JPWO2020234204A5 - - Google Patents
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- JPWO2020234204A5 JPWO2020234204A5 JP2021568555A JP2021568555A JPWO2020234204A5 JP WO2020234204 A5 JPWO2020234204 A5 JP WO2020234204A5 JP 2021568555 A JP2021568555 A JP 2021568555A JP 2021568555 A JP2021568555 A JP 2021568555A JP WO2020234204 A5 JPWO2020234204 A5 JP WO2020234204A5
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- field alignment
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- 230000000007 visual effect Effects 0.000 claims 21
- 238000002595 magnetic resonance imaging Methods 0.000 claims 12
- 230000004044 response Effects 0.000 claims 11
- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 claims 6
- 238000004590 computer program Methods 0.000 claims 3
- 238000010801 machine learning Methods 0.000 claims 3
- 238000000034 method Methods 0.000 claims 3
- 238000013527 convolutional neural network Methods 0.000 claims 1
- 238000002059 diagnostic imaging Methods 0.000 claims 1
Claims (11)
前記予測器アルゴリズムは訓練可能機械学習アルゴリズムを含み、前記予測器アルゴリズムは畳み込みニューラルネットワークであり、
前記マシン実行可能命令の実行は、前記プロセッサに、
前記１つ又は複数のローカライザ磁気共鳴画像及び前記対象者メタデータを受信することと、
前記予測器アルゴリズムへの前記１つ又は複数のローカライザ磁気共鳴画像の入力に応答して、及び前記対象者メタデータの入力に応答して、前記予測器アルゴリズムから前記予測視野整列データを受信することと
を実施させる、医療システム。 A memory storing machine-executable instructions and a predictor algorithm for outputting predicted visual field alignment data for a magnetic resonance imaging system in response to input of one or more localizer magnetic resonance images and subject metadata. and a processor for controlling the medical system, comprising:
said predictor algorithm comprising a trainable machine learning algorithm, said predictor algorithm being a convolutional neural network;
Execution of the machine-executable instructions causes the processor to:
receiving the one or more localizer magnetic resonance images and the subject metadata;
receiving the predicted visual field alignment data from the predictor algorithm in response to inputting the one or more localizer magnetic resonance images into the predictor algorithm and in response to inputting the subject metadata. health care system that enforces
前記予測器アルゴリズムへの前記１つ又は複数の訓練用磁気共鳴画像の入力に応答して、及び前記訓練用対象者メタデータの入力に応答して、前記予測器アルゴリズムから前記予測視野整列データを受信することと、
前記予測視野整列データと前記訓練用視野整列データとの前記比較を判定することと、
前記予測視野整列データと前記訓練用視野整列データとの前記比較を使用して前記予測器アルゴリズムを訓練することと
を実施させる、請求項１に記載の医療システム。 The memory further stores training data including training entries, each of the training entries being one or more of training magnetic resonance images, training subject metadata, and training visual field alignment data. said memory further comprising a training algorithm for training said predictor algorithm using a comparison of said predicted eye alignment data and said training eye alignment data; Execution further causes the processor to:
retrieving the predicted visual field alignment data from the predictor algorithm in response to inputting the one or more training magnetic resonance images into the predictor algorithm and in response to inputting the training subject metadata; to receive;
determining the comparison between the predicted visual field alignment data and the training visual field alignment data;
and using the comparison of the predicted visual field alignment data and the training visual field alignment data to train the predictor algorithm.
前記ローカライザパルスシーケンスコマンドによって前記磁気共鳴イメージングシステムを制御することにより前記ローカライザ磁気共鳴イメージングデータを取得することと、
前記ローカライザ磁気共鳴イメージングデータから前記１つ又は複数のローカライザ磁気共鳴画像を再構成することと
を実施させる、請求項５に記載の医療システム。 The memory further includes localizer pulse sequence commands for controlling the magnetic resonance imaging system to acquire localizer magnetic resonance imaging data, and the execution of the machine-executable instructions further causes the processor to:
acquiring the localizer magnetic resonance imaging data by controlling the magnetic resonance imaging system with the localizer pulse sequence commands;
and reconstructing the one or more localizer magnetic resonance images from the localizer magnetic resonance imaging data.
