CN113196340B - 用于成像的方法、非瞬态计算机可读介质和系统 - Google Patents

用于成像的方法、非瞬态计算机可读介质和系统 Download PDF

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CN113196340B
CN113196340B CN201980081881.5A CN201980081881A CN113196340B CN 113196340 B CN113196340 B CN 113196340B CN 201980081881 A CN201980081881 A CN 201980081881A CN 113196340 B CN113196340 B CN 113196340B
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lesion
suv
image
value
emission
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CN113196340A (zh
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A·G·格迪克
张滨
A·安德烈耶夫
A·F·萨洛蒙
Y·毛
白传勇
胡志强
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Koninklijke Philips NV
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T12/00—Tomographic reconstruction from projections
    • G06T12/30—Image post-processing, e.g. metal artefact correction
    • 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]
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
    • G06N7/04—Physical realisation
    • G06N7/046—Implementation by means of a neural network
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T12/00—Tomographic reconstruction from projections
    • G06T12/10—Image preprocessing, e.g. calibration, positioning of sources or scatter correction
    • 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
    • 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
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06T2207/30—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
    • G06T2211/00—Image generation
    • G06T2211/40—Computed tomography

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Algebra (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Nuclear Medicine (AREA)
CN201980081881.5A 2018-11-13 2019-11-08 用于成像的方法、非瞬态计算机可读介质和系统 Active CN113196340B (zh)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201862760124P 2018-11-13 2018-11-13
US62/760,124 2018-11-13
PCT/EP2019/080628 WO2020099250A1 (en) 2018-11-13 2019-11-08 Artificial intelligence (ai)-based standardized uptake value (suv) correction and variation assessment for positron emission tomography (pet)

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CN113196340A CN113196340A (zh) 2021-07-30
CN113196340B true CN113196340B (zh) 2025-02-18

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US (1) US12346998B2 (enExample)
EP (1) EP3881289A1 (enExample)
JP (1) JP7359851B2 (enExample)
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WO (1) WO2020099250A1 (enExample)

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12346998B2 (en) * 2018-11-13 2025-07-01 Koninklijke Philips N.V. Artificial intelligence (AI)-based standardized uptake value (SUV) correction and variation assessment for positron emission tomography (PET)
US11429840B2 (en) * 2019-09-25 2022-08-30 Siemens Medical Solutions Usa, Inc. Learning parameter invariant image reconstruction embedding for AI systems
WO2021159236A1 (zh) * 2020-02-10 2021-08-19 深圳先进技术研究院 基于非衰减校正pet图像生成合成pet-ct图像的方法和系统
CN113505527B (zh) * 2021-06-24 2022-10-04 中国科学院计算机网络信息中心 一种基于数据驱动的材料性质预测方法及系统
CN114358285B (zh) * 2022-01-11 2025-04-29 浙江大学 一种基于流模型的pet系统衰减校正方法
WO2023149174A1 (ja) * 2022-02-02 2023-08-10 ソニーグループ株式会社 情報処理装置、情報処理方法及びプログラム
KR102784862B1 (ko) * 2022-05-18 2025-03-21 서울대학교산학협력단 복셀 기반 방사선 선량 평가 방법 및 장치
CN116228909A (zh) * 2023-03-10 2023-06-06 南京理工大学 基于深度卷积神经网络的pet系统晶间散射校正方法
US20250225698A1 (en) * 2024-01-10 2025-07-10 Siemens Medical Solutions Usa, Inc. Methods and apparatus for generating images for an uptake time using machine learning based processes
CN118151585B (zh) * 2024-03-12 2024-10-08 河北安迪科正电子技术有限公司 一种基于人工智能的正电子设备自动化控制系统及方法
US20250329070A1 (en) * 2024-04-22 2025-10-23 GE Precision Healthcare LLC Data-driven system and method to access and correct system responses
CN120411688B (zh) * 2025-07-01 2025-11-04 中国科学院自动化研究所 基于小样本的bad识别模型构建方法及bad识别方法
CN120726053B (zh) * 2025-09-01 2025-11-21 川北医学院附属医院 用于神经系统疾病的核医学功能成像分析方法及系统

