RU2018117507A - Формирование псевдо-кт по мр-данным с использованием оценки параметров ткани - Google Patents
Формирование псевдо-кт по мр-данным с использованием оценки параметров ткани Download PDFInfo
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- RU2018117507A RU2018117507A RU2018117507A RU2018117507A RU2018117507A RU 2018117507 A RU2018117507 A RU 2018117507A RU 2018117507 A RU2018117507 A RU 2018117507A RU 2018117507 A RU2018117507 A RU 2018117507A RU 2018117507 A RU2018117507 A RU 2018117507A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1039—Treatment planning systems using functional images, e.g. PET or MRI
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/51—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/032—Transmission computed tomography [CT]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
-
- 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
- G01R33/4808—Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]
- G01R33/4812—MR combined with X-ray or computed tomography [CT]
-
- 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
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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]
-
- 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
-
- 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
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Animal Behavior & Ethology (AREA)
- Databases & Information Systems (AREA)
- Quality & Reliability (AREA)
- Computational Linguistics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Pulmonology (AREA)
- High Energy & Nuclear Physics (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Radiation-Therapy Devices (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US14/881,939 US10102451B2 (en) | 2015-10-13 | 2015-10-13 | Pseudo-CT generation from MR data using tissue parameter estimation |
| US14/881,939 | 2015-10-13 | ||
| PCT/US2016/056635 WO2017066317A1 (en) | 2015-10-13 | 2016-10-12 | Pseudo-ct generation from mr data using tissue parameter estimation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| RU2018117507A true RU2018117507A (ru) | 2019-11-14 |
| RU2018117507A3 RU2018117507A3 (enExample) | 2020-02-27 |
Family
ID=57178551
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| RU2018117507A RU2018117507A (ru) | 2015-10-13 | 2016-10-12 | Формирование псевдо-кт по мр-данным с использованием оценки параметров ткани |
Country Status (7)
| Country | Link |
|---|---|
| US (2) | US10102451B2 (enExample) |
| EP (1) | EP3362146B1 (enExample) |
| JP (2) | JP7030050B2 (enExample) |
| CN (1) | CN108778416B (enExample) |
| AU (1) | AU2016339009B2 (enExample) |
| RU (1) | RU2018117507A (enExample) |
| WO (1) | WO2017066317A1 (enExample) |
Families Citing this family (45)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10102451B2 (en) * | 2015-10-13 | 2018-10-16 | Elekta, Inc. | Pseudo-CT generation from MR data using tissue parameter estimation |
| WO2017103237A1 (en) * | 2015-12-18 | 2017-06-22 | Koninklijke Philips N.V. | Method for determining a patient specific locally varying margin |
| US11139068B2 (en) * | 2016-11-04 | 2021-10-05 | The University Of North Carolina At Chapel Hill | Methods, systems, and computer readable media for smart image protocoling |
| EP3441003B1 (en) | 2017-08-08 | 2020-07-22 | Siemens Healthcare GmbH | Method for performing digital subtraction angiography, hybrid imaging device, computer program and electronically readable storage medium |
| TWI637187B (zh) * | 2017-09-19 | 2018-10-01 | 臺北榮民總醫院 | 磁共振影像的分析方法及評估放射治療風險的方法 |
| US11517197B2 (en) * | 2017-10-06 | 2022-12-06 | Canon Medical Systems Corporation | Apparatus and method for medical image reconstruction using deep learning for computed tomography (CT) image noise and artifacts reduction |
| US10803984B2 (en) | 2017-10-06 | 2020-10-13 | Canon Medical Systems Corporation | Medical image processing apparatus and medical image processing system |
| JP6810675B2 (ja) * | 2017-11-16 | 2021-01-06 | 東京エレクトロンデバイス株式会社 | 情報処理装置及びプログラム |
| US10878529B2 (en) * | 2017-12-22 | 2020-12-29 | Canon Medical Systems Corporation | Registration method and apparatus |
