221 results about "Helical computed tomography" patented technology

A computer-implemented method useful for performing seismic tomography, including obtaining seismic data comprising distributed acoustic sensing (DAS) data, comprising at least one of P-wave or S-wave travel times from a plurality of seismic events to a plurality of recording stations coupled to a network of optical fibers in a geological structure; and generating tomography data from the DAS data, wherein tomography data is useful for characterizing the geological structure. We employ distributed acoustic sensing data recorded along a 100-km fiber-optic cable traversing the caldera to image its subsurface structure. Our images highlight a definite separation between the shallow hydrothermal system and the large magma chamber located at ˜12 km depth. The combination of the geological evidence with our results shows how fluids exsolved through second boiling provide the source of the observed uplift and seismicity. Thus Fiber-seismic tomography reveals that the unrest in the Long Valley Caldera is likely driven by fluid from second boiling, not new magma intrusion.

A computed tomography imaging system (102) includes a gantry (104). The computed tomography imaging system also includes a rotating frame 106 rotatably supported in the gantry. The rotating frame comprises an X-ray source (114) and an X-ray radiation sensitive detector (120), the X-ray source being configured to emit X-ray radiation traversing an examination region, the X-ray radiation sensitive detector is disposed opposite the X-ray source across the examination region and is configured to detect X-ray radiation traversing the examination region and to generate a signal indicative of the detected X-ray radiation. The computed tomography imaging system also includes at least one component (202, 214) of the gantry or rotating frame that generates audible noise. The computed tomography imaging system also includes an audible noise reducer (128) configured to reduce the audible noise. The audible noise reducer includes a resonator that is tuned to a first frequency of the audible noise.

An X-ray tomography sample changing hardware including a support bracket configured to attach to an X-ray tomography support; a mounting arm attached to the support bracket, the mounting arm having a beam, at least one slot formed in the beam, the at least one slot including a mouth configured to receive a sample stage; the sample stage including a body having an axis with an upper portion and a lower portion axially opposite the upper portion; the lower portion configured to mate with a receiver on a sample manipulator for an X-ray tomography machine; and multiple chambers formed in the body and aligned axially between the upper portion and the lower portion, each of the multiple chambers including a mounting feature configured to support a part within each of the multiple chambers.

A laser system is calibrated with a tomography system capable of measuring locations of structure within an optically transmissive material such as a tissue of an eye. Alternatively or in combination, the tomography system can be used to track the location of the eye and adjust the treatment in response to one or more of the location or an orientation of the eye. In many embodiments, in situ calibration and tracking of an optically transmissive tissue structure such as an eye can be provided. The optically transmissive material may comprise one or more optically transmissive structures of the eye, or a non-ocular optically transmissive material such as a calibration gel in a container or an optically transmissive material of a machined part.

The processing circuitry acquires examination information related to a PCCT scan, the examination information including generation purpose information indicating a generation purpose of an image and generation process information indicating a generation process of the image according to the generation purpose. The processing circuitry generates, according to the generation process, a PCCT image according to the generation purpose based on count data collected by the PCCT scan. The processing circuitry stores the examination information including the generation purpose information and the generation process information in a storage device in association with the PCCT image.

For controlling reconstruction in emission tomography, the quality of data for detected emissions and / or the application controls the settings used in reconstruction. For example, a count density of the detected emissions is used to control the number of iterations in reconstruction to more likely avoid over and under fitting. The count density may be adaptively determined by re-binning through pixel size adjustment to find a smallest pixel size providing a sufficient count density. As another example, the detected data may have poor quality due to motion or high body mass index (BMI) of the patient, so the reconstruction is set to perform differently (e.g., less smoothing for high motion or a different number of iterations for high BMI). The quality of the data may be used in conjunction with the application or task for imaging the patient to control the reconstruction.

Systems and methods for performing medical imaging analysis tasks from PCCT (Photon Counting Computed Tomography) imaging data are provided. The PCCT imaging data acquired from the PCCT imaging device is received. A plurality of PCCT virtual images are generated from the PCCT imaging data. A plurality of medical imaging analysis subtasks are performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. The results of the medical imaging analysis subtasks are combined to generate results of the medical imaging analysis task. And outputting the result of the medical imaging analysis task.

A convolutional neural network (CNN) is employed in an automated feature and / or anomaly detection system for analyzing images provided by X-ray imaging system. The automated detection system operates in a manner that reduces the number of full-resolution CNN convolution layers required in order to speed up the network inference and learning processes for the detection system. To do so, the detection system utilizes as an input a more compact representation of the tomographic data to alleviate the CNN memory footprint and computation time issues in prior art X-ray systems.

Methods and systems are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a method for a photon-counting computed tomography (PCCT) system comprises, adjusting an X-ray tube output current of the PCCT system across and / or within one or more views, the current adjusted between a first current and a second current, the first current higher than the second current; for a view of the one or more views scanned by the PCCT system, applying a first pile-up correction to a first photon count output at each detector of a detector array of the PCCT system at the first current, the first pile-up correction calculated based on a second pile-up correction applied to a second photon count output at each detector at the second current; and reconstructing an image based on the corrected first photon count and the corrected second photon count.

