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Further, processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units. In this disclosure, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component are utilized to refer to “memory components,” entities embodied in a “memory,” or components comprising a memory. It is to be appreciated that memory and / or memory components described herein can be either volatile memory or non- volatile memory or can include both volatile and non-volatile memory. By way of illustration, and not limitation, non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random-access memory (RAM) (e.g., ferroelectric RAM (FeRAM). Volatile memory can include RAM, which can act as external cache memory, for example. By way of illustration and not limitation, RAM may be available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the disclosed memory components of systems or computer-implemented methods herein are intended to include, without being limited to including, these and any other suitable types of memory.

[0243] What has been described above include mere examples of systems and computer- implemented methods. It is, of course, not possible to describe every conceivable combination of components or computer-implemented methods for purposes of describing this disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of this disclosure are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” may be interpreted when employed as a transitional word in a claim. The descriptions of the various examples have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the examples disclosed. Many modifications and variations can be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described examples. The terminology used herein was chosen to best explain theprinciples of the examples, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the examples disclosed herein.

[0244] This written description uses examples to disclose the invention, including the best mode, and to enable any person skilled in the art to practice the invention, including making and using any computing system or systems and performing any incorporated methods. The patentable scope of the invention may be defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

[0245] Examples of the present disclosure shown in the drawings and described above are example only and are not intended to limit the scope of the appended claims, including any equivalents as included within the scope of the claims. Various modifications are possible and will be readily apparent to the skilled person in the art. It is intended that any combination of non-mutually exclusive features described herein are within the scope of the present invention. That is, features of the described examples can be combined with any appropriate aspect described above and optional features of any one aspect can be combined with any other appropriate aspect. Similarly, features set forth in dependent claims can be combined with non- mutually exclusive features of other dependent claims, particularly where the dependent claims depend on the same independent claim. Single claim dependencies may have been used as practice in some jurisdictions require them, but this should not be taken to mean that the features in the dependent claims are mutually exclusive.

Claims

CLAIMS What is claimed is:

1. A computer tomography (CT) system, comprising: at least one processor; and at least one memory coupled to the at least one processor and having instructions stored thereon, wherein, in response to the at least one processor executing the instructions, the instructions facilitate performance of operations, comprising: acquiring a set of x-ray projections at a detector, wherein the set of x-ray projections are acquired during a rotation of a gantry in the CT system imaging an object, and the set of x-ray projections are acquired based on at least one of an integration angle or effective integration time per projection; generating a line spread function (LSF) model depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter of the CT system and a defined sampling schema; recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space; and incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in-plane resolution of a reconstructed image while simultaneously improving image noise characteristics.

2. The CT system of claim 1, wherein the operations further comprise: modifying operation of two or more charge integrators at the detector, wherein the one or more charge operators are configured to incorporate multiple integrators configured in parallel to one or more detector elements and be read out at a same frequency but with a uniformly distributed phase shift over the two or more integrators to increase a number of view samples per one turn of gantry rotation while maintaining the integration time per view sample, wherein the modification of the operation of the two or more charge integrators improves view sampling.

3. The CT system of claim 1, wherein the operations further comprise: retrospectively summing consecutive views in an overlapping moving window fashion while maintaining the total number of view samples per one turn of the gantry, to improve the integration angle.

4. The CT system of claim 1, wherein the sampling schema is implemented in conjunction with at least one system parameter relating to a detector aperture and an integration arc length to facilitate computation of an energy spread of a line integral relating to a LSF in the projection direction.

5. The CT system of claim 1, wherein the operations further comprise: recovering a frequency of the measured data in conjunction with the LSF model to increase data resolution in a sinogram space.

6. The CT system of claim 5, wherein the recovering of the frequency is implemented in at least one of: a data acquisition hardware configured to process a stream of consecutive views in a field-programmable gate array (FPGA), an algorithm implemented by the at least one processor, or a deep learning algorithm trained on the LSF model to recover the frequency of the measured data to increase the data resolution in sinogram space.

7. The CT system of claim 5, wherein the operations further comprise: implementing a resolution recovery algorithm as a filter in the frequency domain, wherein the resolution recovery algorithm is configured to match with the LSF model.

8. The CT system of claim 5, wherein the operations further comprise: implementing a resolution recovery algorithm as a deconvolution algorithm in the spatial domain.

