JP2022529944A - ディクソン磁気共鳴イメージングにおける水－脂肪スワップの自動検出 - Google Patents

ディクソン磁気共鳴イメージングにおける水－脂肪スワップの自動検出 Download PDF

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JP2022529944A
JP2022529944A JP2021561670A JP2021561670A JP2022529944A JP 2022529944 A JP2022529944 A JP 2022529944A JP 2021561670 A JP2021561670 A JP 2021561670A JP 2021561670 A JP2021561670 A JP 2021561670A JP 2022529944 A JP2022529944 A JP 2022529944A
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magnetic resonance
water
fat
swap
dixon
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JP2022529944A5 (https=
Inventor
カルステン ソマー
ステッフェン ウェイス
ホルガー エガース
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Koninklijke Philips NV
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Koninklijke Philips NV
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    • 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

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  • 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)
JP2021561670A 2019-04-19 2020-04-09 ディクソン磁気共鳴イメージングにおける水－脂肪スワップの自動検出 Pending JP2022529944A (ja)

Applications Claiming Priority (3)

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
EP19170373.5 2019-04-19
PCT/EP2020/060138 WO2020212244A1 (en) 2019-04-19 2020-04-09 Automated detection of water-fat swaps in dixon magnetic resonance imaging

Publications (2)

Publication Number Publication Date
JP2022529944A true JP2022529944A (ja) 2022-06-27
JP2022529944A5 JP2022529944A5 (https=) 2023-04-17

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JP2021561670A Pending JP2022529944A (ja) 2019-04-19 2020-04-09 ディクソン磁気共鳴イメージングにおける水－脂肪スワップの自動検出

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=)

Cited By (1)

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JP2023518487A (ja) * 2020-03-26 2023-05-01 コーニンクレッカ フィリップス エヌ ヴェ 胸部微小石灰化部位の磁気共鳴イメージング

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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

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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

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AGISILAOS CHARTSIAS, ET AL.: "Multimodal MR synthesis via Modality-Invariant Latent Representation", IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 37, no. 3, JPN6023042133, 2018, pages 803 - 814, XP055656238, ISSN: 0005454471, DOI: 10.1109/TMI.2017.2764326 *
BEN GLOCKER, ET AL.: "Correction of Fat-Water Swaps in Dixon MRI", MICCAI, vol. Part III, LNCS 9902, JPN6023042134, 2016, pages 536 - 543, ISSN: 0005454470 *
DANIEL COHEN, ET AL.: "Universal Approximation Functions for Fast Learning to Rank", SIGIR’18 SHORT RESEARCH PAPERS I, JPN6024013339, 2018, pages 1017 - 1020, XP058633198, ISSN: 0005454472, DOI: 10.1145/3209978.3210137 *
WEI SHEN, ET AL.: "Deep Regression Forests for Age Estimation", PROCEEDINGSOF THE IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), JPN6024013340, 2018, pages 2304 - 2313, XP033476196, ISSN: 0005454473, DOI: 10.1109/CVPR.2018.00245 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023518487A (ja) * 2020-03-26 2023-05-01 コーニンクレッカ フィリップス エヌ ヴェ 胸部微小石灰化部位の磁気共鳴イメージング
JP7663593B2 (ja) 2020-03-26 2025-04-16 コーニンクレッカ フィリップス エヌ ヴェ 胸部微小石灰化部位の磁気共鳴イメージング

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Publication number Publication date
US20220196769A1 (en) 2022-06-23
CN113711076A (zh) 2021-11-26
US11906608B2 (en) 2024-02-20
GB2597218B (en) 2023-07-05
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

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