System and method for CT image reconstruction

Abstract

To provide efficient methods and systems for reconstruction by removing artifacts in a photon-counting computed tomography (PCCT) system.SOLUTION: A method comprises: during a scan of an imaging subject, obtaining photon counts from a detector element of a photon-counting detector of the PCCT system; encoding the photon counts at a plurality of energy bins of the detector element into a single scalar output value, the single scalar output value representing a distribution of spectral information across the plurality of energy bins; and reconstructing an image from projection data acquired via the photon-counting detector, the projection data including the single scalar output value generated at the detector element; where a basis material discrimination process is not performed during image reconstruction.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the subject matter disclosed herein relate to an imaging system and a method of imaging, and more particularly, to image reconstruction in a computed tomography (CT) imaging system.

Background Art

[0002] In a computed tomography (CT) imaging system, an electron beam generated by a cathode travels to a target in an X-ray source or an X-ray tube. The fan-shaped or conical X-ray beam generated by the collision of electrons with the target travels towards a subject (such as a patient). After the X-rays are attenuated by the object, they collide with an array of X-ray detectors and an image is generated. The quality of a CT image can be improved by using photon counting CT (PCCT). In photon counting CT, the X-ray detector is a photon counting type detector and can count photons to obtain spectral information. However, if there are variations in the performance and materials of the PCCT detector, artifacts may occur in the reconstructed image. These artifacts can be removed by a material discrimination (MD) process, but this process is time-consuming and computationally intensive. As an alternative, a method of removing artifacts using deep learning has been proposed. However, this method requires a considerable amount of training data, and it may not be realistic or achievable to collect the training data. As a result, the performance of this method may be lower than expected.

Summary of the Invention

[0003] In one embodiment, a method for a photon-counting computed tomography (PCT) system includes, during a scan of an object to be imaged, obtaining photon count values from detector elements of a photon-counting detector of the PCCT system, where the photon count values are divided into a plurality of energy bins based on the energy imparted to the detector elements by each photon; encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value, where the single scalar output value represents the distribution of spectral information for the plurality of energy bins; and reconstructing an image from projection data obtained by the photon-counting detector, where the projection data includes the single scalar output value generated by the detector elements. During image reconstruction, a reference material discrimination process is not performed. Calculating the single scalar output value can include multiplying each photon count value by a corresponding weight of a weight vector corresponding to the detector element stored in a memory of the PCCT system. The weight vector can be selected from a plurality of weight vectors generated during calibration of the PCCT system for each detector element prior to the scan, and the weights of the weight vector are calculated based on a method that minimizes the output of an objective function based on calibration detector data.

[0004] The above and other advantages and features of the present disclosure will become apparent from the following detailed description alone, or in combination with the accompanying drawings. It should be understood that the above summary is presented in a simplified form and selected from concepts further described in the detailed description of the invention. This is not intended to identify the key features or essential features of the subject matter claimed, which is defined solely by the claims that follow the detailed description of the invention. Further, the subject matter claimed is not limited to implementations that solve any disadvantages described above or in any part of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Various aspects of the present disclosure can be further understood by reading the following embodiments for carrying out the invention and referring to the accompanying drawings.

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Embodiments for Carrying Out the Invention

[0006] Embodiments for carrying out the invention and embodiments of the subject matter disclosed herein relate to a method and system for reconstructing a computed tomography (CT) image from projection data acquired by a photon-counting computed tomography (PCCT) imaging system. In a CT imaging system, an X-ray source or X-ray tube emits an X-ray beam towards an object (such as a patient), and the X-rays attenuated by the subject are detected by one or more detectors (e.g., a detector array), and projection data used to reconstruct one or more images is generated. An X-ray detector or detector array typically includes a collimator that collimates the X-ray beam received by the detector, a scintillator disposed adjacent to the collimator that converts the X-rays into optical energy, and a photodiode that receives the optical energy from the adjacent scintillator and generates an electrical signal. The intensity of the attenuated X-ray beam radiation received by the detector array typically depends on the amount of attenuation of the X-ray beam by the patient. Each detector element of the detector array generates an individual electrical signal indicative of the attenuated X-ray beam received by each detector element. The electrical signals are transmitted to and analyzed by a data processing system. The data processing system processes the electrical signals to assist in the generation of an image. Generally, in a CT system, the X-ray source and the detector array rotate relative to the gantry within the imaging plane and around the patient, and the image is generated from projection data of a plurality of views at different view angles. For example, for one rotation of the X-ray source, a CT system can generate 1000 views.

[0007] Conventional CT imaging systems utilize detectors that convert radiation energy into an electrical signal, which is integrated, measured, and ultimately digitized over a certain period of time. However, a drawback of such detectors is that they cannot provide data or feedback regarding the number of detected photons and / or the energy of the detected photons. That is, the light emitted by the scintillator is a function of both the number of incident X-rays and the X-ray energy level. The photodiode may not be able to distinguish the energy level or photon count value from the scintillation. For example, there may be cases where two scintillators are irradiated with equal intensity and equal output is obtained for each photodiode. However, despite the equal light output obtained, the number of X-rays received by each scintillator may be different, and the intensity of the X-rays may be different.

[0008] In contrast, PCCT detectors can provide photon counting and / or energy discrimination feedback with high spatial resolution. PCCT detectors can operate in X-ray counting mode and energy measurement mode for each X-ray event and can acquire both anatomical details and tissue characteristic information. In this regard, energy discrimination information or data can be used to reduce the effects such as beam hardening. Furthermore, these detectors assist in the acquisition of tissue discrimination data and thus provide diagnostic information indicating diseases or other medical conditions. Also, PCCT detectors can be used to detect, measure, and characterize substances (such as contrast agents and / or other special materials) injected into the subject by using optimal energy weighting to enhance the contrast of iodine and calcium (as well as other high atomic number substances). As a contrast agent, for example, there is iodine injected into the blood for good visualization. Several materials can be used in the structure of direct conversion type energy discrimination detectors, and a semiconductor is shown as one of the preferred materials. Typical materials used for this include cadmium zinc telluride (CZT), cadmium telluride (CdTe), silicon (Si), etc., and a plurality of anodes divided pixel by pixel are attached to these materials.

[0009] However, as a drawback of photon-counting detectors, due to manufacturing limitations, there is non-uniformity between different detector channels, which may cause ring artifacts to occur in the reconstructed CT image. Also, when the energy response is non-ideal due to different detector materials, the bin count data becomes multi-colored, and beam hardening artifacts may occur in the image reconstructed from the bin count data. To remove artifacts, typically, it is necessary to calibrate the PCCT system again (to address ring artifacts) and / or repeatedly apply a material discrimination (MD) process based on algorithms such as maximum likelihood estimation, which may be complex and time-consuming. Furthermore, the amount of computing resources and memory required for MD processing may increase. If image generation is slow, the user has to wait for the results, and the number of imaging tasks that can be performed within a given time frame may decrease. When there are many imaging tasks, it becomes necessary to generate images within a short time frame. For example, there is a perfusion examination that requires high-speed imaging with accurate measurements, and the perfusion examination needs to track the uptake of a contrast agent over multiple scans by analyzing a plurality of images for each.

