• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

深度学习在医学影像合成中的应用综述及其临床应用。

A review on medical imaging synthesis using deep learning and its clinical applications.

机构信息

Department of Radiation Oncology, Emory University, Atlanta, GA, USA.

Winship Cancer Institute, Emory University, Atlanta, GA, USA.

出版信息

J Appl Clin Med Phys. 2021 Jan;22(1):11-36. doi: 10.1002/acm2.13121. Epub 2020 Dec 11.

DOI:10.1002/acm2.13121
PMID:33305538
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7856512/
Abstract

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

摘要

本文回顾了基于深度学习的医学影像合成及其临床应用研究。具体来说,我们通过列出并突出所提出的方法、研究设计以及在具有代表性的研究中的相关临床应用报告的性能,总结了基于深度学习的跨模态和同模态图像合成方法的最新进展。然后,通过讨论总结了所回顾研究中的挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/f5a63e6d97a0/ACM2-22-11-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/208603a862de/ACM2-22-11-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/ed0e4e9f1d62/ACM2-22-11-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/f5a63e6d97a0/ACM2-22-11-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/208603a862de/ACM2-22-11-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/ed0e4e9f1d62/ACM2-22-11-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf1a/7856512/f5a63e6d97a0/ACM2-22-11-g003.jpg

相似文献

1
A review on medical imaging synthesis using deep learning and its clinical applications.深度学习在医学影像合成中的应用综述及其临床应用。
J Appl Clin Med Phys. 2021 Jan;22(1):11-36. doi: 10.1002/acm2.13121. Epub 2020 Dec 11.
2
Deep learning based synthesis of MRI, CT and PET: Review and analysis.基于深度学习的 MRI、CT 和 PET 合成:综述与分析。
Med Image Anal. 2024 Feb;92:103046. doi: 10.1016/j.media.2023.103046. Epub 2023 Dec 1.
3
Recent advances and clinical applications of deep learning in medical image analysis.深度学习在医学图像分析中的最新进展和临床应用。
Med Image Anal. 2022 Jul;79:102444. doi: 10.1016/j.media.2022.102444. Epub 2022 Apr 4.
4
Deep learning in medical image registration: a review.深度学习在医学图像配准中的应用:综述。
Phys Med Biol. 2020 Oct 22;65(20):20TR01. doi: 10.1088/1361-6560/ab843e.
5
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.深度学习技术在 PET/CT 成像中的应用:从能谱到图像空间的全面综述。
Comput Methods Programs Biomed. 2024 Jan;243:107880. doi: 10.1016/j.cmpb.2023.107880. Epub 2023 Oct 21.
6
Deep Learning: A Breakthrough in Medical Imaging.深度学习:医学影像的突破。
Curr Med Imaging. 2020;16(8):946-956. doi: 10.2174/1573405615666191219100824.
7
[Research progress and challenges of deep learning in medical image registration].[深度学习在医学图像配准中的研究进展与挑战]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2019 Aug 25;36(4):677-683. doi: 10.7507/1001-5515.201810004.
8
Medical Image Synthesis via Deep Learning.基于深度学习的医学图像合成。
Adv Exp Med Biol. 2020;1213:23-44. doi: 10.1007/978-3-030-33128-3_2.
9
[Research status and outlook of deep learning in oral and maxillofacial medical imaging].[深度学习在口腔颌面医学影像中的研究现状与展望]
Zhonghua Kou Qiang Yi Xue Za Zhi. 2023 Jun 9;58(6):533-539. doi: 10.3760/cma.j.cn112144-20230405-00138.
10
A Review of deep learning methods for denoising of medical low-dose CT images.深度学习方法在医学低剂量 CT 图像去噪中的研究进展。
Comput Biol Med. 2024 Mar;171:108112. doi: 10.1016/j.compbiomed.2024.108112. Epub 2024 Feb 15.

引用本文的文献

1
Synthesizing [F]PSMA-1007 PET bone images from CT images with GAN for early detection of prostate cancer bone metastases: a pilot validation study.利用生成对抗网络(GAN)从CT图像合成[F]PSMA - 1007 PET骨图像用于前列腺癌骨转移的早期检测:一项初步验证研究。
BMC Cancer. 2025 May 21;25(1):907. doi: 10.1186/s12885-025-14301-x.
2
Clinical implementation of patient-specific quality assurance for synthetic computed tomography.针对合成计算机断层扫描的患者特异性质量保证的临床实施。
Phys Imaging Radiat Oncol. 2025 Apr 4;34:100764. doi: 10.1016/j.phro.2025.100764. eCollection 2025 Apr.
3
Diffusion MRI GAN synthesizing fibre orientation distribution data using generative adversarial networks.

