CN113892149B - 用于运动伪影检测的方法 - Google Patents

用于运动伪影检测的方法

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Publication number
CN113892149B
CN113892149B CN202080039752.2A CN202080039752A CN113892149B CN 113892149 B CN113892149 B CN 113892149B CN 202080039752 A CN202080039752 A CN 202080039752A CN 113892149 B CN113892149 B CN 113892149B
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motion
image
space
cnn
training
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CN113892149A (zh
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S·魏斯
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Koninklijke Philips NV
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Koninklijke Philips NV
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/565—Correction of image distortions, e.g. due to magnetic field inhomogeneities
    • G01R33/56509—Correction of image distortions, e.g. due to magnetic field inhomogeneities due to motion, displacement or flow, e.g. gradient moment nulling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/60—Image enhancement or restoration using machine learning, e.g. neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Artificial Intelligence (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Public Health (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Mathematical Physics (AREA)
  • Signal Processing (AREA)
  • Evolutionary Computation (AREA)
  • Veterinary Medicine (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physiology (AREA)
  • Psychiatry (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • Fuzzy Systems (AREA)
  • Databases & Information Systems (AREA)
  • Quality & Reliability (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Image Analysis (AREA)
CN202080039752.2A 2019-05-28 2020-05-25 用于运动伪影检测的方法 Active CN113892149B (zh)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
EP19176878.7 2019-05-28
EP19176878.7A EP3745153A1 (en) 2019-05-28 2019-05-28 A method for motion artifact detection
PCT/EP2020/064376 WO2020239661A1 (en) 2019-05-28 2020-05-25 A method for motion artifact detection

Publications (2)

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CN113892149A CN113892149A (zh) 2022-01-04
CN113892149B true CN113892149B (zh) 2025-11-14

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US (1) US11995825B2 (https=)
EP (2) EP3745153A1 (https=)
JP (1) JP7420834B2 (https=)
CN (1) CN113892149B (https=)
WO (1) WO2020239661A1 (https=)

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Publication number Priority date Publication date Assignee Title
US12450752B2 (en) * 2021-11-29 2025-10-21 Canon Medical Systems Corporation Motion correction of images corrupted by multiple motion sources
CN115343623B (zh) * 2022-08-31 2023-06-16 中国长江三峡集团有限公司 一种电化学储能电池故障的在线检测方法及装置
CN118447123B (zh) * 2024-07-08 2024-09-13 南昌睿度医疗科技有限公司 一种核磁共振图像伪影去除方法及系统

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CN107507148A (zh) * 2017-08-30 2017-12-22 南方医科大学 基于卷积神经网络去除磁共振图像降采样伪影的方法
CN108022215A (zh) * 2016-11-02 2018-05-11 奥泰医疗系统有限责任公司 基于数据一致性和图像伪影分解技术的运动伪影消除方法

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US9811924B2 (en) * 2011-04-19 2017-11-07 University Of Virginia Patent Foundation Interferometric techniques for magnetic resonance imaging
US8724881B2 (en) * 2011-11-09 2014-05-13 Siemens Aktiengesellschaft Method and system for precise segmentation of the left atrium in C-arm computed tomography volumes
US10092199B2 (en) * 2014-04-01 2018-10-09 Siemens Healthcare Gmbh MR imaging apparatus and method for generating a perfusion image with motion correction
CN106156807B (zh) 2015-04-02 2020-06-02 华中科技大学 卷积神经网络模型的训练方法及装置
CN104749538B (zh) * 2015-04-30 2016-02-03 郑州轻工业学院 一种并行磁共振成像相位处理方法
US10429477B2 (en) * 2015-08-21 2019-10-01 Shanghai United Imaging Healthcare Co., Ltd. System and method for flip angle determination in magnetic resonance imaging
JP6873600B2 (ja) 2016-03-04 2021-05-19 キヤノン株式会社 画像認識装置、画像認識方法及びプログラム
US10074037B2 (en) * 2016-06-03 2018-09-11 Siemens Healthcare Gmbh System and method for determining optimal operating parameters for medical imaging
CN110226100B (zh) * 2017-01-25 2022-03-15 上海联影医疗科技股份有限公司 用于磁共振成像的系统和方法
CN111684492B (zh) * 2017-06-26 2024-03-15 医科达有限公司 使用深度卷积神经网络来改善锥形束ct图像质量的方法
JP6772112B2 (ja) * 2017-07-31 2020-10-21 株式会社日立製作所 医用撮像装置及び医用画像処理方法
EP3477583A1 (en) * 2017-10-31 2019-05-01 Koninklijke Philips N.V. Deep-learning based processing of motion artifacts in magnetic resonance imaging data
US10698063B2 (en) * 2017-11-01 2020-06-30 Siemens Healthcare Gmbh Motion artifact reduction of magnetic resonance images with an adversarial trained network
CN109801259A (zh) * 2018-12-18 2019-05-24 中国科学院深圳先进技术研究院 一种核磁共振图像的快速成像方法、装置及设备

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CN108022215A (zh) * 2016-11-02 2018-05-11 奥泰医疗系统有限责任公司 基于数据一致性和图像伪影分解技术的运动伪影消除方法
CN107507148A (zh) * 2017-08-30 2017-12-22 南方医科大学 基于卷积神经网络去除磁共振图像降采样伪影的方法

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Publication number Publication date
WO2020239661A1 (en) 2020-12-03
US11995825B2 (en) 2024-05-28
US20220215540A1 (en) 2022-07-07
EP3977150A1 (en) 2022-04-06
JP2022534031A (ja) 2022-07-27
CN113892149A (zh) 2022-01-04
JP7420834B2 (ja) 2024-01-23
EP3745153A1 (en) 2020-12-02

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