A comprehensive deep learning framework for MRI reconstruction

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.

Grant Number: 5R01EB029957-04
NIH Institute/Center: NIH
Principal Investigator: Rizwan Ahmad