| US11410086B2 (en) * | 2018-02-22 | 2022-08-09 | General Electric Company | System and method for class specific deep learning |
| EP3543911A1 (en) * | 2018-03-22 | 2019-09-25 | Koninklijke Philips N.V. | Anomaly detection using magnetic resonance fingerprinting |
| US10453224B1 (en) * | 2018-03-27 | 2019-10-22 | Ziwei Lin | Pseudo-CT generation with multi-variable regression of multiple MRI scans |
| US10657410B2 (en) * | 2018-04-13 | 2020-05-19 | Siemens Healthcare Gmbh | Method and system for abnormal tissue detection using z-scores in a joint histogram |
| EP3617733A1 (en) * | 2018-08-30 | 2020-03-04 | Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. | Method and apparatus for processing magnetic resonance data using machine learning |
| US10594027B1 (en) * | 2018-08-31 | 2020-03-17 | Hughes Networks Systems, Llc | Machine learning models for detecting the causes of conditions of a satellite communication system |
| WO2020054803A1 (ja) * | 2018-09-12 | 2020-03-19 | 株式会社Splink | 診断支援システムおよび方法 |
| WO2020082207A1 (en) * | 2018-10-22 | 2020-04-30 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for attenuation correction |
| CN109697741B (zh) * | 2018-12-28 | 2023-06-16 | 上海联影智能医疗科技有限公司 | 一种pet图像重建方法、装置、设备及介质 |
| CN109785405A (zh) * | 2019-01-29 | 2019-05-21 | 子威·林 | 利用多组磁共振图像的多变量回归生成准ct图像的方法 |
| JP7195961B2 (ja) * | 2019-02-08 | 2022-12-26 | キヤノンメディカルシステムズ株式会社 | 医用処理装置、x線ctシステム及び処理プログラム |
| JP7467145B2 (ja) * | 2019-02-08 | 2024-04-15 | キヤノンメディカルシステムズ株式会社 | 放射線治療計画装置 |
| CN109978965B (zh) * | 2019-03-21 | 2025-09-30 | 江南大学 | 一种模拟ct图像生成方法、装置、计算机设备和存储介质 |
| EP3958965B1 (en) * | 2019-04-26 | 2025-11-05 | Spectronic Medical Ab | Generating synthetic electron density images from magnetic resonance images |
| CN110270015B (zh) * | 2019-05-08 | 2021-03-09 | 中国科学技术大学 | 一种基于多序列MRI的sCT生成方法 |
| EP3751579A1 (en) | 2019-06-13 | 2020-12-16 | RaySearch Laboratories AB | System and method for training a machine learning model and for providing an estimated interior image of a patient |
| US11531080B2 (en) * | 2019-07-24 | 2022-12-20 | Cypress Semiconductor Corporation | Leveraging spectral diversity for machine learning-based estimation of radio frequency signal parameters |
| JP7170897B2 (ja) | 2019-09-30 | 2022-11-14 | 富士フイルム株式会社 | 学習装置、方法およびプログラム、画像生成装置、方法およびプログラム、並びに画像生成モデル |
| US11282218B2 (en) * | 2019-11-25 | 2022-03-22 | Shanghai United Imaging Intelligence Co., Ltd. | Systems and methods for providing medical guidance using a patient depth image |
| US11838531B2 (en) * | 2019-12-06 | 2023-12-05 | Dolby Laboratories Licensing Corporation | Cascade prediction |
| US11348243B2 (en) | 2020-01-24 | 2022-05-31 | GE Precision Healthcare LLC | Systems and methods for medical image style transfer using deep neural networks |
| EP3882648B1 (en) | 2020-03-18 | 2024-09-25 | Siemens Healthineers AG | Rf coil device for an mr or mr-pet imaging modality and method to determine the position and/or orientation and/or shape of such an rf coil device |
| US11562482B2 (en) | 2020-03-30 | 2023-01-24 | Varian Medical Systems International Ag | Systems and methods for pseudo image data augmentation for training machine learning models |
| US11554496B2 (en) * | 2020-04-03 | 2023-01-17 | Fanuc Corporation | Feature detection by deep learning and vector field estimation |
| JP6989860B2 (ja) * | 2020-05-02 | 2022-01-12 | Arithmer株式会社 | 画像検査装置、画像検査方法及びプログラム |
| JP7483941B2 (ja) * | 2020-05-06 | 2024-05-15 | シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド | 低磁場mr/petイメージングのためのシステムと方法 |
| CN111626972B (zh) * | 2020-06-02 | 2023-06-02 | 上海鹰瞳医疗科技有限公司 | Ct图像重构方法、模型训练方法及设备 |
| JP7487096B2 (ja) * | 2020-12-24 | 2024-05-20 | パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカ | 制御システム及び制御方法 |
| EP4030179A1 (en) * | 2021-01-19 | 2022-07-20 | Koninklijke Philips N.V. | Method of predicting a field perturbation map for magnetic resonance imaging |
| KR102746259B1 (ko) * | 2021-03-11 | 2024-12-27 | 한국과학기술연구원 | 인공지능 기반 mri-ct 영상 변환 방법 및 이를 이용한 초음파 치료 장치 |
| CN113255756B (zh) * | 2021-05-20 | 2024-05-24 | 联仁健康医疗大数据科技股份有限公司 | 图像融合方法、装置、电子设备及存储介质 |
