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公开(公告)号：US20240037814A1
公开(公告)日：2024-02-01
申请号：US17878413
申请日：2022-08-01
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung CHAN , Li YANG , Xiaoli LI , Wenyuan QI , Evren ASMA , Jeffrey KOLTHAMMER
IPC: G06T11/00
CPC classification number: G06T11/005 , G06T2211/412
Abstract: A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.
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2.发明公开公开(公告)号：US20230237638A1
公开(公告)日：2023-07-27
申请号：US17961365
申请日：2022-10-06
Inventor： Tiantian LI , Zhaoheng XIE , Wenyuan QI , Li YANG , Evren ASMA , Jinyi QI
CPC classification number: G06T7/0004 , G06T5/002 , G06T2207/10104 , G06T2207/10088 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084
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.
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公开(公告)号：US20220327665A1
公开(公告)日：2022-10-13
申请号：US17225672
申请日：2021-04-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung CHAN , Li YANG , Wenyuan Ql , Evren ASMA , Jeffrey KOLTHAMMER , Yi QIANG
Abstract: Existing, low quality images can be restored using reconstruction or a combination of post-reconstruction techniques to generate a real patient phantom. The real patient phantom (RPP) can then be simulated in Monte Carlo simulations of a higher performance system and a lower performance system. Alternatively, the RPP can be simulated in the higher performance system, and a real scan can be performed by an existing, lower performance system. The higher performance system can be differentiated from the lower performance system in a variety of ways, including a higher resolution time of flight measurement capability, a greater sensitivity, smaller detector crystals, or less scattering. A neural network can be trained using the images produce by the higher performance system as the target, and the images produced by the lower performance system as the input. After training, the trained neural network can be used to output input images taken in a lower performance system with higher performance system characteristics.
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4.发明公开公开(公告)号：US20240233211A1
公开(公告)日：2024-07-11
申请号：US18336741
申请日：2023-06-16
Inventor： Wenyuan QI , Li YANG , Jeffrey KOLTHAMMER , Evren ASMA , Jinyi QI , Tiantian LI
CPC classification number: G06T11/005 , A61B6/037 , A61B6/5288 , G06T7/20 , G06T2207/10104
Abstract: A method for signal separation includes obtaining list mode data representing radiation detected during an imaging scan, the list mode data being affected by quasi-periodic motion of an imaging object; dividing the list mode data into first non-overlapping frames of a first frame length, and process the first frames to determine a cardiac cycle length; determining a second frame length, longer than the first frame length, based on the determined cardiac cycle length; re-binning the list mode data into overlapping frames having the second frame length, based on the non-overlapping frames having the first frame length; applying a principal component analysis (PCA) process on the re-binned list mode data having the second frame length to determine a respiratory waveform; determining a cardiac waveform using the determined respiratory waveform; and reconstructing an image based on the list mode data using the determined respiratory waveform and the determined cardiac waveform.
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公开(公告)号：US20240122558A1
公开(公告)日：2024-04-18
申请号：US17963737
申请日：2022-10-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Kent C. BURR , Yi QIANG , Evren ASMA , Jeffrey KOLTHAMMER
CPC classification number: A61B6/037 , A61B6/4258 , A61B6/481
Abstract: A PET scanner includes gamma-ray detector rings that form a bore through which an imaging subject is translated, a length of the bore defining an axial length of the PET scanner, the gamma-ray detector rings being movable along the axial length, the gamma-ray detector rings including gamma-ray detector modules therein, and processing circuitry configured to receive PET data associated with a plurality of transaxial slices of the imaging subject, the PET data including a first set of spatial information and timing information corresponding to a first data acquisition period for the gamma-ray detector modules in a first axial position and a second set of spatial information and timing information corresponding to a second data acquisition period for the gamma-ray detector modules in a second axial position, and reconstruct a PET image based on the first set of spatial and timing information and the second set of spatial and timing information.
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Patent Agency Ranking
公开(公告)号：US20220110600A1
公开(公告)日：2022-04-14
申请号：US17554019
申请日：2021-12-17
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 produce uniform 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：US20230351646A1
公开(公告)日：2023-11-02
申请号：US17965289
申请日：2022-10-13
Inventor： Jinyi QI , Tiantian LI , Zhaoheng XIE , Wenyuan QI , Li YANG , Chung CHAN , Evren ASMA
CPC classification number: G06T11/005 , G06V10/44 , G06V10/7635 , G06V10/82 , G06V20/49 , G06T7/20 , G06T2207/10104 , G06T2207/20081
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.
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公开(公告)号：US20230206516A1
公开(公告)日：2023-06-29
申请号：US17682738
申请日：2022-02-28
Inventor： Jinyi QI , Tiantian LI , Zhaoheng XIE , Wenyuan QI , Li YANG , Chung CHAN , Evren ASMA
CPC classification number: G06T11/005 , G06T3/40 , G06T7/0014 , A61B6/5282 , A61B6/027 , A61B6/032 , A61B6/037 , G06T2207/20081 , G06T2207/20084 , G06T2207/10104 , G06T2207/10081 , G06T2207/30004 , G06T2210/41 , G06T2211/40
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.
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公开(公告)号：US20210335023A1
公开(公告)日：2021-10-28
申请号：US16860425
申请日：2020-04-28
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yujie LU , Evren ASMA , Yi QIANG , Jeffrey KOLTHAMMER , Zhou YU
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.
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公开(公告)号：US20210335022A1
公开(公告)日：2021-10-28
申请号：US16857916
申请日：2020-04-24
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Li YANG , Wenyuan QI , Evren ASMA
IPC: G06T11/00
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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