前記予測視野整列データによって前記臨床パルスシーケンスコマンドを修正することにより修正済みパルスシーケンスコマンドを生成することと、
前記修正済みパルスシーケンスコマンドによって前記磁気共鳴イメージングシステムを制御することにより前記臨床磁気共鳴イメージングデータを取得することと
を実施させる、請求項５又は６に記載の医療システム。 The memory further includes clinical pulse sequence commands for controlling the magnetic resonance imaging system to acquire clinical magnetic resonance imaging data, the execution of the machine-executable instructions further causing the processor to:
generating a modified pulse sequence command by modifying the clinical pulse sequence command with the predicted visual field alignment data;
7. The medical system of claim 5 or 6 , wherein acquiring the clinical magnetic resonance imaging data by controlling the magnetic resonance imaging system with the modified pulse sequence commands is performed.
前記１つ又は複数のローカライザ磁気共鳴画像及び前記対象者メタデータを受信することと、
前記予測器アルゴリズムへの前記１つ又は複数のローカライザ磁気共鳴画像の入力に応答して、及び前記対象者メタデータの入力に応答して、前記予測器アルゴリズムから前記予測視野整列データを受信することと
を実施させる、コンピュータプログラム。 A computer program comprising machine-executable instructions for execution by a processor for controlling a medical system, said computer program being further responsive to input of one or more localizer magnetic resonance images and subject metadata. a predictor algorithm for outputting predicted view alignment data, said predictor algorithm comprising a trainable machine learning algorithm, execution of said machine-executable instructions causing said processor to:
receiving the one or more localizer magnetic resonance images and the subject metadata;
receiving the predicted visual field alignment data from the predictor algorithm in response to inputting the one or more localizer magnetic resonance images into the predictor algorithm and in response to inputting the subject metadata. A computer program that causes
前記予測器アルゴリズムへの前記１つ又は複数の訓練用磁気共鳴画像の入力に応答して、及び前記訓練用対象者メタデータの入力に応答して、前記予測器アルゴリズムから前記予測視野整列データを受信するステップと、
前記予測視野整列データと前記訓練用視野整列データとの前記比較を判定するステップと、
前記予測視野整列データと前記訓練用視野整列データとの前記比較を入力することによって、前記訓練用アルゴリズムによって前記予測器アルゴリズムを訓練するステップと
を有する、方法。 A method of operating a medical system, said medical system comprising a memory storing a predictor algorithm, said predictor algorithm being responsive to input of one or more localizer magnetic resonance images and subject metadata. The predictor algorithm comprises a trainable machine learning algorithm, the memory further stores training data including training entries, each of the training entries configured to output predictive view alignment data. , one or more training magnetic resonance images, training subject metadata, and training visual field alignment data, the memory further comprising comparing the predicted visual field alignment data with the training visual field alignment data. a training algorithm for training the predictor algorithm using
retrieving the predicted visual field alignment data from the predictor algorithm in response to inputting the one or more training magnetic resonance images into the predictor algorithm and in response to inputting the training subject metadata; receiving;
determining the comparison between the predicted visual field alignment data and the training visual field alignment data;
training the predictor algorithm with the training algorithm by inputting the comparison of the predicted visual field alignment data and the training visual field alignment data.
