CN116406463A - 磁共振成像中的射频脉冲和梯度脉冲的实时设计 - Google Patents

磁共振成像中的射频脉冲和梯度脉冲的实时设计 Download PDF

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CN116406463A
CN116406463A CN202180074549.3A CN202180074549A CN116406463A CN 116406463 A CN116406463 A CN 116406463A CN 202180074549 A CN202180074549 A CN 202180074549A CN 116406463 A CN116406463 A CN 116406463A
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neural network
convolutional neural
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radio frequency
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张雅静
王家正
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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
    • G01R33/483—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy
    • G01R33/4833—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices
    • G01R33/4836—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices using an RF pulse being spatially selective in more than one spatial dimension, e.g. a 2D pencil-beam excitation pulse
    • 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
    • G01R33/543—Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription
    • 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

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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)
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  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
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  • General Engineering & Computer Science (AREA)
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  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Signal Processing (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Optics & Photonics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
CN202180074549.3A 2020-11-04 2021-11-04 磁共振成像中的射频脉冲和梯度脉冲的实时设计 Pending CN116406463A (zh)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
CNPCT/CN2020/126499 2020-11-04
CN2020126499 2020-11-04
EP20214200.6 2020-12-15
EP20214200.6A EP3995848A1 (en) 2020-11-04 2020-12-15 Realtime design of radio-frequency pulses and gradient pulses in magnetic resonanc imaging
PCT/EP2021/080567 WO2022096539A1 (en) 2020-11-04 2021-11-04 Realtime design of radio-frequency pulses and gradient pulses in magnetic resonanc imaging

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CN116406463A true CN116406463A (zh) 2023-07-07

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US (1) US12360190B2 (https=)
EP (1) EP3995848A1 (https=)
JP (1) JP2023548854A (https=)
CN (1) CN116406463A (https=)
DE (1) DE112021005801T5 (https=)
WO (1) WO2022096539A1 (https=)

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US12387096B2 (en) * 2021-10-06 2025-08-12 Google Llc Image-to-image mapping by iterative de-noising
CN115759179B (zh) * 2022-11-18 2026-03-03 中国科学院自动化研究所 一种应用于多任务学习的策略模型训练方法、装置及设备
WO2026006649A1 (en) * 2024-06-27 2026-01-02 The General Hospital Corporation System and method of creating and using automated system for physics control

Citations (5)

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JP2016154849A (ja) * 2015-02-23 2016-09-01 東芝メディカルシステムズ株式会社 磁気共鳴イメージング装置
US20190086488A1 (en) * 2017-09-15 2019-03-21 Siemens Healthcare Gmbh Magnetic Resonance Radio Frequency Pulse Design using Machine Learning
US20190377044A1 (en) * 2018-06-12 2019-12-12 Koninklijke Philips N.V. Determination of higher order terms of the three-dimensional gradient impulse response function
US20200294282A1 (en) * 2019-03-14 2020-09-17 Hyperfine Research, Inc. Deep learning techniques for alignment of magnetic resonance images
US20200341084A1 (en) * 2019-04-29 2020-10-29 Regents Of The University Of Minnesota System and method for producing radiofrequency pulses in magnetic resonance using an optimal phase surface

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JP4416221B2 (ja) * 1999-09-28 2010-02-17 株式会社日立メディコ 磁気共鳴画像診断装置
JP2008054738A (ja) * 2006-08-29 2008-03-13 Hitachi Medical Corp 磁気共鳴イメージング装置
JP6495057B2 (ja) * 2015-03-16 2019-04-03 キヤノンメディカルシステムズ株式会社 Ｍｒｉ装置及び撮像時間短縮方法
US10588523B2 (en) * 2016-04-15 2020-03-17 Siemens Healthcare Gmbh 4D flow measurements of the hepatic vasculatures with two-dimensional excitation
CN106372571A (zh) * 2016-08-18 2017-02-01 宁波傲视智绘光电科技有限公司 路面交通标志检测与识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016154849A (ja) * 2015-02-23 2016-09-01 東芝メディカルシステムズ株式会社 磁気共鳴イメージング装置
US20190086488A1 (en) * 2017-09-15 2019-03-21 Siemens Healthcare Gmbh Magnetic Resonance Radio Frequency Pulse Design using Machine Learning
US20190377044A1 (en) * 2018-06-12 2019-12-12 Koninklijke Philips N.V. Determination of higher order terms of the three-dimensional gradient impulse response function
US20200294282A1 (en) * 2019-03-14 2020-09-17 Hyperfine Research, Inc. Deep learning techniques for alignment of magnetic resonance images
US20200341084A1 (en) * 2019-04-29 2020-10-29 Regents Of The University Of Minnesota System and method for producing radiofrequency pulses in magnetic resonance using an optimal phase surface

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JIANFENG ZHENG 等: "Prediction of MRI RF Exposure for Implantable Plate Devices Using Artificial Neural Network", 《IEEE》, 30 June 2020 (2020-06-30) *
TIANRUI LUO 等: "Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI", 《ARXIV:2008.10594V2》, 24 August 2020 (2020-08-24), pages 1 - 14 *
段亚阳 等: "基于非增强MRI的影像组学术前预测肝细胞癌 微血管浸润的研究", 《磁共振成像》, 20 March 2020 (2020-03-20) *

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JP2023548854A (ja) 2023-11-21
US20230408612A1 (en) 2023-12-21
WO2022096539A1 (en) 2022-05-12
EP3995848A1 (en) 2022-05-11
DE112021005801T5 (de) 2023-10-05
US12360190B2 (en) 2025-07-15

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