Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.
Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.
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DOI：
10.3390/bioengineering10091012
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发表时间：
2023-08-26
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影响因子：
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磁共振成像(MRI)是一种基本的医学成像方式，它提供了出色的软组织对比度和高分辨率的人体图像，使我们能够了解有关形态、结构完整性和生理过程的详细信息。然而，核磁共振检查通常需要很长的获取时间。并行MRI和压缩感知(CS)等方法通过欠采样获取较少的数据，从而显著缩短了MRI的采集时间。最近，通过将深度学习(DL)模型与这些欠采样方法相结合，快速磁共振成像的最新技术得到了重新定义。这篇系统的文献综述(SLR)全面分析了深层MRI重建模型，强调了最近提出的方法的关键要素，并强调了它们的优点和缺点。这项SLR涉及从各种数据库中搜索和选择相关研究，包括科学网和SCOPUS，然后使用系统审查和荟萃分析(PRISMA)指南的首选报告项目进行严格的筛选和数据提取过程。它侧重于各种技术，例如残差学习、使用编解码器的图像表示、数据一致性层、展开的网络、学习的激活、注意力模块、即插即用先验、扩散模型和贝叶斯方法。本SLR还讨论了损失函数的使用和对抗性网络的训练以增强深层MRI重建方法。此外，我们还探讨了MRI重建的各种应用，包括非笛卡尔重建、超分辨率重建、动态MRI、结合线圈灵敏度和采样的联合学习重建、定量标测和MR指纹识别。本文还讨论了研究问题，提供了对未来方向的见解，并强调健壮的泛化和人工产物处理。因此，这台SLR是推进快速MRI的宝贵资源，指导MRI重建的研究和开发工作，以获得更好的图像质量和更快的数据采集。
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.
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DOI：
10.1109/isbi52829.2022.9761497
发表时间：
2022-03
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
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作者：
Chen, Zihao;Chen, Yuhua;Xie, Yibin;Li, Debiao;Christodoulou, Anthony G.
通讯作者：
Christodoulou, Anthony G.
影响因子：
3.3
作者：
Chaudhari AS;Fang Z;Kogan F;Wood J;Stevens KJ;Gibbons EK;Lee JH;Gold GE;Hargreaves BA
通讯作者：
Hargreaves BA
DOI：
10.3390/bioengineering10040475
发表时间：
2023-04-14
期刊：
Bioengineering (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
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影响因子：
12
作者：
Ben Yedder, Hanene;Cardoen, Ben;Hamarneh, Ghassan
通讯作者：
Hamarneh, Ghassan
使用增强型递归残差网络进行欠采样 MR 图像重建
影响因子：
2.2
作者：
Bao, Lijun;Ye, Fuze;Chen, Zhong
通讯作者：
Chen, Zhong