CN111295687B - 对磁共振成像数据中的运动伪影的基于深度学习的处理 - Google Patents
对磁共振成像数据中的运动伪影的基于深度学习的处理 Download PDFInfo
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- CN111295687B CN111295687B CN201880070588.4A CN201880070588A CN111295687B CN 111295687 B CN111295687 B CN 111295687B CN 201880070588 A CN201880070588 A CN 201880070588A CN 111295687 B CN111295687 B CN 111295687B
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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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
- G01R33/54—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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- 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
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- 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
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- 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
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- G06N3/045—Combinations of networks
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- 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]
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- 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
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- 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
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T5/60—Image enhancement or restoration using machine learning, e.g. neural networks
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- 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
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- G—PHYSICS
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10076—4D tomography; Time-sequential 3D tomography
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- 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]
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- 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
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- 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]
-
- 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
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
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- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Signal Processing (AREA)
- High Energy & Nuclear Physics (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Quality & Reliability (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP17199301.7 | 2017-10-31 | ||
| EP17199301.7A EP3477583A1 (en) | 2017-10-31 | 2017-10-31 | Deep-learning based processing of motion artifacts in magnetic resonance imaging data |
| PCT/EP2018/078863 WO2019086284A1 (en) | 2017-10-31 | 2018-10-22 | Deep-learning based processing of motion artifacts in magnetic resonance imaging data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111295687A CN111295687A (zh) | 2020-06-16 |
| CN111295687B true CN111295687B (zh) | 2024-05-21 |
Family
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201880070588.4A Active CN111295687B (zh) | 2017-10-31 | 2018-10-22 | 对磁共振成像数据中的运动伪影的基于深度学习的处理 |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US11320508B2 (enExample) |
| EP (2) | EP3477583A1 (enExample) |
| JP (1) | JP6907410B2 (enExample) |
| CN (1) | CN111295687B (enExample) |
| WO (1) | WO2019086284A1 (enExample) |
Families Citing this family (37)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US11633123B2 (en) | 2017-10-31 | 2023-04-25 | Koninklijke Philips N.V. | Motion artifact prediction during data acquisition |
| US10698063B2 (en) * | 2017-11-01 | 2020-06-30 | Siemens Healthcare Gmbh | Motion artifact reduction of magnetic resonance images with an adversarial trained network |
| WO2019169393A1 (en) * | 2018-03-02 | 2019-09-06 | The General Hospital Corporation | Improved multi-shot echo planar imaging through machine learning |
