AU2016338923B2 - Pseudo-CT generation from MR data using a feature regression model - Google Patents

Pseudo-CT generation from MR data using a feature regression model Download PDF

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AU2016338923B2
AU2016338923B2 AU2016338923A AU2016338923A AU2016338923B2 AU 2016338923 B2 AU2016338923 B2 AU 2016338923B2 AU 2016338923 A AU2016338923 A AU 2016338923A AU 2016338923 A AU2016338923 A AU 2016338923A AU 2016338923 B2 AU2016338923 B2 AU 2016338923B2
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pseudo
features
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parameter
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AU2016338923A1 (en
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Xiao HAN
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Elekta Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room
    • A61B5/0035—Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
    • 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
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/60—Image enhancement or restoration using machine learning, e.g. neural networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/37—Surgical systems with images on a monitor during operation
    • A61B2090/376—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy
    • A61B2090/3762—Surgical systems with images on a monitor during operation using X-rays, e.g. fluoroscopy using computed tomography systems [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Biomedical Technology (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Artificial Intelligence (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • General Physics & Mathematics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Physiology (AREA)
  • Psychiatry (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Radiation-Therapy Devices (AREA)
AU2016338923A 2015-10-13 2016-10-12 Pseudo-CT generation from MR data using a feature regression model Active AU2016338923B2 (en)

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Application Number Priority Date Filing Date Title
US14/881,903 US10307108B2 (en) 2015-10-13 2015-10-13 Pseudo-CT generation from MR data using a feature regression model
US14/881,903 2015-10-13
PCT/US2016/056538 WO2017066247A1 (en) 2015-10-13 2016-10-12 Pseudo-ct generation from mr data using a feature regression model

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AU2016338923A1 AU2016338923A1 (en) 2018-04-26
AU2016338923B2 true AU2016338923B2 (en) 2019-06-27

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EP (2) EP3362984B1 (enExample)
JP (1) JP6567179B2 (enExample)
CN (1) CN108770373B (enExample)
AU (1) AU2016338923B2 (enExample)
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Families Citing this family (40)

* Cited by examiner, † Cited by third party
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
US10307108B2 (en) 2015-10-13 2019-06-04 Elekta, Inc. Pseudo-CT generation from MR data using a feature regression model
US10792515B2 (en) * 2015-12-18 2020-10-06 Koninklijke Philips N.V. Method for determining a patient specific locally varying margin
WO2017141317A1 (ja) * 2016-02-15 2017-08-24 三菱電機株式会社 音響信号強調装置
US10794977B2 (en) * 2016-06-23 2020-10-06 Siemens Healthcare Gmbh System and method for normalized reference database for MR images via autoencoders
RU2698997C1 (ru) * 2016-09-06 2019-09-02 Электа, Инк. Нейронная сеть для генерации синтетических медицинских изображений
US11478212B2 (en) * 2017-02-16 2022-10-25 Siemens Healthcare Gmbh Method for controlling scanner by estimating patient internal anatomical structures from surface data using body-surface and organ-surface latent variables
EP3631808B1 (en) * 2017-05-31 2024-09-11 Koninklijke Philips N.V. Machine learning on raw medical imaging data for clinical decision support
US11276208B2 (en) * 2017-06-12 2022-03-15 The Research Foundation For The State University Of New York System, method and computer-accessible medium for ultralow dose computed tomography image reconstruction
US10753997B2 (en) * 2017-08-10 2020-08-25 Siemens Healthcare Gmbh Image standardization using generative adversarial networks
CN108416846A (zh) * 2018-03-16 2018-08-17 北京邮电大学 一种无标识三维注册算法
EP3543911A1 (en) * 2018-03-22 2019-09-25 Koninklijke Philips N.V. Anomaly detection using magnetic resonance fingerprinting
EP3583982A1 (en) * 2018-06-18 2019-12-25 Koninklijke Philips N.V. Therapy planning device
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
EP3633622A1 (en) * 2018-10-02 2020-04-08 Koninklijke Philips N.V. Generation of pseudo radiographic images from optical images
CN109710793B (zh) * 2018-12-25 2021-08-17 科大讯飞股份有限公司 一种哈希参数确定方法、装置、设备及存储介质
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
US10467504B1 (en) * 2019-02-08 2019-11-05 Adhark, Inc. Systems, methods, and storage media for evaluating digital images
US11531840B2 (en) 2019-02-08 2022-12-20 Vizit Labs, Inc. Systems, methods, and storage media for training a model for image evaluation
CN109918519A (zh) * 2019-02-26 2019-06-21 重庆善功科技有限公司 一种面向海量延时摄影胚胎图像存储与查询的方法及系统
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
CN109893784A (zh) * 2019-04-19 2019-06-18 深圳先进技术研究院 一种实现超声穿颅聚焦的方法以及电子设备
WO2020218967A1 (en) * 2019-04-26 2020-10-29 Spectronic Ab Generating synthetic electron density images from magnetic resonance images
CN110084318B (zh) * 2019-05-07 2020-10-02 哈尔滨理工大学 一种结合卷积神经网络和梯度提升树的图像识别方法
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
JP7451293B2 (ja) * 2019-06-13 2024-03-18 キヤノンメディカルシステムズ株式会社 放射線治療システム
US11024034B2 (en) * 2019-07-02 2021-06-01 Acist Medical Systems, Inc. Image segmentation confidence determination
GB2586791B (en) * 2019-08-30 2022-11-16 Elekta ltd Pseudo-CT image generation
EP3828579A1 (en) * 2019-11-28 2021-06-02 Koninklijke Philips N.V. Adaptive reconstruction of magnetic resonance images
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
US12008695B2 (en) * 2020-09-25 2024-06-11 GE Precision Healthcare LLC Methods and systems for translating magnetic resonance images to pseudo computed tomography images
CN112435554B (zh) * 2020-11-25 2022-04-12 皖南医学院 一种ct教学模拟系统及其控制方法
CN112509022A (zh) * 2020-12-17 2021-03-16 安徽埃克索医疗机器人有限公司 一种术前三维影像与术中透视图像的无标定物配准方法
CN114692665B (zh) * 2020-12-25 2024-05-24 西南电子技术研究所（中国电子科技集团公司第十研究所） 基于度量学习的辐射源开集个体识别方法
US11966454B2 (en) * 2021-10-28 2024-04-23 Shanghai United Imaging Intelligence Co., Ltd. Self-contrastive learning for image processing
CN114190889A (zh) * 2021-11-19 2022-03-18 上海联影智能医疗科技有限公司 心电信号的分类方法、系统、电子设备及可读存储介质
CN114638745B (zh) * 2022-03-16 2023-08-18 江南大学 一种基于多借鉴信息的医学影像智能转换方法
US20250345638A1 (en) * 2022-05-01 2025-11-13 The General Hospital Corporation System for and method of planning and real-time navigation for transcranial focused ultrasound stimulation
US20240296527A1 (en) * 2023-03-02 2024-09-05 GE Precision Healthcare LLC Reducing noise in ct images using synthetic data
CN120125505B (zh) * 2025-01-27 2025-09-16 中国人民解放军总医院第四医学中心 一种基于roi-hu数据库构建骨骼预测模型的方法和应用

