JP2023526008A5 - - Google Patents
Info
- Publication number
- JP2023526008A5 JP2023526008A5 JP2022568522A JP2022568522A JP2023526008A5 JP 2023526008 A5 JP2023526008 A5 JP 2023526008A5 JP 2022568522 A JP2022568522 A JP 2022568522A JP 2022568522 A JP2022568522 A JP 2022568522A JP 2023526008 A5 JP2023526008 A5 JP 2023526008A5
- Authority
- JP
- Japan
- Prior art keywords
- magnetic resonance
- image data
- space data
- resonance image
- data
- 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.)
- Granted
Links
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202063022925P | 2020-05-11 | 2020-05-11 | |
| US63/022,925 | 2020-05-11 | ||
| EP20176989.0 | 2020-05-28 | ||
| EP20176989.0A EP3916417A1 (en) | 2020-05-28 | 2020-05-28 | Correction of magnetic resonance images using multiple magnetic resonance imaging system configurations |
| PCT/EP2021/060286 WO2021228515A1 (en) | 2020-05-11 | 2021-04-21 | Correction of magnetic resonance images using multiple magnetic resonance imaging system configurations |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2023526008A JP2023526008A (ja) | 2023-06-20 |
| JP2023526008A5 true JP2023526008A5 (https=) | 2024-06-14 |
| JP7757314B2 JP7757314B2 (ja) | 2025-10-21 |
Family
ID=70918263
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2022568522A Active JP7757314B2 (ja) | 2020-05-11 | 2021-04-21 | 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 |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US12067652B2 (https=) |
| EP (2) | EP3916417A1 (https=) |
| JP (1) | JP7757314B2 (https=) |
| CN (1) | CN115552272A (https=) |
| WO (1) | WO2021228515A1 (https=) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3828579A1 (en) * | 2019-11-28 | 2021-06-02 | Koninklijke Philips N.V. | Adaptive reconstruction of magnetic resonance images |
| US11828824B2 (en) * | 2021-03-17 | 2023-11-28 | GE Precision Healthcare LLC | System and method for deep learning-based accelerated magnetic resonance imaging with extended field of view coil sensitivity calibration |
| US12555291B2 (en) * | 2021-04-27 | 2026-02-17 | Siemens Healthineers Ag | Method for automated regularization of hybrid K-space combination using a noise adjustment scan |
| US20230337987A1 (en) * | 2022-04-21 | 2023-10-26 | The General Hospital Corporation | Detecting motion artifacts from k-space data in segmentedmagnetic resonance imaging |
| US20240037815A1 (en) * | 2022-07-26 | 2024-02-01 | Siemens Healthcare Gmbh | Method and apparatus for accelerated acquisition and reconstruction of cine mri using a deep learning based convolutional neural network |
| EP4431967A1 (en) | 2023-03-13 | 2024-09-18 | Koninklijke Philips N.V. | Training of neural networks to generate synthetic three-dimensional magnetic resonance images |
| US20250005715A1 (en) * | 2023-06-30 | 2025-01-02 | The Regents Of The University Of California | Methods, apparatuses, systems and computer-readable mediums for correcting echo planar imaging artifacts |
| US20250314728A1 (en) * | 2024-04-08 | 2025-10-09 | GE Precision Healthcare LLC | System and method for detecting motion-ridden shots in multi-shot acquisitions and utilizing deep learning based reconstruction for motion correction |
| US12372369B1 (en) * | 2024-07-11 | 2025-07-29 | SB Technology, Inc. | Detecting and fixing map artifacts |
Family Cites Families (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007124450A2 (en) | 2006-04-21 | 2007-11-01 | The Trustees Of The University Of Pennsylvania | Motion artifact compensation |
