Abstract: A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

Abstract: A method of imaging includes obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements, summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produceuniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: A method of imaging includes obtaining a plurality of dynamic sinograms, each dynamic sinogram representing detection events of gamma rays at a plurality of detector elements, summing the plurality of dynamic sinograms to generate an activity map based on a radioactivity level of the gamma rays; reconstructing, using the plurality of dynamic sinograms, a plurality of dynamic images, each of the plurality of dynamic images corresponding to one of the each of the plurality of dynamic sinograms, and generating, using the plurality of dynamic sinograms and the activity map, at least one parametric image.

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

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: A positron emission tomography scanner includes a plurality of gamma-ray detector rings that form a bore through which an imaging subject is translated, each of the plurality of gamma-ray detector rings being in a first axial position, and processing circuitry configured to receive attenuation data associated with a plurality of transaxial slices of the imaging subject, determine a second axial position of each of the plurality of gamma-ray detector rings based on the received attenuation data, and adjust a position of each of the plurality of gamma-ray detector rings from the first axial position to the second axial position. The processing circuitry may further be configured to calculate an attenuation metric based on the received attenuation data, and determine the second axial position such that the attenuation metric calculated for each pair of adjacent gamma-ray detector rings is equal.

Abstract: A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

Abstract: A method and apparatus is provided to iteratively reconstruct an image from gamma-ray emission data by optimizing an objective function with a spatially-varying regularization term. The image is reconstructed using a regularization term that varies spatially based on an activity-level map to spatially vary the regularization term in the objective function. For example, more smoothing (or less edge-preserving) can be imposed where the activity is lower. The activity-level map can be used to calculate a spatially-varying smoothing parameter and/or spatially-varying edge-preserving parameter. The smoothing parameter can be a regularization parameter ? that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter ? can depend on a sensitivity parameter. The edge-preserving parameter ? can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.

Abstract: A system and method for training a neural network to denoise images. One noise realization is paired to an ensemble of training-ready noise realizations, and fed into a neural network for training. Training datasets can also be retrospectively generated based on existing patient studies to increase the number of training datasets.

Abstract: A method and apparatus is provided to correct for scatter in a positron emission tomography (PET) scanner, the scatter coming from both within and without a field of view (FOV) for true coincidences. For a region of interest (ROI), the outside-the-FOV scatter correction are based on attenuation maps and activity distributions estimated from short PET scans of extended regions adjacent to the ROI. Further, in a PET/CT scanner, these short PET scans can be accompanied by low-dose X-ray computed tomography (CT) scans in the extended regions. The use of short PET scans, rather than full PET scans, provides sufficient accuracy for outside-the-FOV scatter corrections with the advantages of a lower radiation dose (e.g., low-dose CT) and requiring less time. In the absence of low-dose CT scans, an atlas of attenuation maps or a joint-estimation method can be used to estimate the attenuation maps for the extended regions.

Abstract: A method and apparatus is provided to correct for scatter in a positron emission tomography (PET) scanner, the scatter coming from both within and without a field of view (FOV) for true coincidences. For a region of interest (ROI), the outside-the-FOV scatter correction are based on attenuation maps and activity distributions estimated from short PET scans of extended regions adjacent to the ROI. Further, in a PET/CT scanner, these short PET scans can be accompanied by low-dose X-ray computed tomography (CT) scans in the extended regions. The use of short PET scans, rather than full PET scans, provides sufficient accuracy for outside-the-FOV scatter corrections with the advantages of a lower radiation dose (e.g., low-dose CT) and requiring less time. In the absence of low-dose CT scans, an atlas of attenuation maps or a joint-estimation method can be used to estimate the attenuation maps for the extended regions.

Abstract: A method and apparatus is provided to iteratively reconstruct an image from gamma-ray emission data by optimizing an objective function with a spatially-varying regularization term. The image is reconstructed using regularization term that varies spatially based on an activity-level map to spatially vary the regularization term in the objective function. For example, more smoothing (or less edge-preserving) can be imposed where the activity is lower. The activity-level map can be used to calculate a spatially-varying smoothing parameter and/or spatially-varying edge-preserving parameter. The smoothing parameter can be a regularization parameter ? that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter ? can depend on a sensitivity parameter. The edge-preserving parameter ? can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produceuniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: An embodiment provides a medical image processing apparatus that comprises circuitry. The circuitry obtains detection data representing detection events of radiation at a plurality of detector elements. The circuitry reconstructs an image by iteratively using an optimization-transfer algorithm to the detection data. The optimization-transfer algorithm uses a quadratic surrogate function that includes a curvature. The curvature is calculated using an inverse-background image.

Abstract: A method and apparatus are provided for positron emission imaging to correct a position at which a gamma ray was detected, when the gamma ray is scattered during detection. When Compton scattering occurs during detection of a gamma ray, the energy of the gamma ray deposited in multiple crystals in an array of detector elements. The corrected position is determined as a weighted sum of the position of the multiple crystals, each weighted by an inverse of the energy measured at the respective crystal. Further, the inverse-energy weight can be raised to a power p. A minimum energy threshold can be applied to determine the multiple crystals at which the gamma ray energy is deposited. The corrected position can be a floating position or can be rounded to a nearest crystal or to a nearest virtual sub-crystal.

Abstract: An embodiment provides a medical image processing apparatus that comprises circuitry. The circuitry obtains detection data representing detection events of radiation at a plurality of detector elements. The circuitry reconstructs an image by iteratively using an optimization-transfer algorithm to the detection data. The optimization-transfer algorithm uses a quadratic surrogate function that includes a curvature. The curvature is calculated using an inverse-background image.

Abstract: A method and apparatus is provided to iteratively reconstruct a PET image for emission data using separable quadratic surrogates (SQS). The quadratic surrogates include a Poisson likelihood surrogate that has a curvature that depends on a back-projection of an inverse of mean-background signal. The method can be used with Nesterov acceleration and ordered subsets to achieve quadratic convergence to an image minimizing a Poisson Likelihood objective function that includes a regularizer that penalizes roughness in the reconstructed image.

Abstract: A method and apparatus is provided to iteratively reconstruct a PET image for emission data using separable quadratic surrogates (SQS). The quadratic surrogates include a Poisson likelihood surrogate that has a curvature that depends on a back-projection of an inverse of mean-background signal. The method can be used with Nesterov acceleration and ordered subsets to achieve quadratic convergence to an image minimizing a Poisson Likelihood objective function that includes a regularizer that penalizes roughness in the reconstructed image.

Abstract: An apparatus for performing a non-local means (NLM) filter is described. The pixel of the NLM-filtered image are weighted averages of pixels from a noisy image, where the weights are a measure of the similarity between patches of the noisy image. The similarity weights can be calculated using a Kullback-Leibler or a Euclidean distance measure. The similarity weights can be based on filtered patches of the noisy image. The similarity weights can be based on a similarity measure between patches of an anatomical image corresponding to the noisy image. The similarity weights can be calculated using a time series of noisy images to increase the statistical sample size of the patches. The similarity weights can be calculated using a weighted sum of channel similarity weights calculated between patches of noisy image that have been band-pass filtered. The NLM-filtered image can also be blended with a non-NLM-filtered image.