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: 10382334
• 负责人: Rizwan Ahmad
• 金额: $ 57.04万
• 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/10382334
• 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; Imaging technology; Immune; Investigation; Lead; 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; base; 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.

项目总结/摘要 这项研究的主要目标是开发和验证一个全面、健壮的深度学习（DL） 这是一个超越现有技术限制的改进MRI重建的框架。拟议框架 使用“即插即用”算法将联合收割机物理驱动的MR采集模型与最先进的学习 图像模型，由图像去噪子例程实例化。充分利用MR丰富的结构 图像，我们建议使用以特定于应用的方式训练的基于DL的去噪器。拟议 PnP-DL框架提供了优于其他现有DL方法以及压缩感知的优势 （CS）。与现有的用于MRI重建的DL方法相比，PnP-DL更不受不可避免的变化的影响 在前向模型中，例如线圈灵敏度或欠采样模式的变化，使其能够概括 跨应用程序和采集设置。与CS相比，PnP-DL恢复图像的速度更快，质量更高， 并具有潜在的上级诊断价值。 我们的初步结果突出了PnP-DL推进MRI技术的潜力。在这项工作中，我们将毛皮- 他们开发了PnP-DL，并在这些主要应用中验证了它：心脏电影，2D脑和3D脑成像。 在目标1中，我们将训练和优化基于卷积神经网络的特定于应用的去噪器， 上述应用。具有最佳去噪性能的去噪器将被选择用于进一步的去噪。 调查在目标2中，我们将开发和比较不同的PPERT算法。产生最佳结果的算法 重建精度和计算速度的组合将在Gadgetron中实现， 处理.在目标3中，我们将比较PnP-DL的性能与其他最先进的方法， 对欠采样数据进行重新采样。本研究将证明，在图像质量方面，PnP-DL优于上级 CS和现有DL方法，尽管加速更高，但不劣于并行MRI（速率2加速）。 行动。在目标4中，我们将使用来自成人的前瞻性欠采样数据评估PnP-DL的性能。 和儿科患者。该项目的成功完成将证明PnP-DL优于国家- 同时表现出一定程度的鲁棒性和广泛的适用性， 避开了其他基于DL的MRI重建方法。加速和图像质量改善 这些发展所提供的技术将使几乎所有的MRI应用受益，包括儿科成像， 减少镇静是迫切的需要，并且高维成像应用（例如，全心4D立体声 成像），这对于常规临床使用来说太慢。