[0010] To realize the advantages of a PCCT detector without generating artifacts, thereby eliminating the need for recalibration and / or the MD process and improving the speed of image reconstruction, herein, an energy weighting method is used to convert the bin count data of an energy discriminative PCCT detector into a single virtual measurement value representing the energy distribution for a plurality of bins, thereby proposing a system and method for generating high-quality (e.g., artifact-free) images. Instead of reconstructing an image based on all projection data including bin count values, by reconstructing an image based on a single virtual measurement value calculated for each detector and applying a correction function, a CT image without ring artifacts and / or beam hardening artifacts can be obtained quickly. As a result, the amount of computational resources used during image reconstruction and the computational time for image reconstruction can be reduced. Furthermore, since the amount of data transmitted from each detector to the image reconstructor is reduced, the data transmission time is shortened, and the image reconstruction time can be further shortened. The CT images include virtual monochromatic X-ray images (VMIs) of different keV, material discrimination images, and single kV CT images equivalent to the conventional ones.

[0011] Examples of PCCT systems that can be used to perform imaging scans according to this technology are shown in FIGS. 1 and 2. FIG. 3 shows an exemplary detector array of a PCCT system, where photons of X-rays irradiated on a subject by an X-ray source are counted by detectors of the detector array. FIG. 4A roughly shows how various parameters of energy weighting and correction functions are generated in the calibration process of a CT system, which can be realized according to the detailed procedure schematically shown in FIG. 5. FIG. 4B shows how the parameters of energy weighting and correction functions are applied to raw projection data during a scan to generate an image. During the scan, the counted photons can be divided into bins, and the photon count values in each bin are weighted and added to reduce the amount of data used for image reconstruction, while the energy discriminative data obtained by the detector is retained according to the method of FIG. 6. The weighted sum of the photon count values can be calculated according to the method of FIG. 7.

[0012] Figures 1 to 3 show exemplary configurations regarding the relative positional relationships of various elements. When elements are illustrated as being in direct contact with each other or directly coupled to each other, these elements can be referred to, in at least one embodiment, as elements in direct contact or directly coupled elements, respectively. Similarly, elements shown as being continuous or adjacent to each other can be referred to, in at least one embodiment, as continuous elements or adjacent elements to each other, respectively. As an example, elements in surface contact with each other can be referred to as elements in surface contact. As another example, when elements arranged apart from each other have a space between the elements but no other elements exist, in at least one example, they can be referred to as elements apart from each other. Also, as another example, elements shown as being above and below each other, opposite to each other, or left and right of each other can be referred to as upper and lower elements, opposite elements, or left and right elements to each other, respectively. Further, as shown in the figure, in at least one example, the uppermost element or the uppermost point of an element can be referred to as the "upper part" of the element, and the lowermost element or the lowermost point of an element can be referred to as the "lower part" of the element. In this specification, upper / bottom, upper side / lower side, up / down represent relative ones with respect to the vertical axis of the figure and are used to explain the relative positions of the elements of the figure to each other. Thus, an element shown above another element is, in one example, vertically arranged above the other element. As yet another example, it can be said that the shapes of the elements shown in the figure have those shapes (e.g., circular, linear, planar, curved, rounded, chamfered, angled, etc.). Further, elements shown as intersecting each other can be referred to, in at least one example, as intersecting elements or elements intersecting each other. Further, an element shown within another element or outside another element can be referred to, in one example, as an element shown within or outside another element.

[0013] FIG. 1 shows an exemplary PCCT system 100 configured to perform CT imaging using a photon-counting detector. In particular, the PCCT system 100 is configured to image a subject 112 (such as a patient), an inanimate object, one or more manufactured parts, and / or a foreign object (such as an implant, stent, and / or contrast agent present in the body). The PCCT system 100 includes a gantry 102, which can further include at least one X-ray source 104 that irradiates an X-ray radiation beam 106 (see FIG. 2) used for imaging the subject 112 lying on the table 114. Specifically, the X-ray source 104 is configured to irradiate the X-ray radiation beam 106 toward a detector array 108 disposed on the opposite side of the gantry 102. Although only a single X-ray source 104 is shown in FIG. 1, in some exemplary embodiments, multiple X-ray sources and multiple detectors may be used to output multiple X-ray radiation beams 106 and acquire projection data corresponding to patients at the same or different energy levels. In some embodiments, the X-ray source 104 can perform dual-energy gemstone spectral imaging (GSI) by peak kilovolt (kVp) high-speed switching. In the embodiments described herein, the X-ray detector employed is a photon-counting detector capable of distinguishing X-ray photons of different energies from each other.

[0014] In certain embodiments, the PCCT system 100 further includes an image processor unit 110 that reconstructs an image of the target volume of the subject 112 using an iterative image reconstruction method or an analytical image reconstruction method. For example, the image processor unit 110 can use an analytical image reconstruction technique such as filtered back projection (FBP) to reconstruct an image of the target volume of the patient. As another example, the image processor unit 110 may use an iterative image reconstruction technique such as ASIR (advanced statistical iterative reconstruction), CG (conjugate gradient), MLEM (maximum likelihood expectation maximization), MBIR (model-based iterative reconstruction), etc. to reconstruct an image of the target volume of the subject 112. In some examples, the image processor unit 110 can also use an analytical image reconstruction technique (such as FBP) in addition to the iterative image reconstruction technique.

[0015] In the configuration of some CT imaging systems, the X-ray source irradiates a conical X-ray radiation beam that is defined with respect to the X-Y-Z Cartesian coordinate system and is generally referred to as the "imaging volume". The X-ray radiation beam passes through the object to be imaged (such as a patient or a subject). After being attenuated by the object, the X-ray radiation beam impinges on an array of detector elements. The intensity of the attenuated X-ray radiation beam received by the detector array depends on the attenuation of the X-ray radiation beam by the object. Each detector element of the array generates an individual electrical signal that is a measurement of the X-ray beam attenuation at the detector position. The attenuation measurements from all the detector elements are individually acquired to create a transmission profile.

[0016] In some CT imaging systems, the X-ray source and detector array are rotated within the imaging volume around the object to be imaged by a gantry such that the angle at which the radiation beam intersects the object continuously changes. A group of X-ray radiation attenuation measurements (e.g., projection data) obtained from the X-ray detector array at a particular gantry angle is called a “view”. A “scan” of an object includes a set of views created at different gantry angles or view angles while the X-ray source and detector make one rotation.

[0017] FIG. 2 shows an exemplary imaging system 200 similar to the PCCT system 100 of FIG. 1. In aspects of the present disclosure, the imaging system 200 is configured to image a subject 204 (e.g., the subject 112 of FIG. 1). In one embodiment, the imaging system 200 includes a detector array 108 (see FIG. 1). The detector array 108 further includes a plurality of detector elements 202. The plurality of detector elements 202 sense an X-ray radiation beam 106 (see FIG. 2) passing through the subject 204 (such as a patient) and acquire corresponding projection data. In some embodiments, the detector array 108 is manufactured in a multi-slice configuration including a plurality of columns of cells or detector elements 202. In such a configuration, one or more additional columns of detector elements 202 are arranged in parallel to acquire projection data.

[0018] In certain embodiments, the imaging system 200 is configured to move to different angular positions around the subject 204 to acquire desired projection data. Accordingly, the gantry 102 and the components attached to the gantry can be configured to rotate around the center of rotation 206, for example, to acquire projection data at different energy levels. Alternatively, in embodiments where the projection angle varies as a function of time with respect to the subject 204, the attached components may be configured to move along a general curve rather than along an arc.

