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公开(公告)号：US11250599B2
公开(公告)日：2022-02-15
申请号：US16857916
申请日：2020-04-24
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Li Yang , Wenyuan Qi , Evren Asma
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.
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公开(公告)号：US11234666B2
公开(公告)日：2022-02-01
申请号：US16258396
申请日：2019-01-25
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung Chan , Jian Zhou , Evren Asma
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.
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13.发明授权公开(公告)号：US12159330B2
公开(公告)日：2024-12-03
申请号：US17674063
申请日：2022-02-17
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Qiang Yi , Evren Asma , Li Yang , Peng Peng
Abstract: Upon receiving list-mode data by detecting radiation emitted from a radiation source positioned with a field of view of a medical imaging scanner, each photon included in the list-mode data can be classified according to one or more interaction properties, such as energy or number of crystals interacted with. Grouped pairs of photons can be generated based on the classifying, and a corresponding interaction-property-specific correction kernel (e.g., a corresponding interaction-property-specific point spread function correction kernel) can be selected for each group. Corresponding interaction-property-specific correction kernels can then be utilized to generate higher quality images.
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Patent Agency Ranking
公开(公告)号：US11835669B2
公开(公告)日：2023-12-05
申请号：US17557710
申请日：2021-12-21
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Yi Qiang , Evren Asma , Xiaoli Li , Li Yang , Peng Peng , Jeffrey Kolthammer
IPC: G01T1/29
CPC classification number: G01T1/2985
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.
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公开(公告)号：US11786205B2
公开(公告)日：2023-10-17
申请号：US17554032
申请日：2021-12-17
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung Chan , Jian Zhou , Evren Asma
CPC classification number: A61B6/5258 , A61B6/032 , A61B6/037 , G06N3/08 , G06T7/0012 , G06T2207/10004 , G06T2207/20081 , G06T2207/20084
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.
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公开(公告)号：US11249206B2
公开(公告)日：2022-02-15
申请号：US16866993
申请日：2020-05-05
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Yi Qiang , Evren Asma , Xiaoli Li , Li Yang , Peng Peng , Jeffrey Kolthammer
IPC: G01T1/29
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.
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17.发明授权公开(公告)号：US11049294B2
公开(公告)日：2021-06-29
申请号：US16149439
申请日：2018-10-02
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Li Yang , Wenyuan Qi , Chung Chan , Evren Asma
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 β that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter β can depend on a sensitivity parameter. The edge-preserving parameter β can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.
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公开(公告)号：US10743830B2
公开(公告)日：2020-08-18
申请号：US16209551
申请日：2018-12-04
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Chung Chan , Li Yang , Evren Asma
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.
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