JP2024507766A - 機械学習によるコントラスト強調 - Google Patents

機械学習によるコントラスト強調 Download PDF

Info

Publication number
JP2024507766A
JP2024507766A JP2023548648A JP2023548648A JP2024507766A JP 2024507766 A JP2024507766 A JP 2024507766A JP 2023548648 A JP2023548648 A JP 2023548648A JP 2023548648 A JP2023548648 A JP 2023548648A JP 2024507766 A JP2024507766 A JP 2024507766A
Authority
JP
Japan
Prior art keywords
training
image
images
contrast
machine learning
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.)
Pending
Application number
JP2023548648A
Other languages
English (en)
Japanese (ja)
Other versions
JP2024507766A5 (enExample
Inventor
リラン ゴシェン
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Publication of JP2024507766A publication Critical patent/JP2024507766A/ja
Publication of JP2024507766A5 publication Critical patent/JP2024507766A5/ja
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/084Backpropagation, e.g. using gradient descent
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/60Image enhancement or restoration using machine learning, e.g. neural networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • G06N3/0455Auto-encoder networks; Encoder-decoder networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0464Convolutional networks [CNN, ConvNet]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/047Probabilistic or stochastic networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0475Generative networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/0495Quantised networks; Sparse networks; Compressed networks
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/0895Weakly supervised learning, e.g. semi-supervised or self-supervised learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/094Adversarial learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/70Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/90Dynamic range modification of images or parts thereof
    • G06T5/92Dynamic range modification of images or parts thereof based on global image properties
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/90Dynamic range modification of images or parts thereof
    • G06T5/94Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/048Activation functions
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10081Computed x-ray tomography [CT]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • G06T2207/10088Magnetic resonance imaging [MRI]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10116X-ray image
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20081Training; Learning
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20084Artificial neural networks [ANN]
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Probability & Statistics with Applications (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Image Analysis (AREA)
JP2023548648A 2021-02-15 2022-02-14 機械学習によるコントラスト強調 Pending JP2024507766A (ja)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
EP21157112.0A EP4044109A1 (en) 2021-02-15 2021-02-15 Contrast boost by machine learning
EP21157112.0 2021-02-15
PCT/EP2022/053452 WO2022171845A1 (en) 2021-02-15 2022-02-14 Contrast boost by machine learning

Publications (2)

Publication Number Publication Date
JP2024507766A true JP2024507766A (ja) 2024-02-21
JP2024507766A5 JP2024507766A5 (enExample) 2025-02-21

Family

ID=74625880

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2023548648A Pending JP2024507766A (ja) 2021-02-15 2022-02-14 機械学習によるコントラスト強調

Country Status (5)

Country Link
US (1) US12511718B2 (enExample)
EP (2) EP4044109A1 (enExample)
JP (1) JP2024507766A (enExample)
CN (1) CN116888624A (enExample)
WO (1) WO2022171845A1 (enExample)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025249085A1 (ja) * 2024-05-27 2025-12-04 学校法人帝京大学 生成モデルの訓練方法、訓練装置、画像生成方法、画像生成装置およびプログラム

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024251601A1 (de) * 2023-06-05 2024-12-12 Bayer Aktiengesellschaft Erzeugen von künstlichen kontrastverstärkten radiologischen aufnahmen
CN116759042B (zh) * 2023-08-22 2023-12-22 之江实验室 一种基于环形一致性的反事实医疗数据生成系统及方法
EP4567716A1 (en) * 2023-12-06 2025-06-11 Bayer Aktiengesellschaft Generating synthetic representations
WO2025137799A1 (en) * 2023-12-25 2025-07-03 Bracco Imaging S.P.A. Simulating images with higher contrast-enhancement in medical applications based on inverse problem
EP4614433A1 (en) * 2024-03-08 2025-09-10 Koninklijke Philips N.V. Synthesizing contrasted x-ray-based image data
CN119991534B (zh) * 2025-04-14 2025-07-29 青岛瑞思德生物科技有限公司 医学图像低对比度区域自适应增强方法及系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019154987A1 (en) * 2018-02-12 2019-08-15 F. Hoffmann-La Roche Ag Transformation of digital pathology images
WO2019209820A1 (en) * 2018-04-26 2019-10-31 Elekta, Inc. Image enhancement using generative adversarial networks
WO2020179200A1 (ja) * 2019-03-04 2020-09-10 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 情報処理方法及び情報処理システム