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110286649A1 (en) * 2010-05-20 2011-11-24 Siemens Corporation Generating pseudo-ct image volumes from ultra-short echo time mr
US20140212013A1 (en) * 2013-01-30 2014-07-31 Impac Medical Systems, Inc. Method and Apparatus for Generating a Derived Image Using Images of Different Types

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1271570C (zh) * 2004-06-17 2006-08-23 上海交通大学 核磁共振多谱图像分割方法
CN101061490A (zh) * 2004-11-19 2007-10-24 皇家飞利浦电子股份有限公司 利用支持向量机（svm）在计算机辅助检测（cad）中进行假阳性降低的系统和方法
EP2053972B1 (en) * 2006-08-17 2013-09-11 Koninklijke Philips Electronics N.V. Computed tomography image acquisition
US8068654B2 (en) * 2007-02-02 2011-11-29 Siemens Akteingesellschaft Method and system for detection and registration of 3D objects using incremental parameter learning
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
US9123095B2 (en) * 2008-09-29 2015-09-01 Koninklijke Philips N.V. Method for increasing the robustness of computer-aided diagnosis to image processing uncertainties
DE102008058488B4 (de) * 2008-11-21 2018-09-20 Siemens Healthcare Gmbh Verfahren und Vorrichtung zur Aufbereitung von kombinierten MR-Emissionstomographieaufnahmen
WO2011015957A1 (en) * 2009-08-06 2011-02-10 Koninklijke Philips Electronics N.V. Method and apparatus for generating computed tomography images with offset detector geometries
US8706668B2 (en) * 2010-06-02 2014-04-22 Nec Laboratories America, Inc. Feature set embedding for incomplete data
WO2013049818A1 (en) * 2011-09-30 2013-04-04 Cincinnati Children's Hospital Medical Center Method for consistent and verifiable optimization of computed tomography (ct) radiation dose
JP5966112B1 (ja) * 2013-04-02 2016-08-10 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 磁気共鳴画像法を使用した骨組織の検出
US9235781B2 (en) * 2013-08-09 2016-01-12 Kabushiki Kaisha Toshiba Method of, and apparatus for, landmark location
US10307108B2 (en) 2015-10-13 2019-06-04 Elekta, Inc. Pseudo-CT generation from MR data using a feature regression model
EP4212057B1 (en) 2016-10-26 2025-08-13 Nike Innovate C.V. Deformable lace guides for automated footwear platform
KR102187717B1 (ko) 2017-03-15 2020-12-07 나이키 이노베이트 씨.브이. 케이블 및 갑피 텐셔너를 구비하는 자동화된 신발

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110286649A1 (en) * 2010-05-20 2011-11-24 Siemens Corporation Generating pseudo-ct image volumes from ultra-short echo time mr
US20140212013A1 (en) * 2013-01-30 2014-07-31 Impac Medical Systems, Inc. Method and Apparatus for Generating a Derived Image Using Images of Different Types

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US11944463B2 (en) 2024-04-02
US20190209099A1 (en) 2019-07-11
CN108770373B (zh) 2022-08-19
JP2018530401A (ja) 2018-10-18
US11234654B2 (en) 2022-02-01
EP3940624B1 (en) 2025-03-26
JP6567179B2 (ja) 2019-08-28
US20170100078A1 (en) 2017-04-13
EP3940624A1 (en) 2022-01-19
EP3362984B1 (en) 2023-07-12
US20220079529A1 (en) 2022-03-17
WO2017066247A1 (en) 2017-04-20
AU2016338923A1 (en) 2018-04-26
CN108770373A (zh) 2018-11-06
EP3362984A1 (en) 2018-08-22
US10307108B2 (en) 2019-06-04
RU2703344C1 (ru) 2019-10-16

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