A comprehensive deep learning framework for MRI reconstruction

用于 MRI 重建的综合深度学习框架

• 批准号: 10608060
• 负责人: Rizwan Ahmad
• 金额: $ 56.92万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608060
• 关键词: 3-Dimensional; 4D MRI; Acceleration; Address; Adoption; Adult; Algorithms; Anesthesia procedures; Architecture; Awareness; Brain; Brain Neoplasms; Brain imaging; Breathing; Cardiac; Child; Childhood; Clinical; Data; Development; Diagnosis; Disease; Evaluation; Exhibits; Formulation; Goals; Headache; Heart; Heart Diseases; Image; Imaging Device; Immune; Investigation; Learning; Magnetic Resonance Imaging; Maps; Methods; Modeling; Motion; Network-based; Patients; Pattern; Performance; Phase; Physics; Play; Predisposition; Process; Pythons; Recovery; Rest; Sampling; Scanning; Sedation procedure; Speed; Structure; Techniques; Technology; Testing; Time; Training; Variant; Work; cardiovascular imaging; computer framework; convolutional neural network; cost; data acquisition; data space; deep learning; denoising; design; diagnostic value; heart imaging; high dimensionality; image reconstruction; imaging modality; improved; learning strategy; musculoskeletal imaging; neural network architecture; novel; pediatric patients; prospective; real-time images; reconstruction; repository

PROJECT SUMMARY/ABSTRACT The primary goal of this investigation is to develop and validate a comprehensive, robust deep learning (DL) framework that improves MRI reconstruction beyond the limits of existing technology. The proposed framework uses “plug-and-play” algorithms to combine physics-driven MR acquisition models with state-of-the-art learned image models, which are instantiated by image denoising subroutines. To fully exploit the rich structure of MR images, we propose to use DL-based denoisers that are trained in an application-specific manner. The proposed framework, termed PnP-DL, offers advantages over other existing DL methods, as well as compressed sensing (CS). Compared to existing DL methods for MRI reconstruction, PnP-DL is more immune to inevitable variations in the forward model, such as changes in the coil sensitivities or undersampling pattern, allowing it to generalize across applications and acquisition settings. Compared to CS, PnP-DL recovers images faster, with higher quality, and with potentially superior diagnostic value. Our preliminary results highlight the potential of PnP-DL to advance MRI technology. In this work, we will fur- ther develop PnP-DL and validate it in these major applications: cardiac cine, 2D brain, and 3D brain imaging. In Aim 1, we will train and optimize convolutional neural network-based application-specific denoisers for the above-mentioned applications. The denoiser with the best denoising performance will be selected for further investigation. In Aim 2, we will develop and compare different PnP algorithms. The algorithm yielding the best combination of reconstruction accuracy and computational speed will be implemented in Gadgetron for inline processing. In Aim 3, we will compare the performance of PnP-DL to other state-of-the-art methods using retro- spectively undersampled data. This study will demonstrate that, in terms of image quality, PnP-DL is superior to CS and existing DL methods and, despite higher acceleration, is non-inferior to parallel MRI with rate-2 acceler- ation. In Aim 4, we will evaluate the performance of PnP-DL using prospectively undersampled data from adult and pediatric patients. Successful completion of this project will demonstrate that PnP-DL outperforms state- of-the-art methods in terms of image quality while exhibiting a level of robustness and broad applicability that has eluded other DL-based MRI reconstruction methods. The acceleration and image quality improvement afforded by these developments will benefit almost all MRI applications, including pediatric imaging, where reducing sedation is a pressing need, and high-dimensional imaging applications (e.g., whole-heart 4D flow imaging), which are too slow for routine clinical use.

项目摘要/摘要 这项调查的主要目的是开发和验证全面，健壮的深度学习（DL） 改善MRI重建超出现有技术限制的框架。提出的框架 使用“插件”算法将物理驱动的MR采集模型与最先进的学习 图像模型，通过图像deo deo subroutines实例化。充分探索MR的丰富结构 图像，我们建议使用以应用特定方式训练的基于DL的DeNoiser。 称为PNP-DL的框架具有比其他现有DL方法的优点，以及压缩感应的 （CS）。与现有的MRI重建方法相比，PNP-DL更不可避免地不可避免地变化 在正向模型中，例如线圈灵敏度的变化或不足采样模式，使其概括 跨应用程序和收购设置。与CS相比，PNP-DL恢复图像的速度更高，质量更高， 并具有卓越的诊断价值。 我们的初步结果凸显了PNP-DL推进MRI技术的潜力。在这项工作中，我们将 在这些主要应用中，开发PNP-DL并验证它：心脏Cine，2D脑和3D脑成像。 在AIM 1中，我们将培训和优化基于卷积神经网络的应用程序特异性代码器 上述申请。将选择具有最佳Denoising性能的DeNoiser进一步选择 投资。在AIM 2中，我们将开发和比较不同的PNP算法。算法产生最好的 在Gadgetron中将实现重建精度和计算速度的组合 加工。在AIM 3中，我们将使用retro- 明显地提出了采样的数据。这项研究将表明，就图像质量而言，PNP-DL优于 CS和现有的DL方法，并且希望更高的加速度与平行MRI无关，而RATE-2加速度 - ation。在AIM 4中，我们将使用成人前瞻性采样数据评估PNP-DL的性能 和儿科患者。该项目的成功完成将证明PNP-DL胜过州 - 在图像质量方面的艺术方法，同时表现出一定程度的鲁棒性和广泛的适用性 已经避免了其他基于DL的MRI重建方法。加速和图像质量改进 这些发展提供的几乎所有MRI应用都将受益，包括儿科成像，在哪里 减少镇静是紧迫的需求和高维成像应用（例如，全心4D流量 成像），对于常规临床使用而言太慢。