[0019] When the X-ray source 104 and the detector array 108 rotate, the detector array 108 collects data on the attenuated X-ray beam. The data collected by the detector array 108 is preprocessed and calibrated so that the data is adjusted to represent the line integral of the attenuation coefficient of the scanned subject 204. The processed data is generally referred to as a projection. In some embodiments, the individual detectors or detector elements 202 of the detector array 108 can include photon-counting detectors that record the interaction of individual photons in one or more energy bins.

[0020] The set of acquired projection data can be used for basis material discrimination (BMD). During BMD, the measured projections are converted into a set of material density projections. The material density projections can be reconstructed to form a set of material density maps or a set of material density images for each basis material such as bone, soft tissue, etc., and / or a contrast map. These density maps or density images can be associated in turn to form a 3D volumetric image of the basis materials (e.g., bone, soft tissue, and / or contrast agent) of the imaging volume.

[0021] Once reconstructed, the internal features of the subject 204, represented by the density of two basis materials, are revealed by the basis material images generated by the imaging system 200. The density images can be displayed to show these features. In the conventional approach to the diagnosis of medical conditions (such as disease states), more generally, for the diagnosis of medical events, a radiologist or physician is thought to examine a hard copy or the displayed density image of the density image to identify the feature portions of interest. Such feature portions include specific anatomical structures or lesions, sizes, and shapes of organs, as well as other feature portions that can be identified from the image based on the skills and knowledge of individual experts.

[0022] In one embodiment, the imaging system 200 includes a control mechanism 208 that controls the movement of components (such as the rotation of the gantry 102 and the operation of the X-ray source 104). In certain embodiments, the control mechanism 208 further includes an X-ray controller 210 configured to supply power and timing signals to the X-ray source 104. Further, the control mechanism 208 includes a gantry motor controller 212 configured to control the rotation speed and / or rotation position of the gantry 102 based on imaging requirements.

[0023] In certain embodiments, the control mechanism 208 further includes a data acquisition system (DAS) 214 configured to sample analog data received from the detector element 202 and convert the analog data into a digital signal for subsequent processing. The DAS 214 can be configured to selectively aggregate analog data from a subset of the detector elements 202 into a so-called macro detector. The data sampled and digitized by the DAS 214 can be transmitted through the slip ring 213 to a computer or computing device 216. In one example, the computing device 216 stores the data in a storage device or mass storage device 218. The storage device 218 can be, for example, a hard disk drive, a floppy disk drive, a compact disc read / write (CD-R / W) drive, a digital versatile disc (DVD) drive, a flash drive, and / or a solid state storage drive.

[0024] Furthermore, computing device 216 provides instructions and parameters to one or more of DAS 214, X-ray controller 210, and gantry motor controller 212 to control system operation (such as data acquisition and / or data processing). In certain exemplary embodiments, computing device 216 controls system operation based on operator input. Computing device 216 receives operator input, including instructions and / or scanning parameters, for example, by an operator console 220 operably coupled to computing device 216. Operator console 220 can include a keyboard (not shown) or a touch screen so that an operator can specify instructions and / or scanning parameters.

[0025] In FIG. 2, only one operator console 220 is shown, but, for example, two or more operator consoles may be coupled to imaging system 200 for entering or outputting system parameters, requesting an examination, graphing data, and / or viewing images. Further, in certain embodiments, imaging system 200 can be coupled to a plurality of displays, printers, workstations, and / or similar devices locally or remotely located within a facility or hospital or at completely different locations through one or more deployable wired and / or wireless networks (Internet and / or virtual private network, wireless telephone network, wireless local area network, wired local area network, wireless wide area network, wired wide area network, etc.).

[0026] In one embodiment, for example, imaging system 200 includes or is coupled to a Picture Archiving and Communication System (PACS) 224. In an exemplary embodiment, PACS 224 is further coupled to remote systems such as a radiology information system, a hospital information system, and / or is coupled to an internal or external network (not shown) to enable an operator located at a different location to supply instructions and parameters and / or access image data.

[0027] The computing device 216 operates the table motor controller 226 using instructions and parameters supplied by the operator and / or defined by the system. The table motor controller 226 can control the table 114, which can be an electric table. Specifically, the table motor controller 226 can move the table 114 so that the subject 204 is properly positioned in the gantry 102 in order to acquire projection data corresponding to the target volume of the subject 204.

[0028] As described above, the DAS 214 samples and digitizes the projection data acquired by the detector element 202. Thereafter, the image reconstructor 230 performs fast reconstruction using the sampled and digitized X-ray data. FIG. 2 illustrates the image reconstructor 230 as an independent entity, but in certain embodiments, the image reconstructor 230 may form part of the computing device 216. Alternatively, the image reconstructor 230 may not be present in the imaging system 200, and instead, the computing device 216 may perform one or more functions of the image reconstructor 230. Further, the image reconstructor 230 may be located locally or remotely and may be operably connected to the imaging system 200 using a wired or wireless network. In particular, in one exemplary embodiment, computing resources within a “cloud” network cluster may be used for the image reconstructor 230.

[0029] In one embodiment, the image reconstructor 230 stores the reconstructed image in the storage device 218. Alternatively, the image reconstructor 230 may transmit the reconstructed image to the computing device 216 to generate patient information useful for diagnosis and evaluation. In some embodiments, the computing device 216 may transmit the reconstructed image and / or patient information to a display or display device 232 communicatively coupled to the computing device 216 and / or the image reconstructor 230. In some embodiments, the reconstructed image may be transmitted from the computing device 216 or the image reconstructor 230 to the storage device 218 for short-term or long-term storage.

[0030] Through the slip ring 213, information can be transmitted between the components present in the gantry 102 and an external device (such as the computing device 216 and / or the image reconstructor 230). The slip ring 213 enables easy electronic communication with the rotating gantry.

[0031] Referring now to FIG. 3, a PCCT photon-counting detector array 300 is shown, which is a non-limiting example of the detector array 108 of FIG. 2. The detector array 300 includes rails 304, between which collimator blades or plates 306 are disposed. The plates 306 are arranged to collimate the X-rays 302, and after the X-rays 302 are collimated, the beam impinges on a plurality of detector modules 308 of the detector array 300 disposed between the plates 306. As an example, the detector array 300 can include 57 detector modules 308, and each detector module 308 has an array size of 64x16 detector elements (e.g., pixels). As a result, the detector array 300 will have 64 rows and 912 columns (16 pixels x 57 detector modules), and 64 slices of data can be collected simultaneously for each gantry rotation (e.g., the gantry 102 of FIG. 1).

[0032] As described above, each detector element of each detector module 308 can be designed to directly convert radiation energy into an electrical signal including energy discrimination data or photon count data. For example, when a photon collides with a detector element of detector module 308, a charge proportional to the energy of the photon is generated within the semiconductor layer of the detector element. A comparator can compare the voltage of the generated charge with one or more threshold values and increment the count value of one bin (out of a plurality of bins) based on the voltage with respect to the one or more threshold values. The plurality of bins can include, for example, eight bins having energy threshold values set to perform optimal material discrimination.

