JP7420834B2 - 運動アーチファクト検出の方法 - Google Patents

運動アーチファクト検出の方法 Download PDF

Info

Publication number
JP7420834B2
JP7420834B2 JP2021569335A JP2021569335A JP7420834B2 JP 7420834 B2 JP7420834 B2 JP 7420834B2 JP 2021569335 A JP2021569335 A JP 2021569335A JP 2021569335 A JP2021569335 A JP 2021569335A JP 7420834 B2 JP7420834 B2 JP 7420834B2
Authority
JP
Japan
Prior art keywords
motion
image
processor
pattern
cnn
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
JP2021569335A
Other languages
English (en)
Japanese (ja)
Other versions
JP2022534031A5 (https=
JP2022534031A (ja
Inventor
ステファン ワイス
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Publication of JP2022534031A publication Critical patent/JP2022534031A/ja
Publication of JP2022534031A5 publication Critical patent/JP2022534031A5/ja
Application granted granted Critical
Publication of JP7420834B2 publication Critical patent/JP7420834B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • 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]

Landscapes

  • 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)
JP2021569335A 2019-05-28 2020-05-25 運動アーチファクト検出の方法 Active JP7420834B2 (ja)

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 (3)

Publication Number Publication Date
JP2022534031A JP2022534031A (ja) 2022-07-27
JP2022534031A5 JP2022534031A5 (https=) 2023-05-24
JP7420834B2 true JP7420834B2 (ja) 2024-01-23

Family

ID=66668764

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2021569335A Active JP7420834B2 (ja) 2019-05-28 2020-05-25 運動アーチファクト検出の方法

Country Status (5)

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

Families Citing this family (3)

* Cited by examiner, † Cited by third party
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

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060074604A1 (en) 2004-09-24 2006-04-06 International Business Machines (Ibm) Corporation Identifying a state of a system using an artificial neural network generated model
JP2017157138A (ja) 2016-03-04 2017-09-07 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
JP2018503152A (ja) 2015-04-02 2018-02-01 ▲騰▼▲訊▼科技（深▲セン▼）有限公司 コンボリューションニューラルネットワークモデルの訓練方法及び装置

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
US10074037B2 (en) * 2016-06-03 2018-09-11 Siemens Healthcare Gmbh System and method for determining optimal operating parameters for medical imaging
CN108022215B (zh) * 2016-11-02 2020-05-15 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
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
CN107507148B (zh) * 2017-08-30 2018-12-18 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
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

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060074604A1 (en) 2004-09-24 2006-04-06 International Business Machines (Ibm) Corporation Identifying a state of a system using an artificial neural network generated model
JP2018503152A (ja) 2015-04-02 2018-02-01 ▲騰▼▲訊▼科技（深▲セン▼）有限公司 コンボリューションニューラルネットワークモデルの訓練方法及び装置
JP2017157138A (ja) 2016-03-04 2017-09-07 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

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Daiki Tamada, 外4名，"Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver"，Magnetic Resonance in Medical Science，第19巻, 第1号，Japn Society for magnetic Resonance iｎ Medicin (JSMRM)，2019年04月26日，p.64-76
G.A. Wright，"Magnetic resonance imaging"，IEEE Signal Processing Magazine，IEEE，1997年01月，第14巻, 第1号，p.56-66
工藤博幸, 外1名，"ＭＲＩにおける静磁場不均一性と被写体の動きの推定"，電子情報通信学会論文誌，日本，社団法人電子情報通信学会，1990年12月25日，第J73-D-II巻, 第12号，p.2039-2046

Also Published As

Publication number Publication date
CN113892149B (zh) 2025-11-14
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
EP3745153A1 (en) 2020-12-02

Similar Documents

Publication Publication Date Title
JP7757314B2 (ja) 複数の磁気共鳴イメージングシステム構成を使用した磁気共鳴画像の補正
US11249160B2 (en) Image segmentation using reference gray scale values
CN111356930A (zh) 人工智能实现的解剖界标定位
CN115943318B (zh) 用于改进的磁共振图像的采集和/或重建的b0不均匀性的估计
JP7420834B2 (ja) 運動アーチファクト検出の方法
US20220248973A1 (en) Deep learning of eletrical properties tomography
CN113711076B (zh) 狄克逊磁共振成像中的水-脂肪交换的自动检测
CN118103721A (zh) 使用低分辨磁共振图像的运动校正
EP3769103B1 (en) Self-navigation in three-dimensional magnetic resonance imaging
JP2022527509A (ja) シミュレーション磁気共鳴画像を使用した磁気共鳴画像の補正
CN115552273A (zh) 基于机器学习的运动损坏数据检测
US20220301687A1 (en) Uncertainty maps for deep learning eletrical properties tomography
EP4306983A1 (en) Making anatomical measurements using magnetic resonance imaging
WO2026077762A1 (en) Subject position determination using an array of spatially distributed pressure sensors

Legal Events

Date Code Title Description
A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20230516

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20230516

A871 Explanation of circumstances concerning accelerated examination

Free format text: JAPANESE INTERMEDIATE CODE: A871

Effective date: 20230516

A977 Report on retrieval

Free format text: JAPANESE INTERMEDIATE CODE: A971007

Effective date: 20230913

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20230926

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20231124

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20231214

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20240111

R150 Certificate of patent or registration of utility model

Ref document number: 7420834

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150