GB2597218B - Automated detection of water-fat swaps in Dixon magnetic resonance imaging - Google Patents

Automated detection of water-fat swaps in Dixon magnetic resonance imaging Download PDF

Info

Publication number
GB2597218B
GB2597218B GB2116627.7A GB202116627A GB2597218B GB 2597218 B GB2597218 B GB 2597218B GB 202116627 A GB202116627 A GB 202116627A GB 2597218 B GB2597218 B GB 2597218B
Authority
GB
United Kingdom
Prior art keywords
water
magnetic resonance
resonance imaging
automated detection
dixon magnetic
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
GB2116627.7A
Other languages
English (en)
Other versions
GB202116627D0 (en
GB2597218A (en
Inventor
Sommer Karsten
Weiss Steffen
Eggers Holger
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 GB202116627D0 publication Critical patent/GB202116627D0/en
Publication of GB2597218A publication Critical patent/GB2597218A/en
Application granted granted Critical
Publication of GB2597218B publication Critical patent/GB2597218B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • G01R33/4828—Resolving the MR signals of different chemical species, e.g. water-fat imaging
    • 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
    • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
    • 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
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • 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
    • G01R33/56527—Correction of image distortions, e.g. due to magnetic field inhomogeneities due to chemical shift effects
    • 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
    • G01R33/56563—Correction of image distortions, e.g. due to magnetic field inhomogeneities caused by a distortion of the main magnetic field B0, e.g. temporal variation of the magnitude or spatial inhomogeneity of B0

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Signal Processing (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
GB2116627.7A 2019-04-19 2020-04-09 Automated detection of water-fat swaps in Dixon magnetic resonance imaging Active GB2597218B (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP19170373.5A EP3726240A1 (en) 2019-04-19 2019-04-19 Automated detection of water-fat swaps in dixon magnetic resonance imaging
PCT/EP2020/060138 WO2020212244A1 (en) 2019-04-19 2020-04-09 Automated detection of water-fat swaps in dixon magnetic resonance imaging

Publications (3)

Publication Number Publication Date
GB202116627D0 GB202116627D0 (en) 2022-01-05
GB2597218A GB2597218A (en) 2022-01-19
GB2597218B true GB2597218B (en) 2023-07-05

Family

ID=66239969

Family Applications (1)

Application Number Title Priority Date Filing Date
GB2116627.7A Active GB2597218B (en) 2019-04-19 2020-04-09 Automated detection of water-fat swaps in Dixon magnetic resonance imaging

Country Status (7)

Country Link
US (1) US11906608B2 (https=)
EP (1) EP3726240A1 (https=)
JP (1) JP2022529944A (https=)
CN (1) CN113711076B (https=)
DE (1) DE112020002013T5 (https=)
GB (1) GB2597218B (https=)
WO (1) WO2020212244A1 (https=)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3885780A1 (en) * 2020-03-26 2021-09-29 Koninklijke Philips N.V. Magnetic resonance imaging of breast micro-calcifications
DE102020215031A1 (de) * 2020-11-30 2022-06-02 Siemens Healthcare Gmbh Computerimplementiertes Verfahren zur Auswertung von Magnetresonanzdaten, Magnetresonanzeinrichtung, Computerprogramm und elektronisch lesbarer Datenträger
CN117148244A (zh) * 2022-05-24 2023-12-01 上海联影医疗科技股份有限公司 图像水脂分离方法、装置、设备和计算机可读存储介质
CN117653026B (zh) * 2023-11-03 2024-09-20 浙江大学 一种人体组织水分含量测量方法、系统、电子设备及介质

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6091243A (en) * 1997-11-13 2000-07-18 The University Of British Columbia Water-fat imaging with direct phase encoding (DPE)
US7298144B2 (en) * 2005-05-06 2007-11-20 The Board Of Trustee Of The Leland Stanford Junior University Homodyne reconstruction of water and fat images based on iterative decomposition of MRI signals
EP3112890A1 (en) * 2008-04-17 2017-01-04 Advanced MR Analytics AB Improved magnetic resonance images
US8064674B2 (en) 2008-11-03 2011-11-22 Siemens Aktiengesellschaft Robust classification of fat and water images from 1-point-Dixon reconstructions
DE102010061974B4 (de) * 2010-11-25 2013-01-03 Siemens Aktiengesellschaft NMR-Verfahren und MR-Vorrichtung zur Phasenkorrektur bei gemischten Geweben
EP2610632A1 (en) 2011-12-29 2013-07-03 Koninklijke Philips Electronics N.V. MRI with Dixon-type water/fat separation and prior knowledge about inhomogeneity of the main magnetic field
CN104603629B (zh) * 2012-09-04 2017-06-13 皇家飞利浦有限公司 具有狄克逊水脂分离的propeller
WO2015028481A1 (en) * 2013-08-30 2015-03-05 Koninklijke Philips N.V. Dixon magnetic resonance imaging
US10359488B2 (en) * 2013-11-07 2019-07-23 Siemens Healthcare Gmbh Signal component identification using medical imaging
US10591562B2 (en) * 2015-06-12 2020-03-17 Koninklijke Philips N.V. Bone MRI using B0 inhomogeneity map and a subject magnetic susceptibility map
DE102015218168A1 (de) 2015-09-22 2017-03-23 Siemens Healthcare Gmbh Zuordnung einer Spinspezies zu einem Kombinationsbild
US10304198B2 (en) * 2016-09-26 2019-05-28 Siemens Healthcare Gmbh Automatic medical image retrieval