[0033] The output of the detector element can be referred to as a bin count value because the photon count value is divided into energy bins based on the energy of each photon incident on the detector array. The number of energy bins is based on the configuration of the detector. For example, a silicon detector is configured to discriminate photon energy into eight energy bins, and a cadmium telluride detector is configured to discriminate photon energy into five bins. The energy threshold values defining the energy bins can be determined at the calibration stage and / or based on a specific scan protocol. In some examples, the energy threshold values can be determined to optimize reference material discrimination and / or to maximize the spectral information detected for a given incident spectrum emitted by an X-ray source. In non-limiting examples, the threshold values of the energy bins are 4 keV, 14 keV, 30 keV, 37 keV, 47 keV, 58 keV, 67 keV, and 79 keV for an eight-bin detector, and 10 keV, 34 keV, 50 keV, 62 keV, and 76 keV for a five-bin detector. The bin count values (e.g., photon count values divided into a plurality of energy bins) can be obtained for each detector element of the detector and for each view obtained during the scan.

[0034] Thus, the X-ray beam generates a plurality of photon count values for each detector element (e.g., one or more count values for each energy bin), and as a result, substantially more data is generated than the data generated by an integrating detector. Generating an image from the data collected by a photon-counting detector is time-consuming, which may cause a delay in image review. A further problem with the PCCT system is that the outputs generated in different detector channels vary due to manufacturing limitations, which may result in ring artifacts in the reconstructed CT image. Furthermore, if the energy responses from different detector materials are non-ideal, there may be an increase in polychromatic binning data, which may result in beam hardening artifacts in the reconstructed image.

[0035] As will be described in more detail below, by pseudo-generating a conventional CT image that does not perform material discrimination of projection data by a complex and / or time-consuming iterative algorithm (such as maximum likelihood estimation), the time required to generate an image can be reduced without generating ring artifacts and / or beam hardening artifacts. The pseudo-conventional CT image can be generated by calculating a single scalar output value for each detector element based on the linear weighted sum of the photon count values in each bin of the detector element. The weights used to generate the linear weighted sum can be determined in the calibration process of the CT system based on the material discrimination of one or more phantoms scanned during calibration, as will be described later with reference to FIGS. 4A and 5. By outputting a single scalar value at each detector element instead of multiple bin count values, the amount of data transmitted from the detector element to the image reconstructor is reduced, and the time and complexity of image processing can be further reduced.

[0036] Figure 4A shows an overview of a general process 400 for generating a set of energy weights and correction function parameters during calibration of a PCCT system (such as the PCCT system 100 of FIGS. 1 and 2). Prior to performing a scan, during calibration 402 of the CT system, the PCCT system is typically calibrated so that the output generated by the detector elements of the PCCT system is reliably consistent. Due to manufacturing limitations, not all detector elements generate the same output, and there is a risk of variation between the outputs of the detector elements. To minimize the variation, a set of calibration vectors 406 is generated during a scan of a phantom containing a known reference material. Each calibration vector can include a plurality of normalized calibration values generated from respective plural bin count values generated by the detector elements when scanning the phantom. Using these calibration values, a set of parameters can be generated for each detector element. This set of parameters is used in an equation for correcting the outputs of different detector elements and can keep the outputs within a desired range. To avoid confusion, it should be understood that the term "calibration vector" herein represents the normalized calibration values collected during the calibration process and not the set of parameters ultimately used to correct the detector outputs (this set of parameters may otherwise be called a calibration vector). Further, other aspects of the PCCT system can be initialized and / or calibrated during the calibration process (such as energy bin thresholds that define the energy bins of each detector, etc.).

[0037] As will be described in more detail below with reference to FIG. 5, after calibration 402 of the CT system is performed and calibration vector 406 is obtained, during an additional calibration task, based on one or more imaging modes 404 supported by the CT system (e.g., VMI, MBD, conventional CT images, etc.), a set of reference outputs of the detector elements of the CT system can be generated using calibration vector 406. For example, a first set of reference outputs 408 can be generated from calibration vector 406 based on a first imaging mode 404, a second set of reference outputs 408 can be generated from calibration vector 406 based on a second imaging mode 404, and so on.

[0038] Once the reference outputs are obtained, a target weight determination process 410 can be performed for each detector element of a plurality of detector elements of the detector of the PCCT system. During the target weight determination process 410, a set of target energy weights 412 can be determined that minimize the variation between the weighted sum of the bin count values in the detector element and a second weighted sum using a predefined equal weight (e.g., air count value). Also, a set of target correction function parameters 414 can be determined to minimize the squared difference between the estimated output and the ground truth reference output. The target weight determination process 410 will be described in more detail below with reference to FIG. 5. The energy weights 412 and the correction function parameters 414 are stored in the memory of the PCCT system and can be used later during scanning, as will be described below with reference to FIG. 4B.

[0039] Figure 4B shows a general process 450 for generating a virtual monoenergetic (VMI) image with reduced artifacts that can be reconstructed more quickly than a conventional VMI image that depends on a bone mineral density (BMD) process during image reconstruction by applying a set of energy weights 460 and correction function parameters 462 during a scan 452 performed using a photon-counting computed tomography (PCCT) system. The scan 452 is initiated based on an imaging mode selection 454. In the imaging mode selection 454, the desired type of image can be selected by the user of the PCCT system. The energy weights 460 and the correction function parameters 462 can be generated during calibration of the PCCT system, as described with reference to FIG. 4A, and thus, the energy weights 460 and the correction function parameters 462 can be selected from the energy weights 412 and the correction function parameters 414 based on the imaging mode selection 454.

[0040] The scan 452 generates raw projection data 456. While the raw projection data 456 is being generated, the raw projection data 456 can be processed in a data processing stage 458. The data processing step 458 will be described in further detail below with reference to FIGS. 6 and 7. During the data processing step 458, the energy weights 460 and the correction function parameters 462 can be applied to the raw projection data 456 to generate a set of adjusted projection data 464. The set of adjusted projection data 464 can contain less data than the raw projection data 456. Specifically, the raw projection data 456 includes bin count data of a plurality of detector elements, while the adjusted projection data 464 can include a single scalar value representing the bin count data and can be made to not include the bin count data. The adjusted projection data 464 is used by an image reconstructor 466 (e.g., image reconstructor 230) to reconstruct an image 468. The reconstructed image 468 can have a quality similar to that of an image reconstructed from the raw projection data 456. However, the reconstructed image 468 can be reconstructed more quickly than an image reconstructed from the raw projection data 456.

[0041] Referring to FIG. 5, a method 500 for generating a set of energy weight vectors during calibration of a PCCT system (such as the PCCT system 100 of FIGS. 1 and 2) is shown. In the PCCT system, the weight vectors can be applied to bin count values during subsequent scans. When the weight vectors are applied to the bin count values, the bin count data becomes a single value, and this single value can be used to reconstruct an image, as will be described later with reference to FIG. 6. Method 500 can be executed in accordance with instructions stored in the memory of one or more controllers or computing devices (such as DAS 214, X-ray controller 210, image reconstructor 230, and / or computing device 216, etc.) included as part of and / or operably coupled to a CT imaging system.

[0042] At 502, method 500 includes performing a calibration scan to generate and store calibration vectors. During the calibration scan, detector data (e.g., calibration vectors) is obtained and air count values are generated by scanning an object of known size and known composition (referred to as a phantom) and / or by performing the scan with no object being scanned. The detector data can include photon count values divided into a plurality of bins (such as 8 or 5 energy bins depending on the detector configuration) based on the energy of each photon.

