-
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.
-
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.
-
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.
-
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.
-
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.
-
Patent Agency Ranking
公开(公告)号：US20190236763A1
公开(公告)日：2019-08-01
申请号：US15884089
申请日：2018-01-30
Applicant: Canon Medical Systems Corporation
Inventor： Chung Chan , Zhou Yu , Jian Zhou
CPC classification number: G06T5/50 , A61B6/032 , A61B6/463 , A61B6/5235 , A61B6/5258 , G06T5/002 , G06T11/006 , G06T11/008 , G06T2207/10081 , G06T2207/20216 , G06T2207/30016 , G06T2207/30061 , G06T2210/41 , G06T2211/424
Abstract: A method and apparatus is provided generate a display image that optimize a tradeoff between resolution and noise by using blending weights/ratio based on the content/context of the image. The blending weights control the relative weights when combining multiple computed tomography (CT) images having different degrees of smoothing/denoising to generate a display image having the optimal tradeoff lying within the continuum between/among the CT images. The blending weights are automated based on information indicating the content/context of the display image (e.g., the segmented tissue type, average attenuation, and the display setting such as window width and window level). Thus, indicia indicating content/context of the image determine the weighting coefficients, which are used in a weighted sum, e.g., to combine the plurality of images with different noise/smoothing parameters into a single blended image, which is displayed.
-
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.
-
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.
-
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.
-
10.发明授权公开(公告)号：US10685461B1
公开(公告)日：2020-06-16
申请号：US16228512
申请日：2018-12-20
Inventor： Chung Chan , Zhou Yu , Jian Zhou , Patrik Rogalla , Bernice Hoppel , Kurt Walter Schultz
Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a spatially-varying content-oriented regularization parameter, thereby achieving uniform statistical properties within respective organs/regions and different statistical properties (e.g., degree of smoothing and noise level) among the respective organs/regions. For example, less smoothing and sharper features/resolution can be applied within a lung region than within a soft-tissue region by using a smaller regularization parameter value in the lung region than in the soft-tissue region. This can be achieved, e.g., using a minimum intensity projection to suppress/eliminate sub-solid nodules in the lung region. The content-oriented regularization parameter can be generated by reconstructing an initial CT image, which is then segmented/classified according to organs and/or tissue type. Segmenting the image and generating the content-oriented regularization parameter can be integrated into one process by applying an HU-to-β mapping to the CT image.
-
-
-
-
-
-
-
-
-
Search Results
11
records
(took 0.018 seconds)
