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公开(公告)号：US11096633B1
公开(公告)日：2021-08-24
申请号：US16885038
申请日：2020-05-27
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Wenyuan Qi , Yi Qiang , Evren Asma , Jeffrey Kolthammer
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.
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公开(公告)号：US20200170605A1
公开(公告)日：2020-06-04
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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公开(公告)号：US12073538B2
公开(公告)日：2024-08-27
申请号：US17225672
申请日：2021-04-08
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung Chan , Li Yang , Wenyuan Qi , Evren Asma , Jeffrey Kolthammer , Yi Qiang
CPC classification number: G06T5/70 , G06N3/084 , G06T7/0012 , G06T2207/20081 , G06T2207/20084
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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公开(公告)号：US11961209B2
公开(公告)日：2024-04-16
申请号：US17013104
申请日：2020-09-04
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Chung Chan , Jian Zhou , Evren Asma
CPC classification number: G06T5/002 , G06N3/08 , G06T2207/10104 , G06T2207/20081 , G06T2207/20084
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.
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公开(公告)号：US12178631B2
公开(公告)日：2024-12-31
申请号：US18371486
申请日：2023-09-22
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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Patent Agency Ranking
公开(公告)号：US11759162B2
公开(公告)日：2023-09-19
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
CPC classification number: A61B6/5282 , A61B6/032 , A61B6/037 , A61B6/5205
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：US11276209B2
公开(公告)日：2022-03-15
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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8.发明授权公开(公告)号：US10310098B1
公开(公告)日：2019-06-04
申请号：US16153486
申请日：2018-10-05
Applicant: CANON MEDICAL SYSTEMS CORPORATION
Inventor： Yi Qiang , Xiaoli Li , Evren Asma
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.
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公开(公告)号：US11982779B2
公开(公告)日：2024-05-14
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
CPC classification number: G01T1/2907 , G01T1/202 , G01T1/2985
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：US11801029B2
公开(公告)日：2023-10-31
申请号：US17554019
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 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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