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103339652A (zh) * 2010-12-01 2013-10-02 皇家飞利浦电子股份有限公司 靠近伪影源的诊断图像特征

Family Cites Families (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2007144703A (ru) 2005-05-03 2009-06-10 Конинклейке Филипс Электроникс Н.В. (Nl) Виртуальная количественная оценка очага поражения
US8026488B2 (en) * 2008-01-24 2011-09-27 Case Western Reserve University Methods for positive emission tomography (PET) target image segmentation
RU2526717C2 (ru) 2009-01-22 2014-08-27 Конинклейке Филипс Электроникс, Н.В. Попиксельное и поэлементное гибридное объединение для изображений позитрон-эмиссионной томографии (рет)/компьютерной томографии (ст)
EP2457216B1 (en) 2009-07-20 2017-11-08 Koninklijke Philips N.V. Anatomy modeling for tumor region of interest definition
US8620053B2 (en) * 2009-11-04 2013-12-31 Siemens Medical Solutions Usa, Inc. Completion of truncated attenuation maps using maximum likelihood estimation of attenuation and activity (MLAA)
US9436989B2 (en) * 2011-06-03 2016-09-06 Bayer Healthcare Llc System and method for rapid quantitative dynamic molecular imaging scans
US9256967B2 (en) * 2012-11-02 2016-02-09 General Electric Company Systems and methods for partial volume correction in PET penalized-likelihood image reconstruction
EP3996039A1 (en) * 2014-10-17 2022-05-11 Stichting Maastricht Radiation Oncology "Maastro-Clinic" Image analysis method supporting illness development prediction for a neoplasm in a human or animal body
CN108292443A (zh) * 2015-11-20 2018-07-17 皇家飞利浦有限公司 使用病变代理的pet图像重建和处理
US10311560B2 (en) 2016-09-07 2019-06-04 Huazhong University Of Science And Technology Method and system for estimating blur kernel size
CN107123095B (zh) * 2017-04-01 2020-03-31 上海联影医疗科技有限公司 一种pet图像重建方法、成像系统
US10475214B2 (en) * 2017-04-05 2019-11-12 General Electric Company Tomographic reconstruction based on deep learning
JP7326160B2 (ja) * 2017-05-01 2023-08-15 コーニンクレッカ フィリップス エヌ ヴェ 定量的分子撮像のための正確なハイブリッドデータセットの生成
US20200175732A1 (en) * 2017-06-02 2020-06-04 Koninklijke Philips N.V. Systems and methods to provide confidence values as a measure of quantitative assurance for iteratively reconstructed images in emission tomography
CN107403201A (zh) * 2017-08-11 2017-11-28 强深智能医疗科技（昆山）有限公司 肿瘤放射治疗靶区和危及器官智能化、自动化勾画方法
US12346998B2 (en) * 2018-11-13 2025-07-01 Koninklijke Philips N.V. Artificial intelligence (AI)-based standardized uptake value (SUV) correction and variation assessment for positron emission tomography (PET)
EP4026054A4 (en) * 2019-10-09 2022-11-30 Siemens Medical Solutions USA, Inc. IMAGE RECONSTRUCTION BY MODELING IMAGE GENERATION AS ONE OR MORE NEURAL NETWORKS
EP3901903B1 (en) * 2020-04-23 2023-06-14 Siemens Healthcare GmbH Classifying a lesion based on longitudinal studies
EP4208848A1 (en) * 2020-09-02 2023-07-12 Genentech, Inc. Connected machine-learning models with joint training for lesion detection
US20220383045A1 (en) * 2021-05-25 2022-12-01 International Business Machines Corporation Generating pseudo lesion masks from bounding box annotations

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103339652A (zh) * 2010-12-01 2013-10-02 皇家飞利浦电子股份有限公司 靠近伪影源的诊断图像特征

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Publication number Publication date
WO2020099250A8 (en) 2020-07-30
CN113196340A (zh) 2021-07-30
JP7359851B2 (ja) 2023-10-11
WO2020099250A1 (en) 2020-05-22
JP2022506395A (ja) 2022-01-17
US20210398329A1 (en) 2021-12-23
EP3881289A1 (en) 2021-09-22
US12346998B2 (en) 2025-07-01

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