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11.发明申请公开(公告)号：US20210304457A1
公开(公告)日：2021-09-30
申请号：US17180182
申请日：2021-02-19
Inventor： Jinyi QI , Tiantian LI , Zhaoheng XIE , Wenyuan QI , Li YANG , Chung CHAN , Evren ASMA
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.
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12.发明公开公开(公告)号：US20240225585A1
公开(公告)日：2024-07-11
申请号：US18336807
申请日：2023-06-16
Inventor： Wenyuan QI , Li YANG , Jeffrey KOLTHAMMER , Yu-Jung TSAI , Evren ASMA , Maria IATROU , Jinyi QI , Tiantian LI
Abstract: A method for performing single gating in a positron emission tomography (PET) system includes: receiving list-mode data acquired by scanning an imaging object using the PET system, the list-mode data being affected by quasi-periodic motion of the imaging object; producing a plurality of vectors based on the received list-mode data; generating a reference vector based on the produced plurality of vectors; selecting, from the produced plurality of vectors, a set of vectors corresponding to a single gate, based on respective differences compared with the generated reference vector; and generating an image of the imaging object based on the selected set of vectors.
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公开(公告)号：US20240008832A1
公开(公告)日：2024-01-11
申请号：US18371486
申请日：2023-09-22
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 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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14.发明公开公开(公告)号：US20230260171A1
公开(公告)日：2023-08-17
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
CPC classification number: G06T11/006 , A61B6/037 , A61B6/5282 , G01T1/2985 , G06T11/005 , G06T2211/421
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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公开(公告)号：US20220113437A1
公开(公告)日：2022-04-14
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
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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Patent Agency Ranking
公开(公告)号：US20220104787A1
公开(公告)日：2022-04-07
申请号：US17554032
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 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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17.发明申请公开(公告)号：US20230026719A1
公开(公告)日：2023-01-26
申请号：US17469144
申请日：2021-09-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung CHAN , Junyu CHEN , Evren ASMA , Jeffrey KOLTHAMMER
IPC: G06N3/08 , G06N3/04 , G06V10/771 , G06V10/82
Abstract: A neural network is initially trained to remove errors and is later fine tuned to remove less-effective portions (e.g., kernels) from the initially trained network and replace them with further trained portions (e.g., kernels) trained with data after the initial training.
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公开(公告)号：US20220395246A1
公开(公告)日：2022-12-15
申请号：US17345716
申请日：2021-06-11
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yujie LU , Ryo OKUDA , Evren ASMA , Manabu TESHIGAWARA , Jeffrey KOLTHAMMER
Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.
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公开(公告)号：US20220335664A1
公开(公告)日：2022-10-20
申请号：US17230372
申请日：2021-04-14
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan QI , Yi QIANG , Peng PENG , Evren ASMA , Jeffrey KOLTHAMMER
Abstract: A guided pairing method includes generating a singles list by detecting a plurality of singles at a plurality of detector elements in a detector array, the plurality of singles falling within a plurality of detection windows; for each detection window of the plurality of detection windows in the singles list having exactly two singles of the plurality of singles, determining the line of responses (LORs) for each of the two singles of the plurality of singles; for each detection window of the plurality of detection windows in the singles list having more than two singles of the plurality of singles, determining all coincidences possible based on the more than two singles; generating a weight for said each coincidence of the coincidences based on the determined LORs for said each of the two singles of the plurality of singles; and pairing the more than two singles based on the generated weight for said each coincidence of the coincidences.
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公开(公告)号：US20210208293A1
公开(公告)日：2021-07-08
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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