Abstract: Data acquired from a scan of an object can be decomposed into frequency components. The frequency components can be input into a trained model to obtain processed frequency components. These processed frequency components can be composed and used to generate a final image. The trained model can be trained, independently or dependently, using frequency components covering the same frequencies as the to-be-processed frequency components. In addition, organ specific processing can be enabled by training the trained model using image and/or projection datasets of the specific organ.

Abstract: An apparatus, method, and computer-readable medium for improving image quality of a medical volume.

Abstract: A method for motion estimation in CT systems is provided. The method includes dividing projection data, obtained by scanning a heart using the CT system, into a plurality of partial-angle-reconstruction (PAR) bins, reconstructing a plurality of PAR volumes from the PAR-binned projection data, obtaining, based on the plurality of reconstructed PAR volumes, a number of short-scan volumes, determining, based on the obtained number of short-scan volumes, a plurality of nodes throughout the heart, estimating, for each of the determined plurality of nodes, a plurality of model parameters of a motion model, and generating, based on the plurality of model parameters estimated for each of the plurality of nodes, parameters of a global motion model at each voxel of a volume of the heart. The method also includes reconstructing, based on the generated motion parameters of the global motion model at each voxel of the volume of the heart, a motion-compensated short-scan volume.

Abstract: An apparatus is provided with processing circuitry that receives a phase image acquired at a corresponding cardiac phase, determines, from the received phase image, a mask image of a particular cardiac region, applies both the determined mask image and the phase image to inputs of a trained neural network model to obtain, from outputs of the neural network model, a location probability map. The neural network model is trained with a set of input data and a corresponding set of output data. The input data includes a training mask image and a training phase image, and the output data includes a training location probability map. The processing circuitry calculates, for the cardiac phase, from the determined location probability map output from the trained neural network model, a value of a cardiac motion metric. The determined location probability map specifies a probable location of a cardiac vessel.

Abstract: An information processing method controls a CT scanner such that the method includes, but is not limited to, determining an X-ray irradiation period from an electrocardiogram acquired from an electrocardiography device attached to a living object to be imaged, by processing the electrocardiogram at multiple different cardiac phases; performing, by controlling a CT gantry including and rotatably supporting an X-ray source and an X-ray detector, a diagnostic CT scan in the determined X-ray irradiation period, of at least a part of the heart region, to obtain a CT image; and causing a display unit to display the obtained CT image. The method can be performed at least by an information processing apparatus including processing circuitry and/or computer instructions stored in a non-transitory computer readable storage medium for performing the method.

Abstract: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

Abstract: Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction (PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

Abstract: A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.

Abstract: Data acquired from a scan of an object can be decomposed into frequency components. The frequency components can be input into a trained model to obtain processed frequency components. These processed frequency components can be composed and used to generate a final image. The trained model can be trained, independently or dependently, using frequency components covering the same frequencies as the to-be-processed frequency components. In addition, organ specific processing can be enabled by training the trained model using image and/or projection datasets of the specific organ.

Abstract: An apparatus, method, and computer-readable medium for improving image quality of a medical volume.

Abstract: Devices, systems, and methods receive scan data that were generated by scanning a region of a subject with a computed tomography apparatus; generate multiple partial angle reconstruction(PAR) images based on the scan data; obtain corresponding characteristics of the multiple PAR images; perform correspondence mapping on the multiple PAR images based on the obtained corresponding characteristics and on the multiple PAR images, wherein the correspondence mapping generates correspondence-mapping data; and generate a motion-corrected reconstruction image based on the correspondence-mapping data and on one or both of the scan data and the PAR images.

Abstract: Devices, systems, and methods for generating a medical image obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

Abstract: A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

Abstract: An apparatus and method are provided for computed tomography (CT) imaging to reduce truncation artifacts due to a part of an imaged object being outside the scanner field of view (FOV) for at least some views of a CT scan. After initial determining extrapolation widths to extend the projection data to fill a truncation region, the extrapolation widths are combined into a padding map and smoothed to improve uniformity and remove jagged edges. Then a hybrid material model fits the measured projection data nearest the truncation region to extrapolate projection data filling the truncation region. Smoothing the padding map is improved by the insight that in general smaller extrapolation widths are more accurate and trustworthy. Further, practical applications often include multiple inhomogeneous materials. Thus, the hybrid material model provides a better approximation than single material models, and more accurate fitting is achieved.

Abstract: A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

Abstract: An apparatus and method of denoising a dynamic image is provided. The dynamic image can represent a time-series of snapshot images. The dynamic image is transformed, using a sparsifying transformation, into an aggregate image and a series of transform-domain images. The transform-domain images represent kinetic information of the dynamic images (i.e., differences between the snapshots), and the aggregate image represents static information (i.e., features and structure common among the snapshots). The transform-domain images, which can be approximated using a sparse approximation method, are denoised. The denoised transform-domain images are recombined with the aggregate image using an inverse sparsifying transformation to generate a denoised dynamic image. The transform-domain images can be denoised using at least one of a principal component analysis method and a K-SVD method.

Abstract: An apparatus and method are provided for computed tomography (CT) imaging to reduce truncation artifacts due to a part of an imaged object being outside the scanner field of view (FOV) for at least some views of a CT scan. After initial determining extrapolation widths to extend the projection data to fill a truncation region, the extrapolation widths are combined into a padding map and smoothed to improve uniformity and remove jagged edges. Then a hybrid material model fits the measured projection data nearest the truncation region to extrapolate projection data filling the truncation region. Smoothing the padding map is improved by the insight that in general smaller extrapolation widths are more accurate and trustworthy. Further, practical applications often include multiple inhomogeneous materials. Thus, the hybrid material model provides a better approximation than single material models, and more accurate fitting is achieved.

Abstract: A method and apparatus is provided to reconstruct a computed tomography image from projection data using windowed filtered back-projection (FBP) and using regularization constraints that can be quadratic or non-quadratic. The method emulates multiple Landweber iterations using a single windowed FBP operation and then iterates between imposing regularization constraints and a single windowed FBP operation. This windowed FBP operation is only performed once. The regularization constraints can be imposed using edge-preserving denoising methods, including, e.g., a Huber filter, a median filter, a bilateral filter, a guided filter, a non-local means filter, a total-variation minimization regularizer, other known regularizer, or an anisotropic diffusion filter. The entire procedure contains no forward projection and contains only one back-projection.

Abstract: A method and apparatus is provided to determine a reconstructed image from computed tomography projection data using iterative reconstruction with an objective function that includes modified weights. The modified weights can include, among other weight values, redundancy weights and statistical weights, which are modified to compress low-frequency components. Additionally, high-frequency components of the statistical weights can be compressed, amplified, or maintained at their current magnitude. The high-frequency components can be subject to a threshold-and-invert step, substituting an inverted value for each high-frequency component above a predefined threshold. Using the modified weights, the reconstructed image can be determined using penalized weighted least squares to minimize the objective function.

Abstract: A method and apparatus is provided to determine a reconstructed image from computed tomography projection data using iterative reconstruction with an objective function that includes modified weights. The modified weights can include, among other weight values, redundancy weights and statistical weights, which are modified to compress low-frequency components. Additionally, high-frequency components of the statistical weights can be compressed, amplified, or maintained at their current magnitude. The high-frequency components can be subject to a threshold-and-invert step, substituting an inverted value for each high-frequency component above a predefined threshold. Using the modified weights, the reconstructed image can be determined using penalized weighted least squares to minimize the objective function.