| US9340768B2 (en) | 2008-07-16 | 2016-05-17 | The Texas A&M University System | Transformation of glycerol and cellulosic materials into high energy fuels |
| US8848990B2 (en) | 2010-09-28 | 2014-09-30 | Siemens Aktiengesellschaft | Automatic registration of image series with varying contrast based on synthetic images derived from intensity behavior model |
| JP5835930B2 (ja) * | 2011-04-15 | 2015-12-24 | 株式会社東芝 | 医用画像表示装置 |
| US9588207B2 (en) * | 2011-10-06 | 2017-03-07 | National Institutes of Health (NIH), U.S. Dept. of Health and Human Services (DHHS), The United States of America NIH Division of Extramural Inventions and Technology Resources (DEITR) | System for reconstructing MRI images acquired in parallel |
| WO2015197366A1 (en) | 2014-06-23 | 2015-12-30 | Koninklijke Philips N.V. | Motion correction in magnetic resonance imaging |
| US9983283B2 (en) | 2015-03-16 | 2018-05-29 | Toshiba Medical Systems Corporation | Accelerated MRI using radial strips and undersampling of k-space |
| JP2016209336A (ja) * | 2015-05-11 | 2016-12-15 | 株式会社日立製作所 | 磁気共鳴イメージング装置 |
| US10671939B2 (en) | 2016-04-22 | 2020-06-02 | New York University | System, method and computer-accessible medium for learning an optimized variational network for medical image reconstruction |
| US10096109B1 (en) | 2017-03-31 | 2018-10-09 | The Board Of Trustees Of The Leland Stanford Junior University | Quality of medical images using multi-contrast and deep learning |
| EP3447520A1 (en) * | 2017-08-22 | 2019-02-27 | Koninklijke Philips N.V. | Data-driven correction of phase depending artefacts in a magnetic resonance imaging system |
| EP3477583A1 (en) | 2017-10-31 | 2019-05-01 | Koninklijke Philips N.V. | Deep-learning based processing of motion artifacts in magnetic resonance imaging data |
| US10573031B2 (en) | 2017-12-06 | 2020-02-25 | Siemens Healthcare Gmbh | Magnetic resonance image reconstruction with deep reinforcement learning |
| KR102708986B1 (ko) | 2018-04-19 | 2024-09-24 | 서틀 메디컬, 인크. | 딥 러닝을 사용하여 자기 공명 이미징을 향상시키기 위한 시스템들 및 방법들 |
| US12039699B2 (en) * | 2018-05-25 | 2024-07-16 | Vidur MAHAJAN | Method and system for simulating and constructing original medical images from one modality to other modality |
| US10852379B2 (en) | 2018-06-07 | 2020-12-01 | Siemens Healthcare Gmbh | Artifact reduction by image-to-image network in magnetic resonance imaging |
| US11756160B2 (en) * | 2018-07-27 | 2023-09-12 | Washington University | ML-based methods for pseudo-CT and HR MR image estimation |
| CN109325985B (zh) * | 2018-09-18 | 2020-07-21 | 上海联影智能医疗科技有限公司 | 磁共振图像重建方法、装置和计算机可读存储介质 |
| CN110095742B (zh) * | 2019-05-13 | 2022-02-08 | 上海东软医疗科技有限公司 | 一种基于神经网络的平面回波成像方法和装置 |
| CN110333466B (zh) * | 2019-06-19 | 2022-06-07 | 东软医疗系统股份有限公司 | 一种基于神经网络的磁共振成像方法和装置 |
| CN110244246B (zh) * | 2019-07-03 | 2021-07-16 | 上海联影医疗科技股份有限公司 | 磁共振成像方法、装置、计算机设备和存储介质 |
| CN110807492B (zh) * | 2019-11-06 | 2022-05-13 | 厦门大学 | 一种磁共振多参数同时定量成像方法及系统 |
| CN110942496B (zh) * | 2019-12-13 | 2022-02-11 | 厦门大学 | 基于螺旋桨采样和神经网络的磁共振图像重建方法及系统 |
-
2020
- 2020-05-28 EP EP20176989.0A patent/EP3916417A1/en not_active Withdrawn
-
2021
- 2021-04-21 EP EP21719164.2A patent/EP4150360A1/en active Pending
- 2021-04-21 JP JP2022568522A patent/JP7757314B2/ja active Active
- 2021-04-21 WO PCT/EP2021/060286 patent/WO2021228515A1/en not_active Ceased
- 2021-04-21 CN CN202180034897.8A patent/CN115552272A/zh active Pending
- 2021-04-21 US US17/923,617 patent/US12067652B2/en active Active
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