| US11681001B2 (en) * | 2018-03-09 | 2023-06-20 | The Board Of Trustees Of The Leland Stanford Junior University | Deep learning method for nonstationary image artifact correction |
| AU2019268404B2 (en) * | 2018-05-15 | 2025-04-17 | Monash University | Method and system of motion correction for magnetic resonance imaging |
| TW202011893A (zh) | 2018-07-30 | 2020-04-01 | 美商超精細研究股份有限公司 | 用於核磁共振影像重建之深度學習技術 |
| US11995800B2 (en) * | 2018-08-07 | 2024-05-28 | Meta Platforms, Inc. | Artificial intelligence techniques for image enhancement |
| CA3107776A1 (en) | 2018-08-15 | 2020-02-20 | Hyperfine Research, Inc. | Deep learning techniques for suppressing artefacts in magnetic resonance images |
| US11011257B2 (en) * | 2018-11-21 | 2021-05-18 | Enlitic, Inc. | Multi-label heat map display system |
| CN113811921A (zh) | 2019-03-14 | 2021-12-17 | 海珀菲纳股份有限公司 | 用于根据空间频率数据来生成磁共振图像的深度学习技术 |
| EP3745153A1 (en) * | 2019-05-28 | 2020-12-02 | Koninklijke Philips N.V. | A method for motion artifact detection |
| US11726209B2 (en) | 2019-06-25 | 2023-08-15 | Faro Technologies, Inc. | Artifact filtering using artificial intelligence |
| EP3757940B1 (de) * | 2019-06-26 | 2025-04-16 | Siemens Healthineers AG | Ermittlung einer patientenbewegung während einer medizinischen bildgebungsmessung |
| EP3839547A1 (en) * | 2019-12-16 | 2021-06-23 | Koninklijke Philips N.V. | Sense magnetic resonance imaging reconstruction using neural networks |
| CN111223066B (zh) * | 2020-01-17 | 2024-06-11 | 上海联影医疗科技股份有限公司 | 运动伪影校正方法、装置、计算机设备和可读存储介质 |
| CN111325161B (zh) * | 2020-02-25 | 2023-04-18 | 四川翼飞视科技有限公司 | 一种基于注意力机制的人脸检测神经网络的构建方法 |
| US12131548B2 (en) * | 2020-04-15 | 2024-10-29 | Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi | Method for training shallow convolutional neural networks for infrared target detection using a two-phase learning strategy |
| EP3916417A1 (en) | 2020-05-28 | 2021-12-01 | Koninklijke Philips N.V. | Correction of magnetic resonance images using multiple magnetic resonance imaging system configurations |
| EP3910359A1 (en) * | 2020-05-12 | 2021-11-17 | Koninklijke Philips N.V. | Machine learning based detection of motion corrupted magnetic resonance imaging data |
| JP7551336B2 (ja) * | 2020-05-21 | 2024-09-17 | キヤノン株式会社 | 情報処理装置、情報処理方法、およびプログラム |
| EP3933758B1 (en) | 2020-07-02 | 2025-11-19 | Siemens Healthineers AG | Method and system for generating a medical image with localized artifacts using machine learning |
| US20220013231A1 (en) * | 2020-07-13 | 2022-01-13 | Corsmed Ab | Method for ai applications in mri simulation |
| CN111815730B (zh) * | 2020-07-15 | 2024-03-29 | 东软教育科技集团有限公司 | 生成含有运动伪影的ct图像的方法、装置及存储介质 |
| US11346912B2 (en) * | 2020-07-23 | 2022-05-31 | GE Precision Healthcare LLC | Systems and methods of generating robust phase images in magnetic resonance images |
| EP3975125A1 (en) * | 2020-09-24 | 2022-03-30 | Koninklijke Philips N.V. | Anonymous fingerprinting of medical images |
| US11360179B2 (en) | 2020-10-29 | 2022-06-14 | The Mitre Corporation | Systems and methods for estimating magnetic susceptibility through continuous motion in an MRI scanner |
| CN113192014B (zh) * | 2021-04-16 | 2024-01-30 | 深圳市第二人民医院(深圳市转化医学研究院) | 改进脑室分割模型的训练方法、装置、电子设备和介质 |
| US11948288B2 (en) * | 2021-06-07 | 2024-04-02 | Shanghai United Imaging Intelligence Co., Ltd. | Motion artifacts simulation |
| US12045958B2 (en) * | 2021-07-16 | 2024-07-23 | Shanghai United Imaging Intelligence Co., Ltd. | Motion artifact correction using artificial neural networks |
| US12136484B2 (en) | 2021-11-05 | 2024-11-05 | Altis Labs, Inc. | Method and apparatus utilizing image-based modeling in healthcare |
| EP4202427A1 (en) | 2021-12-23 | 2023-06-28 | Orbem GmbH | Direct inference based on undersampled mri data of industrial samples |
