公开(公告)号：US11100684B2

公开(公告)日：2021-08-24

申请号：US16509408

申请日：2019-07-11

Applicant: Canon Medical Systems Corporation

Inventor： Ilmar Hein ,  Zhou Yu ,  Ting Xia

IPC: G06K9/00 ,  G06T11/00 ,  G06T7/00

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g., convolutional neural network can be trained to perform the image processing).

公开(公告)号：US12205199B2

公开(公告)日：2025-01-21

申请号：US17577689

申请日：2022-01-18

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Masakazu Matsuura ,  Yujie Lu ,  Jian Zhou ,  Zhou Yu ,  Liang Cai ,  Ting Xia

IPC: G06T11/00 ,  A61B6/00 ,  A61B6/03 ,  A61B6/58

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

公开(公告)号：US12016718B2

公开(公告)日：2024-06-25

申请号：US17692697

申请日：2022-03-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Ting Xia ,  Liang Cai ,  Jian Zhou ,  Zhou Yu

IPC: A61B6/00 ,  A61B6/02 ,  A61B6/03 ,  G06T11/00

CPC classification number: A61B6/544 ,  A61B6/5258 ,  G06T11/008 ,  A61B6/025 ,  A61B6/032 ,  G06T2200/04 ,  G06T2210/41

Abstract: A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.

公开(公告)号：US11908568B2

公开(公告)日：2024-02-20

申请号：US17077413

申请日：2020-10-22

Applicant: CANON MEDICAL SYSTEMS CORPORATION ,  University Health Network

Inventor： Ting Xia ,  Zhou Yu ,  Patrik Rogalla ,  Bernice Hoppel

IPC: G06T7/00 ,  G16H30/20 ,  G06N3/04 ,  G16H50/30 ,  G16H50/20

CPC classification number: G16H30/20 ,  G06N3/04 ,  G06T7/0014 ,  G16H50/20 ,  G16H50/30 ,  G06T2207/10028 ,  G06T2207/10081 ,  G06T2207/30168

Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing. based on the evaluating, scan acquisition parameters and image reconstruction parameters, and generating, optimal imaging protocol parameters, wherein the optimal imaging protocol parameters maximize image quality while minimizing radiation exposure.