[0043] A calibration scan can be performed to calibrate a particular CT imaging system (such as determining the response of each detector element, identifying the optimal energy bin threshold, etc.). During the calibration scan, one or more phantoms are scanned, and the material composition and thickness of the object to be imaged are made known. For example, known materials of different combinations and different thicknesses can be scanned using a plurality of settings of current and kVp (peak kilovoltage), with the plurality of settings being different from each other. The phantom can be composed of one or more suitable materials (such as polyvinyl chloride (PVC) or polyethylene (PE)). In some examples, the phantom can include regions of water, iodide, calcium, and / or other substances (such as other contrast agents). In some embodiments, one or more of the plurality of phantoms can be a step wedge phantom. During the calibration scan, the CT imaging system can be controlled such that the X-ray source emits X-rays, which are detected by the detector of the CT imaging system after being attenuated by each scanned phantom. As described above, the calibration scan can be performed with no object being scanned to generate an air count vector for each detector element. The air count vector can be used in the weighting method described below.

[0044] After calibration, the calibration vectors are stored in the memory of the PCCT system for each detector channel. Each calibration vector is composed of a set of bin count values A that are normalized with respect to the pair of thicknesses of the corresponding ground truth reference material. Here, the size of A corresponds to the number N mat of the desired reference materials and the number N cal of calibration points.

Number

[0045] A set n of calibration vectors that describe the native bin count values collected at each calibration point can be expressed as follows.

Equation

[0046] In 504, method 500 includes generating a reference output from a calibration vector for a desired imaging mode. The imaging mode can be a mode based on what kind of image is desired (e.g., VMI, BMD, normal CT image, etc.). The desired imaging mode defines the reference kilovolt voltage (keV) to be used and can generate a reference output. According to the desired imaging mode, a calibration vector can be used to generate a reference output of the detector for a given number of reference materials. The reference output is a ground truth output determined based on a pair of the known linear attenuation value of each reference material and the known thickness of the reference material. For example, in the case of 80 keV VMI, when using PE and PVC as reference materials, the reference output O of the detector can be described by the following equation. [Number] Here, μ PE (80 keV) and μ PVC (80 keV) are the known linear attenuation values of PE and PVC at 80 keV, and A represents a matrix of different combinations of PE and PVC with known path lengths (thicknesses). O is a one-dimensional vector, and the size of O corresponds to the number of calibration points. Each element of O is a scalar value representing the attenuation when a monochromatic X-ray beam of 80 keV passes through the corresponding pair of PE / PVC thicknesses.

[0047] Therefore, the reference output or ground truth output typically estimates the information generated from the material discrimination process after the projection data is acquired.

[0048] In 506, method 500 includes determining a target energy weight for each detector, where the target energy weight minimizes the bias and variance of the objective function based on the reference output from the detector. The target energy weight can be stored in a weight vector, and the size of the weight vector can be made the same number as the energy bins of the detector. Each weight of the weight vector can be applied to a respective energy bin of the detector, thereby adjusting the count value of one or more energy bins or the count value of all energy bins. For example, the weight vector indicates that bin 1 is weighted at 0.5 and bin 2 is weighted at 1. After applying the weights of the weight vector, the weighted count values can be added to obtain a linearly weighted sum and generate a reference output.

[0049] Generally, the weights can take on any continuous real-valued numbers. The weights can be normalized to the range of ±1. This is because normalization or scaling generates weights such that the sum of the weighted bin count values contains the same amount of information. In some examples, the weights may be binary weights, which are a special case of more general continuous weights. In the case of binary weights, the weights are either 0 or 1, and each original bin may or may not contribute to the weighted bin by addition. The contributions from the original bins may be mutually exclusive, in which case each original bin contributes only once, and each original bin contributes only once to the added bin. Each weight vector can be used to weight the complete / original bin count values, and the respective weighted bin count values may be added.

[0050] The method used determines a target energy weight that minimizes the objective function. The objective function can be the exemplary objective function shown below. The objective function includes a first term for minimizing the variance of the detector output by generating an output based on the weighted sum of the bin count values as opposed to using equal weights for each bin, and a second term for minimizing the bias of the estimated output with respect to the reference output O of the ground truth. [Number] Here, l is normalized with respect to the air scan data represented by N i (0) . [Number] l j (0) is the case where equal weights are used and is represented by the following equation. [Number] α is a trade-off parameter. f(l j , a) is obtained by fitting a desired simple correction function such that f(l, a) = 0. Here, a is a vector of function parameters applied to l. In this way, for each detector pixel, a target energy weight and a are generated. At 508, determining the target energy weight can include determining a parameter vector parameter a that minimizes the difference between f(l j , a) and the corresponding reference output. In some embodiments, the function f may be a linear function, such that the output of f(l, a) is based on a linear addition of factors defined by a. In other embodiments, the function may be a non-linear function or a non-parametric function (e.g., a look-up table).

[0051] At 510, method 500 includes storing a weight vector and function parameters for each detector channel. The weight vector and function parameters can be stored in memory (e.g., in DAS214) to be applied during a subsequent scan of the object to be imaged (such as the scan described below with reference to FIG. 5). Then, method 500 ends.

[0052] FIG. 6 shows a method 600 for a PCCT system (e.g., PCCT system 100) that reconstructs a CT image from projection data that includes a weighted linear sum of photon count values in a plurality of energy bins, rather than from photon count data from each bin of a plurality of energy bins. Method 600 can be executed in accordance with instructions stored in a memory of one or more controllers or computing devices (such as DAS 214, x-ray controller 210, image reconstructor 230, and / or computing device 216, etc.) that are part of and / or operably coupled to a CT imaging system.

[0053] At 602, method 600 includes determining scan parameters to be applied during a scan of an object to be imaged. The scan parameters can be determined based on a selected scan protocol and / or based on user input received by a computing device of the CT imaging system (e.g., computing device 216). Scan parameters include a scan prescription (e.g., voltage and current of an x-ray source, slice thickness, gantry table speed, etc.), the anatomical structure to be scanned, whether one or more contrast agents have been administered to the object to be imaged, and other parameters. Scan protocols include an imaging mode that specifies the desired image (such as VMI) to be reconstructed and a peak kilovolt voltage to be applied during the scan.

[0054] In 604, method 600 includes scanning an object to be imaged according to a selected scan protocol to obtain detector data including bin count values. The detector data includes photon count values divided into a plurality of energy bins for each detector element of the photon counting detector (e.g., for each detector element 202) based on the energy imparted to each photon by the photon counting detector, which is referred to herein as bin count values. During a patient scan, the X-ray source of the PCCT system (e.g., X-ray source 104 of FIGS. 1 and 2) is controlled to emit X-rays according to a scan prescription set by the selected scan protocol (e.g., at a set current and voltage of the X-ray source or X-ray tube). As the X-ray source and the detector array rotate around the object to be imaged, detector data can be obtained from the detector elements of the detector array (e.g., detector array 108 of FIGS. 1 and 2), and as a result, detector data for a plurality of views is obtained. For each view, photon count values for each energy bin are generated for each detector element of the detector array (e.g., while the detector array is being read out by DAS 214). For example, in a detector configuration having 8 energy bins, 8 photon count values can be generated for each detector element of the detector array and for each view.