前記予測器アルゴリズムへの前記１つ又は複数のローカライザ磁気共鳴画像の入力に応答して、及び前記対象者メタデータの入力に応答して、前記予測器アルゴリズムから前記予測視野整列データを受信するステップと
を有する、請求項９又は１０に記載の方法。 receiving one or more localizer magnetic resonance images and subject metadata;
receiving said predicted visual field alignment data from said predictor algorithm in response to inputting said one or more localizer magnetic resonance images into said predictor algorithm and in response to inputting said subject metadata; 11. A method according to claim 9 or 10 , comprising
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP19175092.6 | 2019-05-17 | ||
| EP19175092 | 2019-05-17 | ||
| RU2019120557 | 2019-07-02 | ||
| RU2019120557 | 2019-07-02 | ||
| PCT/EP2020/063744 WO2020234204A1 (en) | 2019-05-17 | 2020-05-16 | Automated field of view alignment for magnetic resonance imaging |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2022533165A JP2022533165A (en) | 2022-07-21 |
| JPWO2020234204A5 true JPWO2020234204A5 (en) | 2023-05-23 |
| JP7391112B2 JP7391112B2 (en) | 2023-12-04 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2021568555A Active JP7391112B2 (en) | 2019-05-17 | 2020-05-16 | Automatic field alignment for magnetic resonance imaging |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US20220225888A1 (en) |
| EP (1) | EP3969923A1 (en) |
| JP (1) | JP7391112B2 (en) |
| CN (1) | CN113853526A (en) |
| WO (1) | WO2020234204A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022187666A1 (en) | 2021-03-04 | 2022-09-09 | Hyperfine Operations, Inc. | Systems and methods for performing magnetic resonance imaging with reduced operator interaction |
Family Cites Families (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008064106A1 (en) | 2008-12-19 | 2010-07-08 | Siemens Aktiengesellschaft | Method for image recording with a magnetic resonance device and magnetic resonance device |
| ES2962576T3 (en) | 2013-03-06 | 2024-03-19 | Koninklijke Philips Nv | Apparatus for determining the scanning region |
| US9471987B2 (en) * | 2013-08-09 | 2016-10-18 | Siemens Healthcare Gmbh | Automatic planning for medical imaging |
| US11300643B2 (en) * | 2016-07-11 | 2022-04-12 | Synaptive Medical Inc. | Adaptive shim coils for MR imaging |
| US11800978B2 (en) * | 2016-08-05 | 2023-10-31 | Siemens Healthcare Gmbh | Deep learning based isocenter positioning and fully automated cardiac MR exam planning |
| US10452813B2 (en) | 2016-11-17 | 2019-10-22 | Terarecon, Inc. | Medical image identification and interpretation |
| US11868852B1 (en) * | 2017-05-04 | 2024-01-09 | Amazon Technologies, Inc. | Introspection of machine learning estimations |
| WO2019118640A1 (en) * | 2017-12-13 | 2019-06-20 | Washington University | System and method for determining segments for ablation |
| US11191281B1 (en) * | 2018-01-05 | 2021-12-07 | Tyson Foods, Inc. | Method and apparatus for conveying a meat product and using an ultrasonic knife for automated cutting of meat |
| EP3557274A1 (en) * | 2018-04-19 | 2019-10-23 | Koninklijke Philips N.V. | Automatic artifact detection and pulse sequence modification in magnetic resonance imaging |
| US10872413B2 (en) * | 2018-04-20 | 2020-12-22 | iCometrue Company Ltd. | Method for data management and machine learning with fine resolution |
| US10852379B2 (en) * | 2018-06-07 | 2020-12-01 | Siemens Healthcare Gmbh | Artifact reduction by image-to-image network in magnetic resonance imaging |
| AU2019412503A1 (en) * | 2018-12-24 | 2021-07-08 | Body Composition Technologies Pty Ltd | Analysing a body |
| CA3133351A1 (en) * | 2019-03-14 | 2020-09-17 | Hyperfine, Inc. | Deep learning techniques for generating magnetic resonance images from spatial frequency data |
| US11087172B2 (en) * | 2019-12-31 | 2021-08-10 | Plus One Robotics, Inc. | Systems and methods for creating training data |
| US11651518B2 (en) * | 2021-06-03 | 2023-05-16 | Meta Platforms Technologies, Llc | System for determining an expected field of view |
| US11868672B1 (en) * | 2023-03-30 | 2024-01-09 | Brelyon, Inc. | Methods and systems for interactive displays with intelligent generative content and tandem computing |
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2020
- 2020-05-16 CN CN202080036637.XA patent/CN113853526A/en active Pending
- 2020-05-16 JP JP2021568555A patent/JP7391112B2/en active Active
- 2020-05-16 WO PCT/EP2020/063744 patent/WO2020234204A1/en active Application Filing
- 2020-05-16 EP EP20725193.5A patent/EP3969923A1/en active Pending
- 2020-05-16 US US17/610,455 patent/US20220225888A1/en active Pending
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