| EP4202468A1 (en) | 2021-12-23 | 2023-06-28 | Orbem GmbH | Direct inference based on undersampled mri data of humans or animals |
| US12475564B2 (en) | 2022-02-16 | 2025-11-18 | Proscia Inc. | Digital pathology artificial intelligence quality check |
| CN114862680B (zh) * | 2022-05-12 | 2025-10-21 | 上海电气控股集团有限公司智惠医疗装备分公司 | 一种图像重建方法、装置及电子设备 |
| CN115100310A (zh) * | 2022-06-27 | 2022-09-23 | 杭州微影医疗科技有限公司 | 一种磁共振磁敏感伪影的自动提示方法及系统 |
| CN115797729B (zh) * | 2023-01-29 | 2023-05-09 | 有方(合肥)医疗科技有限公司 | 模型训练方法及装置、运动伪影识别及提示的方法及装置 |
| EP4545954A1 (en) | 2023-10-26 | 2025-04-30 | Orbem GmbH | Method for enabling high-throughput imaging of industrial samples |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104541179A (zh) * | 2012-06-05 | 2015-04-22 | 皇家飞利浦有限公司 | 并行mri中的逐通道伪影减少 |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4919408B2 (ja) * | 2007-01-12 | 2012-04-18 | 富士フイルム株式会社 | 放射線画像処理方法および装置ならびにプログラム |
| CN102016922A (zh) * | 2008-01-10 | 2011-04-13 | 新加坡科技研究局 | 从核磁共振成像扫描数据的伪像中区别梗塞的方法 |
| CN102077108B (zh) * | 2008-04-28 | 2015-02-25 | 康奈尔大学 | 分子mri中的磁敏度精确量化 |
| US20110077484A1 (en) * | 2009-09-30 | 2011-03-31 | Nellcor Puritan Bennett Ireland | Systems And Methods For Identifying Non-Corrupted Signal Segments For Use In Determining Physiological Parameters |
| US10321892B2 (en) | 2010-09-27 | 2019-06-18 | Siemens Medical Solutions Usa, Inc. | Computerized characterization of cardiac motion in medical diagnostic ultrasound |
| US9636019B2 (en) * | 2010-10-07 | 2017-05-02 | The Medical Research, Infrastructure, And Health Services Fund Of The Tel-Aviv Medical Center | Device for use in electro-biological signal measurement in the presence of a magnetic field |
| US9788761B2 (en) * | 2014-02-27 | 2017-10-17 | Toshiba Medical Systems Corporation | Motion correction for magnetic resonance angiography (MRA) with 3D radial acquisitions |
| WO2015175806A1 (en) | 2014-05-16 | 2015-11-19 | The Trustees Of The University Of Pennsylvania | Applications of automatic anatomy recognition in medical tomographic imagery based on fuzzy anatomy models |
| DE102015212953B4 (de) | 2015-07-10 | 2024-08-22 | Siemens Healthineers Ag | Künstliche neuronale Netze zur Klassifizierung von medizinischen Bilddatensätzen |
| US10521902B2 (en) | 2015-10-14 | 2019-12-31 | The Regents Of The University Of California | Automated segmentation of organ chambers using deep learning methods from medical imaging |
| EP3694413B1 (en) * | 2017-10-09 | 2025-06-11 | The Board of Trustees of the Leland Stanford Junior University | Contrast dose reduction for medical imaging using deep learning |
-
2017
- 2017-10-31 EP EP17199301.7A patent/EP3477583A1/en not_active Withdrawn
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2018
- 2018-10-22 US US16/759,778 patent/US11320508B2/en active Active
- 2018-10-22 WO PCT/EP2018/078863 patent/WO2019086284A1/en not_active Ceased
- 2018-10-22 JP JP2020524193A patent/JP6907410B2/ja active Active
- 2018-10-22 CN CN201880070588.4A patent/CN111295687B/zh active Active
- 2018-10-22 EP EP18786366.7A patent/EP3704666B1/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104541179A (zh) * | 2012-06-05 | 2015-04-22 | 皇家飞利浦有限公司 | 并行mri中的逐通道伪影减少 |
Non-Patent Citations (1)
| Title |
|---|
| Automatic detection of motion artifacts in MR images using CNNS;KristoJ Meding et al.;《2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)》;第811-815页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| JP6907410B2 (ja) | 2021-07-21 |
| WO2019086284A1 (en) | 2019-05-09 |
| US11320508B2 (en) | 2022-05-03 |
| CN111295687A (zh) | 2020-06-16 |
| EP3477583A1 (en) | 2019-05-01 |
| JP2021501015A (ja) | 2021-01-14 |
| EP3704666A1 (en) | 2020-09-09 |
| EP3704666B1 (en) | 2021-06-16 |
| US20210181287A1 (en) | 2021-06-17 |
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