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
[Lecture Notes in Computer Science; Lect.Notes Computer], vol. 9993 Chap.16, no. 558, 2016, Zhao Liang et al., "Identification of Water and Fat Images in Dixon MRI Using Aggregated Patch-Based Convolutional Neural Networks", p. 125-132. *
CHO, JAEJIN ET AL., "Robust Water-Fat Separation in Multi-Echo GRE Sequence using Patch-Based Neural Network", INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE, ISMRM, 2030 ADDISON STREET, 7TH FLOOR, BERKELEY, CA 94704 USA, no. 5610, 2018-06-01, page 5610, the whole document *
GLOCKER BEN ET AL "Correction of Fat-Water Swaps in Dixon MRI", 2016-10-02, INTERNATIONAL CONFERENCE ON COMPUTER ANALYSIS OF IMAGES AND PATTERNS. CAIP 2017: COMPUTER ANALYSIS OF IMAGES AND PATTERNS; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE 536-543 *
JONATHAN ANDERSSON ET AL, "Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 2018-12-12, the whole document *
MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE, vol. 29, no. 5, 2016, Yang Yu Xin et al., "Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images", p. 723-731. *
VEMULAPALLI RAVITEJA ET AL, "Unsupervised Cross-Modal Synthesis of Subject-Specific Scans", 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), IEEE, 12-07-2015, pages 630-638, doi:10.1109/ICCV.2015.79, third paragraph of section 1 *

Also Published As

Publication number Publication date
JP2022529944A (ja) 2022-06-27
US20220196769A1 (en) 2022-06-23
CN113711076A (zh) 2021-11-26
US11906608B2 (en) 2024-02-20
EP3726240A1 (en) 2020-10-21
DE112020002013T5 (de) 2022-01-27
GB202116627D0 (en) 2022-01-05
CN113711076B (zh) 2025-02-18
WO2020212244A1 (en) 2020-10-22
GB2597218A (en) 2022-01-19

Similar Documents

Publication Publication Date Title
GB2597218B (en) Automated detection of water-fat swaps in Dixon magnetic resonance imaging
IL266747A (en) Portable low-field magnetic resonance imaging methods and systems
IL266748A (en) Systems and methods for automatic detection in magnetic resonance images
SG11202109088TA (en) Systems and methods for performing magnetic resonance imaging
IL278820A (en) B0 magnet methods and fixture for a magnetic resonance imaging system
IL280125A (en) Methods and apparatus for patient positioning in magnetic resonance imaging
SG11202010238PA (en) Systems and methods for improving magnetic resonance imaging using deep learning
IL284396A (en) Correcting for hysteresis in magnetic resonance imaging
EP3749973A4 (en) MULTI-RESOLUTION QUANTITATIVE SUSCEPTIBILITY MAPPING WITH MAGNETIC RESONANCE IMAGING
IL289101A (en) Systems and methods for image reconstruction in magnetic resonance imaging
GB201809706D0 (en) Magnetometer and method of detecting a magnetic field
GB2545149B (en) System and method for flip angle determination in magnetic resonance imaging
EP3688478A4 (en) MAGNETIC RESONANCE COILS FOR SIMULTANEOUS IMAGING AND B0 SHIMMING
SG11202101715SA (en) A method of analysing magnetic resonance imaging images
WO2013036607A3 (en) Systems and methods for detecting the presence of anomalous material within tissue
EP3459052A4 (en) NUCLEAR MAGNETIC RESONANCE MEASURING DEVICE FOR HOLE HOLE MEASUREMENTS
GB201618017D0 (en) Magnetic resonance imaging matrix shim coil system and method
EP3248543B8 (en) Apparatus and method for visualizing tissue macro- and microstructure for pathology evaluation in magnetic resonance imaging
GB2591000B (en) Gradient coil apparatus and methods for MRI
GB2585275B (en) Integrated active detuning for magnetic resonance imaging
EP3217873A4 (en) System and method for device tracking via magnetic resonance imaging with light-modulated magnetic susceptibility markers
MX2016014683A (es) Sistemas y metodos de resonancia magnetica que emplean secuencias de pulsos de multiples formas para mediciones paralelas.
GB2537211B (en) Tensioning apparatus, superconducting magnet and magnetic resonance imaging device
GB2565400B (en) System and method for using coils in magnetic resonance imaging
KR102345856B9 (ko) 자기공명영상시스템 내에서 타핵종 핵자기공명신호를 얻을 수 있는 고주파코일장치 및 동작방법

Legal Events

Date Code Title Description
746 Register noted 'licences of right' (sect. 46/1977)

Effective date: 20230904