9. The CT system of claim 5, wherein the operations further comprise: implementing a resolution recovery algorithm as an unsharp masking algorithm using a deconvolution kernel derived from the LSF model in the spatial domain.

10. The CT system of claim 1, wherein the system parameters utilized as part of the reconstruction process comprise incorporated knowledge of the sampling positions of the conjugate line integrals, wherein the operations further comprise: generating an up-sampled sinogram data, wherein the up-sampled sinogram data is configured for subsequent resolution recovery.

11. The CT system of claim 10, the operations further comprising: up-sampling the sinogram based on interlacing of conjugate line integral samples.

12. The CT system of claim 10, the operations further comprising: up-sampling based on a constrained least squares solver configured to iteratively estimate a higher resolution sample from low resolution measurements using the conjugate line integral samples in conjunction with the LSF model to minimize estimation error of an initial measurement.

13. The CT system of claim 1, wherein the system parameters utilized as part of the reconstruction process are updated, during the reconstruction process, with the focal spot position per projection sample, to improve the fidelity of the computed tomographic reconstruction and restore any blur and resolution loss induced by assuming fixed focal spot position.

14. The CT system of claim 13, wherein the focal spot position can be determined by at least one of: experimental modeling estimated over multiple data acquired of a known object to deduce the focal spot position model as a function of projection angle over multiple gantry rotation times, a computational model deduced by mechanical forces on the tube to derive the focal spot positional variation as a function of gantry angle over multiple gantry rotation times, or a system comprising of beam blocker placed close to the source near collimator aperture in the fan direction without any interference from any object in the beam path to derive the focal spot motion as a proportional consequence of the measured signal variation on the detector due to projected shadow from the beam blocker.

15. A computer-implemented method, comprising: modifying, by a device comprising at least processor, the ISO-center alignment geometry of a detector relative to an X-ray source focal spot in a CT system, wherein the modification causes improved conjugate sampling over one turn of gantry rotation; recovering, by the device, a frequency of measured data obtained by a sampling schema to increase data resolution in a sinogram space; and incorporating, by the device, one or more system parameters into an image reconstruction process to propagate the restored data resolution into an improvement in the in-plane resolution of a reconstructed image while ensuring minimal impact to image noise in the reconstructed image.

16. The computer-implemented method of claim 15, wherein the one or more system parameters utilized as part of the reconstruction process comprise incorporated knowledge of the sampling positions of the conjugate line integrals, wherein the method further comprising: generating, by the device, an up-sampled sinogram data, wherein the up-sampled sinogram data is configured for subsequent resolution recovery.

17. The computer-implemented method of claim 14, wherein modification of the ISO-center alignment geometry of the detector relative to the X-ray source focal spot is achieved using a pair of actuators, wherein the pair of actuators are programmed at least one of prior to a scan acquisition or dynamically during a scan acquisition to generate a motion on the detector position to facilitate improved conjugate sampling over one turn of gantry rotation, wherein the detector is placed with an edge detector offset alignment or a programmable offset.

18. A computer program product stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein, in response to being executed, the machine-executable instructions cause a system to perform operations, comprising: acquiring a set of x-ray projections at a detector, wherein the set of x-ray projections are acquired during a rotation of a gantry in the CT system imaging an object, and the set of x-ray projections are acquired based on at least one of an integration angle or effective integration time per projection; generating a line spread function (LSF) model depicting energy distribution of a line integral generated from the object, wherein the LSF model is generated based on at least one geometric parameter of the CT system and a defined sampling schema; recovering a frequency of measured data in the LSF model to increase data resolution in a sinogram space; and incorporating the system parameters in the image reconstruction process to propagate restored data resolution into an improvement in the in-plane resolution of a reconstructed image while simultaneously improving image noise characteristics.

19. The computer program product according to claim 18, the machine executable instructions further comprising: implementing an edge detector offset alignment schema, deriving a sampling schema configured to improve radial view sampling while independently controlling the view integration angle, determining a focal spot deviation, implementing resolution recovery to reduce image blur of a reconstructed image, or identifying one or more image frequencies producing blur in the LSF model.

20. The computer program product according to claim 18, the machine executable instructions further comprising: implementing at least one resolution recovery process during conversion of measured signals received from the detector to imaging data, wherein the conversion process applies a view sampling schema to the measured signals; and implementing at least one resolution recovery process during sharpening of the image data, wherein the sharpening process applies at least one of a LSF model, system geometric parameters, or focal spot estimation data to the image data.

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