Abstract: A method of determining a position offset includes receiving a first detection result from a first pixel of a plurality of pixels of a radiation detector, wherein the plurality of pixels are disposed on an incident side of the radiation detector on which an anti-scatter grid (ASG) is arranged, the plurality of pixels being aligned in at least a channel direction; receiving a second detection result from a second pixel of the plurality of pixels, wherein a septa of the ASG is arranged over a portion of the first pixel but is not arranged over any portion of the second pixel as viewed from the incident side of the radiation detector; estimating a positional offset of the septa of the ASG, based on the first detection result and the second detection result.

Abstract: A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

Abstract: A photon counting detector (PCD) apparatus includes a PCD array including a plurality of micro-pixels positioned in at least one of a channel direction and a row direction; and processing circuitry configured to: receive signals from each of the plurality of micro-pixels; configure the PCD array to include (a) a first micro-pixel area including a first group of plural micro-pixels of the plurality of micro-pixels and (b) a second micro-pixel area including a second group of plural micro-pixels of the plurality of micro-pixels, such that a portion of the first and second groups of plural micro-pixels overlap between the first and second groups; bin the signals from the first group of plural micro-pixels into a first virtual bin value; and bin the signals from the second group of plural micro-pixels into a second virtual bin value.

Abstract: A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

Abstract: A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

Abstract: An apparatus and method to obtain input projection data based on radiation detected at a plurality of detector elements, reconstruct plural uncorrected images in response to applying a reconstruction algorithm to the input projection data, segment the plural uncorrected images into two or more types of material-component images by applying a deep learning segmentation network, generate output projection data corresponding to the two or more types of material-component images based on a forward projection, generate corrected multi material-decomposed projection data based on the generated output projection data corresponding to the two or more types of material-component images, and reconstruct the multi material-component images from the corrected multi material-decomposed projection data to generate one or more corrected images.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

Abstract: An apparatus, system and method for calibrating an x-ray apparatus including acquiring sinogram data by scanning a symmetrical phantom using a plurality of detector channels; generating mirror-copied sinogram data by mirror-copying at least one of first sinogram data and second sinogram data of the acquired sinogram data, wherein the first sinogram data and the second sinogram data are generated by dividing the sinogram data at a center detector channel of the plurality of detector channels; outputting a first reconstructed image by reconstructing the mirror-copied sinogram data; and determining a calibration parameter based on the first reconstructed image.

Abstract: An apparatus, system and method for calibrating an x-ray apparatus including acquiring sinogram data by scanning a symmetrical phantom using a plurality of detector channels; generating mirror-copied sinogram data by mirror-copying at least one of first sinogram data and second sinogram data of the acquired sinogram data, wherein the first sinogram data and the second sinogram data are generated by dividing the sinogram data at a center detector channel of the plurality of detector channels; outputting a first reconstructed image by reconstructing the mirror-copied sinogram data; and determining a calibration parameter based on the first reconstructed image.

Abstract: A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network together with a multi-resolution detector to perform X-ray projection imaging to provide improved resolution similar to a single-resolution detector but at lower cost and less demand on the communication bandwidth between the rotating and stationary parts of an X-ray gantry. The DL network is trained using a training dataset that includes input data and target data. The input data includes projection data acquired using a multi-resolution detector, and the target data includes projection data acquired using a single-resolution, high-resolution detector. Thus, the DL network is trained to improve the resolution of projection data acquired using a multi-resolution detector. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., noise and artifacts).

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: A CT imaging apparatus has processing circuitry that is configured to obtain projection data collected by a CT detector during a scan of an object. The processing circuitry is also configured to perform iterative reconstruction of the projection data to generate a current image. The iterative reconstruction includes filtering forward-projected data during backprojection or filtering image data prior to forward projection to model system optics. The processing circuitry is also configured to combine the current image with a previously-obtained image to generate an updated image.

Abstract: A CT imaging apparatus has processing circuitry that is configured to obtain projection data collected by a CT detector during a scan of an object. The processing circuitry is also configured to perform iterative reconstruction of the projection data to generate a current image. The iterative reconstruction includes filtering forward-projected data during backprojection or filtering image data prior to forward projection to model system optics. The processing circuitry is also configured to combine the current image with a previously-obtained image to generate an updated image.

Abstract: Iterative reconstruction (IR) algorithms are advantageous over standard filtered backprojection (FBP) algorithms by improving resolution and noise performance. In this regard, model-based IR algorithms (MBIR) have been developed to incorporate accurate system models into IR and result in a better image quality than IR algorithms without a system model. System optics are included in both forward and backprojection (IRSOM-FPBP).

Abstract: Iterative reconstruction (IR) algorithms are advantageous over standard filtered backprojection (FBP) algorithms by improving resolution and noise performance. In this regard, model-based IR algorithms (MBIR) have been developed to incorporate accurate system models into IR and result in a better image quality than IR algorithms without a system model. System optics are included in both forward and backprojection (IR-SOM-FPBP).

Abstract: A method of computed-tomography and a computed-tomography apparatus in which x-ray projection data is acquired at a number of views for a scan of an object. Partial images are created from data for a desired number of said views. Full scan images are created from plural ones of the partial images. Non-overlapping time images are created from the full-scan images. Gradient images are also created. An improved image is created by weighting respective ones of the full scan and non-overlapping time images using the gradient image. The improved image has increased sharpness with reduced noise.

Abstract: Embodiments and processes of computer tomography perform tasks associated with selecting a portion of projection or emission data that contain the least amount of motion based upon a predetermined motion index, a derivative of plane integral (DPI). Other embodiments and processes of computer tomography perform tasks associated with determining an amount of direction-dependent motion in an object based upon a comparison of the DPIs in predetermined directions.

Abstract: A method of computed-tomography and a computed-tomography apparatus in which x-ray projection data is acquired at a number of views for a scan of an object. Partial images are created from data for a desired number of said views. Full scan images are created from plural ones of the partial images. Non-overlapping time images are created from the full-scan images. Gradient images are also created. An improved image is created by weighting respective ones of the full scan and non-overlapping time images using the gradient image. The improved image has increased sharpness with reduced noise.