本文引用的文献

1
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.与用于低剂量CT图像重建的商业算法相比,模块化深度神经网络的竞争性能。
Nat Mach Intell. 2019 Jun;1(6):269-276. doi: 10.1038/s42256-019-0057-9. Epub 2019 Jun 10.
2
PET Image Denoising Using a Deep Neural Network Through Fine Tuning.通过微调深度学习网络实现PET图像去噪
IEEE Trans Radiat Plasma Med Sci. 2019 Mar;3(2):153-161. doi: 10.1109/TRPMS.2018.2877644. Epub 2018 Oct 23.
3
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.
使用生成对抗网络的扩散磁共振成像生成对抗网络合成纤维取向分布数据。
Commun Biol. 2025 Mar 28;8(1):512. doi: 10.1038/s42003-025-07936-w.
4
Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants.人工智能在预测高级别胶质瘤生存中的应用:涉及79638名参与者的系统评价和荟萃分析
Neurosurg Rev. 2025 Feb 15;48(1):240. doi: 10.1007/s10143-025-03419-y.
5
Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.用于低剂量PET图像重建的扩散多尺度生成对抗网络
Biomed Eng Online. 2025 Feb 9;24(1):16. doi: 10.1186/s12938-025-01348-x.
6
Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.利用基于排名的评分将放射科医生的知识纳入机器学习的MRI质量指标中。
J Magn Reson Imaging. 2025 Jun;61(6):2572-2584. doi: 10.1002/jmri.29672. Epub 2024 Dec 17.
7
SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.SMART-PET:一种用于重建低计数[18F]-FDG-PET脑成像的自相似感知生成对抗框架。
Front Nucl Med. 2024 Nov 19;4:1469490. doi: 10.3389/fnume.2024.1469490. eCollection 2024.
8
Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.使用特定患者的扩散模型来生成基于CBCT的合成CT,用于CBCT引导的自适应放疗。
Med Phys. 2025 Jan;52(1):471-480. doi: 10.1002/mp.17463. Epub 2024 Oct 14.
9
Deep learning for the harmonization of structural MRI scans: a survey.深度学习在结构磁共振成像扫描配准中的应用:综述。
Biomed Eng Online. 2024 Aug 31;23(1):90. doi: 10.1186/s12938-024-01280-6.
10
Artificial intelligence-based motion tracking in cancer radiotherapy: A review.基于人工智能的癌症放射治疗中的运动跟踪:综述。
J Appl Clin Med Phys. 2024 Nov;25(11):e14500. doi: 10.1002/acm2.14500. Epub 2024 Aug 28.
机器学习在定量 PET 中的应用:衰减校正和低计数图像重建方法综述。
Phys Med. 2020 Aug;76:294-306. doi: 10.1016/j.ejmp.2020.07.028. Epub 2020 Jul 29.
4
Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy.比较适用于自适应质子治疗中质子剂量计算的基于 CBCT 的合成 CT 方法。
Phys Med Biol. 2020 Apr 28;65(9):095002. doi: 10.1088/1361-6560/ab7d54.
5
CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.基于 CBCT 的深度注意力循环生成对抗网络的胰腺自适应放疗的合成 CT 生成。
Med Phys. 2020 Jun;47(6):2472-2483. doi: 10.1002/mp.14121. Epub 2020 Mar 28.
6
Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains.基于深度学习的空间域和小波域的 3T MRI 向 7T MRI 的合成。
Med Image Anal. 2020 May;62:101663. doi: 10.1016/j.media.2020.101663. Epub 2020 Feb 19.
7
MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network.使用条件生成对抗网络对盆腔区域多中心数据进行磁共振到 CT 的合成。
Phys Med Biol. 2020 Apr 2;65(7):075002. doi: 10.1088/1361-6560/ab7633.
8
Medical Image Synthesis via Deep Learning.基于深度学习的医学图像合成。
Adv Exp Med Biol. 2020;1213:23-44. doi: 10.1007/978-3-030-33128-3_2.
9
Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.基于多序列磁共振图像的生成对抗网络合成 CT 在头颈部 MRI 引导放疗中的应用。
Med Phys. 2020 Apr;47(4):1880-1894. doi: 10.1002/mp.14075. Epub 2020 Feb 26.
10
Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.基于新型多通道多路径条件生成对抗网络的多参数 MRI 伪 CT 生成用于鼻咽癌患者。
Med Phys. 2020 Apr;47(4):1750-1762. doi: 10.1002/mp.14062. Epub 2020 Feb 21.