[0055] At 606, method 600 includes selecting a weight vector and correction function parameters for each detector element based on a predetermined imaging mode and associated kilovolt voltage. In some embodiments, the size of the object being imaged can also be a factor in selecting the weight vector. For example, the large size of a first patient can be approximated by a first set of pairs of thicknesses of reference materials used to generate a first calibration vector, thereby allowing the first weight vector to be selected at least in part based on the first calibration vector. The small size of a second patient can be approximated by a second set of pairs of thicknesses of reference materials used to generate a second calibration vector, thereby allowing the second weight vector to be selected at least in part based on the second calibration vector. In various embodiments, the weight vector can be selected from a set of candidate weight vectors generated during the calibration process described above with reference to FIG. 5 and stored in the memory of the CT system (e.g., in DAS 214).

[0056] At 608, method 600 includes repeatedly applying, for each detector row / each detector channel, the selected weight vector and correction function parameters to process the bin count values at each detector element in order to adjust the output of the detector elements. As described above, the selected energy weight vector and correction function parameters can include values optimized for reconstructing an image in a predetermined imaging mode. The adjusted output of the detector element can be a single scalar value representing the energy distribution for a plurality of energy bins of the detector element, rather than a vector of photon count values observed in each energy bin. Processing the bin count values will be described in more detail below with reference to FIG. 7.

[0057] After calculating the adjusted output, the corresponding value is sent to an image reconstructor (e.g., image reconstructor 230) or other suitable computing device (e.g., from a DAS), where one or more images can be reconstructed using a single scalar value from a plurality of detector elements. In some examples, the original bin count value / full bin count value can also be sent to the image reconstructor. In this way, an initial image can be generated relatively quickly using a linear weighted sum, while additional images can be generated, if necessary, at a later time using the original bin count value / full bin count value (since it may take time to send the original bin count value / full bin count value to the image reconstructor).

[0058] At 610, method 600 includes reconstructing one or more images using the adjusted output of the detector elements (e.g., adjusted projection data). The images can be reconstructed by an image reconstructor or other suitable computing device. In various embodiments, the images can be VMI (e.g., grayscale images) or BMD images. At 612, the reconstructed images are displayed on a display device and / or stored in memory (e.g., in a PACS as part of a patient examination).

[0059] In this way, an advantage of method 600 is to generate an image with reduced artifacts without relying on the time-consuming and resource-intensive BMD process. In other words, in a conventional CT system, after projection data is acquired, to reconstruct an image, the voxel count values are discriminated against a selected reference material or attenuation factor (e.g., Compton scattering and photoelectric effect) using maximum likelihood estimation (MLE), least squares method, polynomial fitting, neural network, or other appropriate discrimination methods, and the MD image is reconstructed using a filtered backprojection method or other appropriate reconstruction techniques. Next, the VMI at a selected energy (e.g., keV level) can be formed by a linear combination of the reconstructed images. By reconstructing an image based on the adjusted projection data (e.g., the voxel count values are weighted and added using a predefined weight vector generated during the calibration of the PCCT system) instead of the projection data including the voxel count values, the BMD process may not be used, and an artifact-free image can be reconstructed faster and with fewer computational resources than the conventional approach described above. As a result, the reconstructed image can be generated in a shorter time with the same or equivalent image quality and more efficiently in terms of computational and memory resources.

[0060] FIG. 7 shows a method 700 for generating a single scalar output value of a detector element that can be used to process bin count values in a detector element of a PCCT system (such as PCCT system 100) and reconstruct an image using a predefined weight vector. The predefined weight vector can be generated during calibration of the PCCT system as described above with reference to FIG. 5. Method 700 can be executed according to instructions stored in the memory of one or more controllers or computing devices (such as DAS 214, X-ray controller 210, image reconstructor 230, and / or computing device 216) included as part of and / or operably coupled to a CT imaging system. In various embodiments, method 700 can be implemented as part of method 600 described above with reference to FIG. 6.

[0061] At 702, method 700 includes obtaining a complete bin count value from the detector element. The complete bin count value can be similar to the complete bin count values described above with respect to FIGS. 5 and 6 and is a photon count value divided into the total number of energy bins allowed by the detector configuration (such as 5 or 8 bins). The bin count value can be obtained for each detector element of the detector array and for each view obtained during the scan.

[0062] At 704, method 700 includes using a selected weight vector to process the bin count values and generate a single scalar output for the detector element. In various embodiments, the selection of the weight vector can be performed as described above with reference to FIG. 6. Processing the bin count values can include calculating a weight-based line integral based on energy according to the following equation.

Equation

[0063] Here, N i is the corresponding bin count value, and Ni (0) is the corresponding air count value (for example, when no phantom is used and the electron beam passes through air). Thus, at 706, as shown by this equation, method 700 includes generating a linearly weighted sum of the bin count value and the air count value in the detector element. In other words, the detector data from each detector element can be converted from all bins (for example, 8 bins or 5 bins) to a linearly weighted sum. Each bin is weighted by an amount specified by the weight of the weight vector, and then the weighted bins are added together to obtain the weighted and added bins. At 708, calculating the line integral includes calculating the ratio of the linearly weighted sum of the bin count values using the selected weight vector to the weighted sum of the air count values using the corresponding air count vector, and at 710, calculating the line integral includes calculating the negative logarithm of the ratio.

[0064] At 712, method 700 includes adjusting the output of the energy-based weighted line integral by applying the correction function O=f(l,a) used during calibration to the calculated line integral to generate a single scalar output value. The correction function can be applied using a set of correction function parameters a determined during calibration as described above with reference to FIG. 5. In some embodiments, the correction function can be applied using a look-up table.

[0065] Therefore, a plurality of energy bin count values (such as 5 or 8 energy bins) can be converted into a single value representing the distribution of spectral energy for the plurality of energy bins. The single value can be transmitted for image reconstruction, thereby accelerating the reconstruction of at least the initial image during a CT examination.

[0066] Accordingly, this specification describes a system and method for generating an image by a PCCT system with a reduced number of artifacts without performing BMD processing to remove artifacts. Instead of BMD processing, energy weighting processing is performed during calibration of the system, and a set of target weights is determined that can be used to weight bin count values during subsequent scans. Different weight vectors can be stored for different pairs of imaging modes and kilovolts, and different thicknesses of the material scanned during calibration. The weight vectors are used to generate scalar values output by each detector element of the PCCT system during the scan. An image (such as a VMI image, a BMD image, or a normal CT image) can be generated from the scalar values rather than from the complete bin count data. As a result, image generation is performed faster and more efficiently than other methods that rely on reconstructing an image from complete bin count data and applying a computationally intensive iterative BMD process, reducing resource usage and saving time. Also, the method described in this specification can generate higher quality images than the images that can currently be generated using a deep learning neural network approach, and further, as an additional advantage, it does not depend on training data, eliminating the need to collect, store, and process training data, thus reducing the use of computational and memory resources. The technical effect of using a weighting method to generate an image from the output of scalar values of detector elements rather than complete bin count data is that it can reduce the time spent generating the image, the processing power used by the PCCT system, and the amount of memory.