In emission imaging, one, two, or a few topograms without a full CT scan are used to localize, such as through triangulation, and / or generate an attenuation map, such as based on model fitting. A limited radiation exposure, rather than a longer, larger full CT exposure, is needed to localize and / or provide attenuation correction for emission imaging, such as cardiac SPECT imaging.

A tube voltage control circuitry switches a tube voltage applied to an X-ray tube between a first tube voltage and a second tube voltage of lower value than the first tube voltage. A first signal generating circuitry generates a first switching signal at a first time interval. A view switching circuitry switches a view based on the first switch signal. A data acquisition circuitry performs data acquisition for each view via an X-ray detector. A second signal generating circuitry generates a second switch signal at a second time interval shorter than the first time interval. A gain switching circuitry switches gains of the data acquisition circuitry based on the second switch signal.

System (FS) and related method for facilitating planning of raw data acquisition by a tomographic imaging apparatus (IA). The system (FS) comprises input interface (IN) through which is receivable input 3D surview image (V0) of a movable object (OI) occupying a first portion of 3D space, the surview image (V0) previously obtained the tomographic imaging apparatus based on raw data acquired at a lower quality than a quality intended for the planned raw data acquisition, and the surview image representative of a first motion state of the object motion. A predictor component (PC) predicts, based on the input surview image(V0), predicted 3D surview images (Vj, j>0) representable of the object in other, predicted, motion states, and the object in the said motion states occupying other portions of space. Output interface (OUT) provides the predicted surview images (Vj) for the said facilitating of the said planning of the raw data acquisition.

Medical computed tomography (CT) data is processed as follows. Projection data obtained by scanning a subject is retrieved. The retrieved projection data is processed to reconstruct a three-dimensional image. A two-dimensional image is generated based on the reconstructed three-dimensional image. A disease or an uncertainty in an identification of the disease is identified in the two-dimensional image. The projection data or the three-dimensional image is reprocessed into an updated three-dimensional image such that at least one para-meter of the reprocessing is based on the disease or the uncertainty in the identification of the disease. An updated two-dimensional image is then generated based on the updated three-dimensional image.

A method and system for correcting detector defects in an X-ray microscope system based on deep learning. Normal geometry projections and offset geometry projections are collected for a given detector, mappings between defective regions and healthy regions are created, and a machine learning system is trained using these projections. The trained neural network system is then used to correct the tomographic projection dataset, thereby improving the image quality of the resulting reconstructed tomographic image volume set.

A cone beam breast computed tomography scanning system includes a vertical plane gantry subsystem pivotally connected to a foundation element for ergonomic patient positioning.

An X-ray tomography sample changing hardware including a support bracket configured to attach to an X-ray tomography support; a mounting arm attached to the support bracket, the mounting arm having a beam, at least one slot formed in the beam, the at least one slot including a mouth configured to receive a sample stage; the sample stage including a body having an axis with an upper portion and a lower portion axially opposite the upper portion; the lower portion configured to mate with a receiver on a sample manipulator for an X-ray tomography machine; and multiple chambers formed in the body and aligned axially between the upper portion and the lower portion, each of the multiple chambers including a mounting feature configured to support a part within each of the multiple chambers.

The invention relates to a detection system (100) for a computed tomography system (200). The detection system (100) comprises a first detector (110) and a second detector (120), each detector being configured for detecting X-ray radiation of the computed tomography system (200) impinging on a respective detector. The detection system further comprises a repositioning mechanism (130) configured for repositioning at least one of the first detector (110) and the second detector (120) relative to the other of the first detector (110) and the second detector (120).

A method and a related apparatus for performing a tomographic examination of an object (2) which advances through an examination area (6), wherein the examination area (6) is irradiated with x-rays transversally to a motion trajectory of the object (2) and the residual intensity of the x-rays which have crossed the object (2) is repeatedly detected to obtain, for each detection, an electronic two-dimensional pixel map, the two-dimensional maps thus obtained being processed by a computer to obtain a three-dimensional tomographic reconstruction of the object (2); wherein, during the advancement, the object (2) is made or let rotate, at least partly uncontrolled, in such a way that the object (2) rotates around one or more rotation axes which are transversal both to the motion trajectory and to the propagation directions of the x-rays crossing it; and wherein a computer also determines the spatial position in which the object (2) is located relative to the one or more emitters (4) and / or the one or more detectors (5) at the instant when each two-dimensional map is detected, and factors this in the tomographic reconstruction.

In some implementations, the device may include a plurality of ultrasound sensors configured to capture tomographical information of a physiological structure. In addition, the device may include an ultrasound coupling medium on each of the plurality of ultrasound sensors. The device may include a processing device configured to: apply beamforming techniques to the ultrasound sensors; capture, using the plurality of ultrasound sensors, bodily data of overlapping volumes of the physiological structure; process the bodily data to generate a vessel location model; and locate a blood vessel from the generated vessel location model.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (≥1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

A method for providing thermal conductivity data relating to an anatomical structure, comprises: receiving first spectral computed tomography data relating to the anatomical structure; calculating a fat map of the anatomical structure and a water map of the anatomical structure based on the first spectral computed tomography data; calculating the thermal conductivity data relating to the anatomical structure based on the fat map and the water map; and providing the thermal conductivity data.

