Abstract: A method, apparatus, and computer instructions stored on a computer-readable medium perform latent image feature extraction by performing the functions of receiving image data acquired during an imaging of a patient, wherein the image data includes motion by the patient during the imaging; segmenting the image data to include M image data segments corresponding to at least N motion phases having shorter durations than a duration of the motion by the patient during the imaging, wherein M is a positive integer greater than or equal to a positive integer N; producing, from the M image data segments, at least N latent feature vectors corresponding to the motion by the patient during the imaging; and performing a gated reconstruction of the N motion phases by reconstructing the image data based on the at least N latent feature vectors.

Abstract: A method, apparatus, and non-transitory computer-readable storage medium for image denoising whereby a deep image prior (DIP) neural network is trained to produce a denoised image by inputting the second medical image to the DIP neural network and combining a converging noise and an output of the DIP network during the training such that the converging noise combined with the output of the DIP network approximates the first medical image at the end of the training, wherein the output of the DIP network represents the denoised image.

Abstract: A method, system, and computer readable medium to perform nuclear medicine scatter correction estimation, sinogram estimation and image reconstruction from emission and attenuation correction data using deep convolutional neural networks. In one embodiment, a Deep Convolutional Neural network (DCNN) is used, although multiple neural networks can be used (e.g., for angle-specific processing). In one embodiment, a scatter sinogram is directly estimated using a DCNN from emission and attenuation correction data. In another embodiment a DCNN is used to estimate a scatter-corrected image and then the scatter sinogram is computed by a forward projection.

Abstract: The disclosed embodiments relate to a system that performs ultra-fast tracer imaging on a subject using positron emission tomography. During operation, the system performs a high-temporal-resolution, total-body dynamic PET scan on the subject as an intravenously injected radioactive tracer propagates through the vascular system of the subject to produce PET projection data. Next, the system applies an image reconstruction technique to the PET projection data to produce subsecond temporal frames, which illustrate the dynamic propagation of the radioactive tracer through the vascular system of the subject. Finally, the system outputs the temporal frames through a display device.

Abstract: To reduce the effect(s) caused by patient breathing and movement during PET data acquisition, an unsupervised non-rigid image registration framework using deep learning is used to produce motion vectors for motion correction. In one embodiment, a differentiable spatial transformer layer is used to warp the moving image to the fixed image and use a stacked structure for deformation field refinement. Estimated deformation fields can be incorporated into an iterative image reconstruction process to perform motion compensated PET image reconstruction. The described method and system, using simulation and clinical data, provide reduced error compared to at least one iterative image registration process.

Abstract: First and second substantially independent identically distributed half scans are obtained; the first substantially independent identically distributed half scan is used as training data to train a machine learning-based system, and the second substantially independent identically distributed half scan is used as label data to train a machine learning-based system. This produces a trained machine learning-based system.

Abstract: A method of calculating a system matrix for time-of-flight (TOF) list-mode reconstruction of positron-emission tomography (PET) images, the method including determining a TOF geometric projection matrix G including effects of object attenuation; estimating an image-blurring matrix R in image space; obtaining a diagonal matrix D that includes TOF-based normalization factor; and calculating the system matrix H as H=DGR.

Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a hybrid pre-log and post-log iterative reconstruction method. A pre-log formulation is applied to values of the projection data that are less than a threshold (e.g., X-ray intensities corresponding to high absorption trajectories). The pre-log formulation has better noise modeling and better image quality for reconstructed images, but is slow to converge. Projection data values above the threshold are processed using a post-log formulation, which has fast convergence but poorer noise handling. However, the poorer noise handling has little effect on high value projection data. Thus, the hybrid pre-log and post-log method provides improved image quality by more accurately modeling the noise of low count projection data, without sacrificing the fast convergence of the post-log method, which is applied to high-count projection data.

Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a hybrid pre-log and post-log iterative reconstruction method. A pre-log formulation is applied to values of the projection data that are less than a threshold (e.g., X-ray intensities corresponding to high absorption trajectories). The pre-log formulation has better noise modeling and better image quality for reconstructed images, but is slow to converge. Projection data values above the threshold are processed using a post-log formulation, which has fast convergence but poorer noise handling. However, the poorer noise handling has little effect on high value projection data. Thus, the hybrid pre-log and post-log method provides improved image quality by more accurately modeling the noise of low count projection data, without sacrificing the fast convergence of the post-log method, which is applied to high-count projection data.

Abstract: Systems and methods for a positron emission tomography (PET) kit are described. A PET detector kit may include a gantry, a plurality of PET detector modules, and an event processing device. A PET detector module may include a housing, a crystal, a light detector, and a communication component. The housing may include at least one connective element configured to removably and adjustably couple the PET detector module to the gantry. The crystal may be located within the housing. The light detector may be configured to detect light emitted by the crystal. The communication component may be configured to communicate data from the at least one light detector to an event processing device. The event processing device may receive data from the plurality of PET detector modules and may cause the one or more processors to determine coincidence events based on the received data.

Abstract: A method of calculating a system matrix for time-of-flight (TOF) list-mode reconstruction of positron-emission tomography (PET) images, the method including determining a TOF geometric projection matrix G including effects of object attenuation; estimating an image-blurring matrix R in image space; obtaining a diagonal matrix D that includes TOF-based normalization factor; and calculating the system matrix H as H=DGR.

Abstract: Systems and methods for a positron emission tomography (PET) kit are described. A PET detector kit may include a gantry, a plurality of PET detector modules, and an event processing device. A PET detector module may include a housing, a crystal, a light detector, and a communication component. The housing may include at least one connective element configured to removably and adjustably couple the PET detector module to the gantry. The crystal may be located within the housing. The light detector may be configured to detect light emitted by the crystal. The communication component may be configured to communicate data from the at least one light detector to an event processing device. The event processing device may receive data from the plurality of PET detector modules and may cause the one or more processors to determine coincidence events based on the received data.