[0067] The present disclosure also supports a method for a photon-counting computed tomography (PCT) system. The method includes, during a scan of an object to be imaged, obtaining photon count values from detector elements of a photon-counting detector of the PCCT system, where the photon count values are divided into a plurality of energy bins based on the energy imparted to the detector elements by each photon; encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value, where the single scalar output value represents the distribution of spectral information for the plurality of energy bins; and reconstructing an image from projection data obtained by the photon-counting detector, where the projection data includes the single scalar output value generated by the detector elements, and during image reconstruction, a reference material discrimination process is not performed. In a first embodiment of the method, the single scalar output value is generated by the detector elements and transmitted to an image reconstructor of the PCCT system, the image is reconstructed, and the photon count values are not transmitted to the image reconstructor. In a second embodiment of the method, optionally including the first embodiment, encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value includes calculating a linear weighted sum of the photon count values. In a third embodiment of the method, optionally including one or both of the first and second embodiments, calculating the linear weighted sum further includes multiplying each photon count value by a corresponding weight of a weight vector corresponding to the detector elements stored in a memory of the PCCT system, where the weight vector is selected from a set of weight vectors generated during calibration of the PCCT system. In a fourth embodiment of the method, optionally including one or more or each of the embodiments from the first to the third embodiments, the corresponding weights of the weight vector are calculated based on calibration detector data obtained from a calibration scan of a phantom, where the calibration detector data includes, for each detector element of the photon-counting detector, photon count values divided into a plurality of energy bins based on the energy of each photon incident on the detector element.In a fifth embodiment of the method, optionally including one or more of the first to fourth embodiments or each embodiment, the weight vector is selected from a set of the weight vectors based on a kilovolt voltage associated with a selected imaging mode. In a sixth embodiment of the method, optionally including one or more of the first to fifth embodiments or each embodiment, calculating the linear weighted sum further includes calculating a line integral of a ratio of the linear weighted sum to a second linear weighted sum of a plurality of air count values generated during calibration of the PCCT system, and the second linear weighted sum of the plurality of air counts is calculated using the weight vector. In a seventh embodiment of the method, optionally including one or more of the first to sixth embodiments or each embodiment, the method further includes minimizing a bias of an estimated output of the detector element relative to a ground-truth reference output of the detector element, where the ground-truth reference output is generated using the calibration detector data, and a variance between the linear weighted sum and a second estimated output of the detector element based on equal weighting of photon count values in the plurality of energy bins, and generating the weights according to a method for minimizing the variance. In an eighth embodiment of the method, optionally including one or more of the first to seventh embodiments or each embodiment, generating a ground-truth reference output using the calibration detector data includes receiving an imaging mode selected by a user of the PCCT system, calculating attenuation coefficients of a selected plurality of reference substances from the calibration detector data, and calculating a ground-truth reference output based on the selected imaging mode, the attenuation coefficients, and an estimated thickness of the selected plurality of reference substances. In a ninth embodiment of the method, optionally including one or more of the first to eighth embodiments or each embodiment, the method further includes calculating a single scalar output value of the detector element by applying a correction function to the linear weighted sum, and the correction function includes parameters determined during calibration of the PCCT system.In a tenth embodiment of the present method, optionally, one or more embodiments or each embodiment among the first to ninth embodiments are included, and the image is any one of a virtual monochromatic energy image (VMI), a substance discrimination image, and a normal single kV CT image.

[0068] The present disclosure also supports a photon-counting computed tomography (PCCT) system. The system includes an X-ray source that irradiates an X-ray beam toward a subject to be imaged, a photon-counting detector that receives the X-ray beam attenuated by the subject, and a data acquisition system (DAS) operably connected to the photon-counting detector, which acquires detector data from detector elements of the photon-counting detector during a scan of an object to be imaged, wherein the detector data includes photon count values divided into a plurality of energy bins based on the energy imparted to the detector elements by each photon, acquiring, encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value representing the distribution of spectral information for the plurality of energy bins, and reconstructing an image without performing a reference material discrimination process from the projection data acquired by the photon-counting detector, wherein the projection data includes the single scalar output value generated by the detector elements, and a data acquisition system (DAS) configured to perform the reconstruction. In a first embodiment of the system, the single scalar output value is a first linear weighted sum of a plurality of photon count values, a first linear weighted sum obtained by multiplying each photon count value by a corresponding weight of a weight vector, and a second linear weighted sum of a plurality of air count values, obtained by multiplying each air count value by the corresponding weight of the weight vector, and the air count value and the weight vector are calculated based on calculating a line integral of a ratio between the first linear weighted sum and the second linear weighted sum, which are generated during calibration of the PCCT system. In a second embodiment of the system, optionally including the first embodiment, the weight vector is calculated based on a calibration vector obtained from a calibration scan of a phantom, and the calibration vector includes photon count values divided into a plurality of energy bins of the detector elements based on the energy of each photon incident on the detector elements.In a third embodiment of the present system, optionally including one or both of the first and second embodiments, the weight vector is a bias of the estimated output of the detector element relative to the reference output of the grand truth of the detector element, the reference output of the grand truth being generated using the calibration detector data, the bias, and the estimated output of the detector element based on a linear weighted sum of photon count values in the plurality of energy bins, and the second estimated output of the detector element based on an equal weighting of the photon count values in the plurality of energy bins, and is generated according to a method of minimizing the variance therebetween. In a fourth embodiment of the present system, optionally including one or more or each of the first to third embodiments, a single scalar output value of the detector element is calculated by applying a correction function to a calculated line integral, the correction function including parameters determined during calibration of the PCCT system.

[0069] The present disclosure also supports a method for a photon-counting computed tomography (PCCT) system. The method includes, during calibration of the PCCT system, performing a first calibration scan of a phantom on detector elements of a photon-counting detector of the PCCT system to obtain a calibration vector for the detector elements, where the calibration vector includes photon count values in each of a plurality of energy bins based on the energy of each photon incident on the detector elements; obtaining the calibration vector; performing a second calibration scan with no object being scanned and generating a vector of air count values, where the air count vector includes photon count values in each of the plurality of energy bins based on the energy of each photon incident on the detector elements; generating the air count vector; using the calibration vector to generate a ground-truth reference output for the detector elements; determining a set of weights that minimizes a bias of the output of the detector elements with respect to the ground-truth reference output and a variance of the output of the detector elements with respect to an output based on equal weights; storing a weight vector including the calibration vector, the air count vector, and the set of weights in a memory of the PCCT system; during a subsequent scan of an object to be imaged, obtaining photon count values in the plurality of energy bins of the detector elements; retrieving the weight vector and the air count vector from the memory; using the weight vector and the air count vector to calculate a linearly weighted sum of the photon count values; applying a correction function to the linearly weighted sum to generate an output of the detector elements, where the correction function includes parameters determined during calibration of the PCCT system; generating; and reconstructing an image from projection data of the photon-counting detector, where the projection data includes the output of the detector elements; reconstructing. In a first embodiment of the method, the method further includes reconstructing an image from the projection data without performing a reference material discrimination process.In a second embodiment of the method, optionally including the first embodiment, calculating a linear weighted sum of the photon count values using the weight vector and the air count vector further includes calculating a line integral of a ratio of the linear weighted sum of the photon count values to a second linear weighted sum of the air count values. In a third embodiment of the method, optionally including one or both of the first and second embodiments, generating a ground truth reference output includes receiving an imaging mode selected by a user of the PCCT system, and calculating a ground truth reference output based on the selected imaging mode, the calibration vector, attenuation coefficients of a selected plurality of reference substances, and estimated thicknesses of the selected plurality of reference substances.