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US1028282A (en) 1911-06-21 1912-06-04 Arthur Horowitz Apparatus for producing vegetable extracts.
EP2504811B1 (en) 2009-11-25 2014-06-18 Koninklijke Philips N.V. Enhanced image data/dose reduction
WO2012073140A1 (en) 2010-12-01 2012-06-07 Koninklijke Philips Electronics N.V. Contrast to noise ratio (cnr) enhancer
JP6100772B2 (ja) 2011-07-15 2017-03-22 コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. 画像処理方法及びコンピューティング装置
JP6472088B2 (ja) 2013-02-21 2019-02-20 コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. スペクトルctに関する構造伝播復元
BR112020007105A2 (pt) 2017-10-09 2020-09-24 The Board Of Trustees Of The Leland Stanford Junior University método para treinar um dispositivo de diagnóstico por imagem para realizar uma imagem para diagnóstico médico com uma dose reduzida de agente de contraste
US11222415B2 (en) 2018-04-26 2022-01-11 The Regents Of The University Of California Systems and methods for deep learning microscopy
EP3576050A1 (en) 2018-05-29 2019-12-04 Koninklijke Philips N.V. Deep anomaly detection
US11232541B2 (en) * 2018-10-08 2022-01-25 Rensselaer Polytechnic Institute CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
EP3731144A1 (en) * 2019-04-25 2020-10-28 Koninklijke Philips N.V. Deep adversarial artifact removal
US20200372301A1 (en) * 2019-05-21 2020-11-26 Retrace Labs Adversarial Defense Platform For Automated Dental Image Classification
EP3739522A1 (en) * 2019-05-17 2020-11-18 Koninklijke Philips N.V. Deep virtual contrast
CN111915513B (zh) * 2020-07-10 2022-07-26 河海大学 一种基于改进的自适应神经网络的图像去噪方法
CN112258438B (zh) * 2020-10-28 2023-07-25 清华大学深圳国际研究生院 一种基于非配对数据的ldct图像恢复方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019154987A1 (en) * 2018-02-12 2019-08-15 F. Hoffmann-La Roche Ag Transformation of digital pathology images
WO2019209820A1 (en) * 2018-04-26 2019-10-31 Elekta, Inc. Image enhancement using generative adversarial networks
WO2020179200A1 (ja) * 2019-03-04 2020-09-10 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 情報処理方法及び情報処理システム

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025249085A1 (ja) * 2024-05-27 2025-12-04 学校法人帝京大学 生成モデルの訓練方法、訓練装置、画像生成方法、画像生成装置およびプログラム

Also Published As

Publication number Publication date
EP4292043A1 (en) 2023-12-20
CN116888624A (zh) 2023-10-13
US12511718B2 (en) 2025-12-30
US20240311974A1 (en) 2024-09-19
WO2022171845A1 (en) 2022-08-18
EP4044109A1 (en) 2022-08-17

Similar Documents

Publication Publication Date Title
JP2024507767A (ja) コントラスト補正機械学習システムのための訓練データ合成器
JP2024507766A (ja) 機械学習によるコントラスト強調
US12064281B2 (en) Method and system for denoising CT images using a neural network
Zhou et al. Limited view tomographic reconstruction using a cascaded residual dense spatial-channel attention network with projection data fidelity layer
CN112969412B (zh) 深谱团注剂跟踪
JP6855223B2 (ja) 医用画像処理装置、x線コンピュータ断層撮像装置及び医用画像処理方法
Xia et al. Physics-/model-based and data-driven methods for low-dose computed tomography: A survey
CN117813055A (zh) 用于从快速spect扫描和ct图像合成spect图像的多模态和多尺度特征聚合
He et al. Downsampled imaging geometric modeling for accurate CT reconstruction via deep learning
Isola et al. Fully automatic nonrigid registration‐based local motion estimation for motion‐corrected iterative cardiac CT reconstruction
EP3739522A1 (en) Deep virtual contrast
EP3731144A1 (en) Deep adversarial artifact removal
Chen et al. DuDoCFNet: dual-domain coarse-to-fine progressive network for simultaneous denoising, limited-view reconstruction, and attenuation correction of cardiac SPECT
Xia et al. Deep residual neural network based image enhancement algorithm for low dose CT images
Li et al. Learning non-local perfusion textures for high-quality computed tomography perfusion imaging
EP4009268A1 (en) Performing denoising on an image
Zhou et al. Limited view tomographic reconstruction using a deep recurrent framework with residual dense spatial-channel attention network and sinogram consistency
Garehdaghi et al. Positron emission tomography image enhancement using magnetic resonance images and U-net structure
Cao et al. MBST-Driven 4D-CBCT reconstruction: Leveraging swin transformer and masking for robust performance
US20240095885A1 (en) Performing denoising on an image
Shen et al. Unsupervised PET reconstruction from a Bayesian perspective
US20260030819A1 (en) Cone beam artifact reduction
Chen et al. SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction
Krishnan et al. Relevancy aware cascaded generative adversarial network for LSO-transmission image denoising in CT-less PET
Yang et al. Trans ${^ 2} $-CBCT: A Dual-Transformer Framework for Sparse-View CBCT Reconstruction

Legal Events

Date Code Title Description
A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20250213

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20250213

A977 Report on retrieval

Free format text: JAPANESE INTERMEDIATE CODE: A971007

Effective date: 20260212

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20260302

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20260410