A computed tomography device comprises a gantry with an opening, and a radiation protection apparatus covering the opening. The radiation protection apparatus has a holding structure, a radiation protection curtain and a pivoting apparatus. An examination object can be inserted into the opening along a system axis of the gantry when the holding structure is in a first position relative to the gantry. The holding structure is pivotably mounted relative to the gantry about a pivot axis via the pivoting apparatus such that the holding structure can be lowered relative to the gantry from the first position to a second position by a first pivoting motion of the holding structure about the pivot axis. The radiation protection curtain is arranged on the holding structure. A lower region of the radiation protection curtain hangs down from the holding structure.

A system and method for producing a computed tomography (CT) medical image includes receiving x-rays passing through an object with a photon-counting detector system, which includes a plurality of detector pixels configured to generate a photon-counting signal in response to receiving each photon of the x-rays having passed through the object. The method also includes summing a charge associated with each photon received at a given detector pixel of the plurality of pixels to generate a charge integration signal, utilizing the charge integration signal to correct a count of the photon-counting signal for pileup-induced count losses to create a corrected photon-counting signal, and reconstructing an image of the object using the corrected photon-counting signal.

The invention relates to a method for reconstructing an object by x-ray tomography by means of x-ray sources and detectors. The method comprises the following steps: obtaining (EO) positions of the x-ray sources and detectors; pre-calculating (E1) a set of directions; calculating (E2) a tomographic incompleteness vector map; calculating (E3) a tomographic incompleteness map of the metrics IM(x); calculating (E4) regularization terms from the tomographic incompleteness vector map and from the tomographic incompleteness map of the metrics; and solving (E5), iteratively, a regularized tomographic reconstruction problem by means of the calculated regularization terms.

A system (FS) and related method for facilitating contrast agent-based tomography imaging may comprise: an input interface (IN) for receiving a specification of imaging operations of an imaging device (IA) of tomography type and a 3D survey image volume (V0) of a patient obtained by the imaging device (IA) in a preparation phase prior to a contrast agent-assisted imaging phase; a segmenter (SEG) for segmenting a monitored area m-ROI in the 3D survey image volume, the m-ROI being associated with a target anatomical feature that can be specified in the specification; and an output interface (OUT) providing a reference position (z0) of the segmented m-ROI in the 3D survey volume to facilitate monitoring of the presence of contrast agent related to the target anatomical feature.

Detection of markers in a medical image volume is an important step of developing various diagnostic procedures. The invention relates to a method for detecting markers in a medical image volume. The medical image volume may be selected from a computed tomography volume or a magnetic resonance volume. The invention includes a residual U-Net architecture to generate a heat map describing the probability of finding a flag, which heat map can be combined with a 3D microspace-to-numerical transformation (DSNT) to obtain coordinates of the flag. According to the invention, evaluation is carried out on two data sets: a data set comprising 157 coronary computed tomography angiography (CCTA) scans and a data set comprising 77 computed tomography angiography (CTA) scans. Compared to known methods, the proposed method provides significantly improved accuracy of flag detection, in particular for open flag detection.

A cone beam breast computed tomography system includes a breast stabilization unit for maintaining the position and stability of a breast during imaging.

A mechanism for generating an artifact estimation image that represents the effect of cone-beam artifacts in a computed tomography (CT) image. This is achieved by identifying locations of gradients (i.e., abrupt changes in intensity) in an axis of the CT image that is parallel to a rotation axis of a CT system that generated the CT image, where each gradient represents a source of a cone-beam artifact. A look-up table is used to individually identify the effect of the cone-beam artifact on a region around each identified location of the gradient, thereby generating the artifact estimation image.

Provided are: a molded article having excellent strength and appearance; a method for producing the molded article; and a resin composition and a molding material for 3D printers, which are capable of providing the molded article. In one embodiment, provided is a molded article containing a thermoplastic resin and cellulose microfibers, the molded article being an output of a 3D printer and having a region having a thickness of 2.5 mm or more, at least one 2 mm * 2 mm * 2 mm cubic region selected from the location having a thickness of 2.5 mm or more so as not to include the outermost layer of the molded object has a porosity of 10 vol% or less as determined by an X-ray computed tomography (CT) method.

Proposed concepts thus aim to provide schemes, solutions, concept, designs, methods and systems pertaining to representing flux-dependent artefacts of tomographic image data. In particular, embodiments aim to provide a method for representing flux-dependent artefacts of tomographic image data by processing the tomographic projection data, in which unwanted flux dependent artefacts can be more easily detected than in image data. Artefact data can thus be generated by processing the tomographic projection data. The artefact data can then be used along with the projection data to generate an artefact map which represents the flux-dependent artefacts in at least a portion of tomographic image data. The tomographic image data corresponds to a reconstruction of the said tomographic projection data. In other words, the tomographic image data is data generated via the reconstruction of the tomographic projection data.