[0070] When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there is one or more of the elements. The terms “first,” “second,” etc. are not meant to indicate order, quantity, or importance, but are used to distinguish one element from another. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements other than the recited elements may be present. As used herein, when terms such as “connected to” and “coupled to” are used, one object (e.g., a material, element, structure, member, etc.) can be connected or coupled to another object whether or not one object is directly connected or coupled to the other object or whether there is one or more intervening objects between one object and the other object. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be construed as excluding the existence of additional embodiments that also incorporate the recited features.

[0071] In addition to the modified forms shown previously, many other modified and alternative structures can be considered by those skilled in the art without departing from the spirit and scope of this description, and the claims are intended to include such modified forms and structures. Therefore, although the above information has been described in particular detail with respect to what is currently considered to be the most practical and preferred embodiments, it will be apparent to those skilled in the art that many modifications are possible in terms of form, function, method of operation, and use (without limitation thereto), etc., without departing from the principles and concepts described herein. Also, in this specification, the examples and embodiments are meant to be illustrative in every respect and should not be construed in any way as limiting.

Explanation of Reference Numerals

[0072] 100 PCCT system 102 Gantry 104 X-ray source 106 X-ray emission beam 108 Detector array 110 Image processor unit 112 Subject 114 Table 200 Imaging system 202 Detector element 204 Subject 206 Center of rotation 208 Control mechanism 210 X-ray controller 212 Gantry motor controller 213 Slip ring 214 Data acquisition system (DAS) 216 Computing device 218 Volatile storage device 218 Storage device 220 Operator console 226 Table motor controller 230 Image composer 232 Display device 300 Detector array 302 X-ray 304 Rail 306 Plate 308 Detector module 400 Process 402 Calibration 404 Imaging mode 406 Calibration vector 408 Reference output 410 Target weight determination process 412 Target energy weight 414 Correction function parameter 452 Scan 454 Imaging mode selection 456 Raw projection data 458 Data processing stage 460 Energy weight 462 Correction function parameter 464 Projection data 466 Image constructor 468 Image 500 Method 600 Method 700 Method

Claims

Claim 1 A method for a photon-counting computed tomography (PCT) system, comprising: during a scan of an object to be imaged, obtaining photon count values from detector elements of a photon-counting detector of the PCT system, wherein the photon count values are divided into a plurality of energy bins based on the energy imparted to the detector elements by each photon (604); encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value, wherein the single scalar output value represents the distribution of spectral information for the plurality of energy bins (608, 704); and reconstructing an image from projection data obtained by the photon-counting detector, wherein the projection data includes the single scalar output value generated by the detector elements (610). The method further includes: during image reconstruction, not performing a reference material discrimination process; encoding the photon count values in the plurality of energy bins of the detector elements into a single scalar output value includes calculating a linear weighted sum of the photon count values (706); calculating the linear weighted sum further includes multiplying each photon count value by a corresponding weight of a weight vector corresponding to the detector element stored in a memory of the PCT system, wherein the weight vector is selected from a set of weight vectors generated during calibration of the PCT system (506). Claim 2 The method of claim 1, wherein the single scalar output value is generated by the detector elements, transmitted to an image reconstructor of the PCT system, the image is reconstructed, and the photon count values are not transmitted to the image reconstructor. Claim 3 The method of claim 1, wherein the corresponding weights of the weight vectors are calculated based on calibration detector data obtained from a calibration scan of a phantom, and the calibration detector data includes photon count values divided into a plurality of energy bins based on the energy of each photon incident on the detector element for each detector element of the photon-counting detector. Claim 4 The method of claim 1, wherein the weight vector is selected from the set of weight vectors based on a selected imaging mode (504). Claim 5 Calculating the linear weighted sum further includes calculating a line integral of a ratio between the linear weighted sum and a second linear weighted sum of a plurality of air count values generated during calibration of the PCCCT system, wherein the second linear weighted sum of the plurality of air count values is calculated using the weight vector. The method according to claim 1.

6. A bias of the estimated output of the detector element with respect to the ground truth reference output of the detector element, wherein the ground truth reference output is generated using the calibration detector data, and The variance between the linear weighted sum and a second estimated output of the detector element based on equal weighting of the photon count values in the plurality of energy bins The method according to claim 3, further including generating the weights according to a method of minimizing.

7. Generating a ground truth reference output using the calibration detector data includes Receiving an imaging mode selected by a user of the PCCCT system, Calculating attenuation coefficients of a plurality of selected reference substances from the calibration detector data, and Calculating a ground truth reference output (504) based on the selected imaging mode, the attenuation coefficients, and the estimated thicknesses of the plurality of selected reference substances The method according to claim 6, including.

8. The method according to claim 5, further including calculating a single scalar output value of the detector element by applying a correction function to the linear weighted sum, wherein the correction function includes parameters determined during calibration of the PCCCT system.

9. The image is A virtual monoenergetic image (VMI), A material discrimination image, and A CT image of a single kV The method according to claim 1, which is any one of the images.

10. A photon counting computed tomography (PCCCT) system (100), comprising An X-ray source (104) that irradiates an X-ray beam toward a subject (112) to be imaged, A photon counting detector (108) that receives the X-ray beam attenuated by the subject, and A data acquisition system (DAS) (214) operably connected to the photon counting detector, During the scan of the object to be imaged, obtaining detector data from a detector element (202) of a photon-counting detector (108), wherein the detector data includes photon count values divided into a plurality of energy bins based on the energy imparted to the detector element (202) by each photon, obtaining; encoding the photon count values in the plurality of energy bins of the detector element (202) into a single scalar output value representative of the distribution of spectral information for the plurality of energy bins; and reconstructing an image (468) from projection data (464) obtained by the photon-counting detector (108) without performing a reference material discrimination process, wherein the projection data (464) includes a single scalar output value generated by the detector element (202), reconstructing; A data acquisition system (DAS) configured to perform; Including, The single scalar output value is, A first linear weighted sum of a plurality of photon count values, the first linear weighted sum obtained by multiplying each photon count value by a corresponding weight of a weight vector; and A second linear weighted sum of a plurality of air count values, the second linear weighted sum obtained by multiplying each air count value by a corresponding weight of the weight vector, wherein the air count values and the weight vector are generated during calibration of the PCCCT system (100); and A system calculated based on calculating a line integral of the ratio between.

11. The system according to claim 10, wherein the weight vector is calculated based on a calibration vector (406) obtained from a calibration scan of a phantom, and the calibration vector (406) includes photon count values divided into a plurality of energy bins of the detector element (202) based on the energy of each photon incident on the detector element (202).

12. The weight vector is, A bias of an estimated output of the detector element relative to a ground truth reference output (408) of the detector element (202), wherein the ground truth reference output (408) is generated using the calibration vector (406); and The variance between the estimated output of the detector element (202) based on the linearly weighted sum of the photon count values in the plurality of energy bins and a second estimated output of the detector element (202) based on equal weighting of the photon count values in the plurality of energy bins The system according to claim 11, generated according to a method of minimizing

Images