| CN114882996B (zh) * | 2022-03-17 | 2023-04-07 | 深圳大学 | 基于多任务学习的肝细胞癌ck19及mvi预测方法 |
| US20230377721A1 (en) * | 2022-05-19 | 2023-11-23 | Elekta Ltd. | Jointly trained machine learning models for automatic contouring in radiotherapy applications |
| US12107742B2 (en) | 2022-08-31 | 2024-10-01 | Hughes Network Systems, Llc | Machine learning to enhance satellite terminal performance |
| CN118154587A (zh) * | 2024-05-09 | 2024-06-07 | 四川省肿瘤医院 | 一种MRI-only放疗的质量控制方法 |
| CN118864288B (zh) * | 2024-09-25 | 2025-01-28 | 江西省肿瘤医院(江西省第二人民医院、江西省癌症中心) | 无监督伪ct对抗扩散模型构建方法、系统、介质及设备 |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101959454B (zh) * | 2008-03-07 | 2013-11-20 | 皇家飞利浦电子股份有限公司 | 通过磁共振图像的自动分割进行ct替代 |
| US8218848B2 (en) * | 2008-07-23 | 2012-07-10 | Siemens Aktiengesellschaft | System and method for the generation of attenuation correction maps from MR images |
| US8774482B2 (en) * | 2010-05-20 | 2014-07-08 | Siemens Aktiengesellschaft | Generating pseudo-CT image volumes from ultra-short echo time MR |
| JP2015516194A (ja) | 2012-03-29 | 2015-06-11 | コーニンクレッカ フィリップス エヌ ヴェ | 個々のピクセルもしくはボクセルに組織固有のpet減衰値を割り当てるmri方法 |
| US9135695B2 (en) * | 2012-04-04 | 2015-09-15 | Siemens Aktiengesellschaft | Method for creating attenuation correction maps for PET image reconstruction |
| US9392958B2 (en) * | 2012-05-30 | 2016-07-19 | Siemens Aktiengesellschaft | Method of attenuation correction of positron emission tomography data and combined positron emission tomography and magnetic resonance tomography system |
| FR2994481B1 (fr) * | 2012-08-07 | 2014-08-29 | Snecma | Procede de caracterisation d'un objet en materiau composite |
| US9256965B2 (en) * | 2013-01-30 | 2016-02-09 | Impac Medical Systems, Inc. | Method and apparatus for generating a derived image using images of different types |
| WO2014161766A1 (en) | 2013-04-02 | 2014-10-09 | Koninklijke Philips N.V. | Detection of bone tissue using magnetic resonance imaging |
| WO2015081079A1 (en) | 2013-11-26 | 2015-06-04 | Henry Ford Innovation Institute | Software for using magnetic resonance images to generate a synthetic computed tomography image |
| US10029121B2 (en) | 2013-12-31 | 2018-07-24 | The Medical College Of Wisconsin, Inc. | System and method for producing synthetic images |
| CN106133790B (zh) | 2014-03-28 | 2020-09-29 | 皇家飞利浦有限公司 | 用于在组织种类分离的帮助下基于磁共振图像生成一幅或多幅计算机断层摄影图像的方法和设备 |
| CN106462948B (zh) | 2014-04-01 | 2019-06-25 | 皇家飞利浦有限公司 | 一种估计伪亨氏单位值的方法 |
| US10307108B2 (en) * | 2015-10-13 | 2019-06-04 | Elekta, Inc. | Pseudo-CT generation from MR data using a feature regression model |
| US10102451B2 (en) | 2015-10-13 | 2018-10-16 | Elekta, Inc. | Pseudo-CT generation from MR data using tissue parameter estimation |
-
2015
- 2015-10-13 US US14/881,939 patent/US10102451B2/en active Active
-
2016
- 2016-10-12 RU RU2018117507A patent/RU2018117507A/ru not_active Application Discontinuation
- 2016-10-12 CN CN201680072849.7A patent/CN108778416B/zh active Active
- 2016-10-12 WO PCT/US2016/056635 patent/WO2017066317A1/en not_active Ceased
- 2016-10-12 AU AU2016339009A patent/AU2016339009B2/en active Active
- 2016-10-12 EP EP16784722.7A patent/EP3362146B1/en active Active
- 2016-10-12 JP JP2018519467A patent/JP7030050B2/ja active Active
-
2018
- 2018-09-05 US US16/122,331 patent/US10664723B2/en active Active
-
2020
- 2020-12-25 JP JP2020216068A patent/JP2021049424A/ja active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| US10102451B2 (en) | 2018-10-16 |
| US10664723B2 (en) | 2020-05-26 |
| EP3362146B1 (en) | 2020-03-25 |
| JP2021049424A (ja) | 2021-04-01 |
| EP3362146A1 (en) | 2018-08-22 |
| US20190042885A1 (en) | 2019-02-07 |
| US20170103287A1 (en) | 2017-04-13 |
| AU2016339009B2 (en) | 2020-02-06 |
| CN108778416A (zh) | 2018-11-09 |
| CN108778416B (zh) | 2020-06-19 |
| JP7030050B2 (ja) | 2022-03-04 |
| WO2017066317A1 (en) | 2017-04-20 |
| JP2018535732A (ja) | 2018-12-06 |
| RU2018117507A3 (enExample) | 2020-02-27 |
| AU2016339009A1 (en) | 2018-04-26 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FA94 | Acknowledgement of application withdrawn (non-payment of fees) |
Effective date: 20210428 |