Suppressing image blurring of PROPELLER MRI via untrained method

Abstract

Objective: Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) used in magnetic resonance imaging (MRI) is inherently insensitive to motion artifacts but with an expense of around 60% increase in minimum scan time. An untrained deep learning method is proposed to accelerate PROPELLER MRI while suppressing image blurring. 
Approach: Several reconstruction methods have been developed to accelerate PROPELLER with reduced sampling on blades. However, image quality is degraded due to blurring. Deep learning has been applied to enhance MRI reconstruction quality, and external training data are therefore needed. In addition, the distribution shift problem in deep learning also exists between the external training data and to-be-reconstructed target blade data. This paper introduces an untrained neural network (UNN) to suppress image blurring, which is applied to improve PROPELLER MRI. This network structure was then incorporated into blade k-space.
Results: The untrained method improved the blade image quality from brain MRI data. Furthermore, it enhanced the sharpness of the reconstructed image compared to PROPELLER reconstructions using parallel imaging methods and supervised learning methods using external training data. PROPELLER blade acquisition was accelerated by undersampling data with reduction factors 2, 3, and 4.
Significance: The reported UNN enhanced PROPELLER method can improve image quality by suppressing blurring. External training data are not needed to mitigate the challenge of collecting high-quality clinical data for training without affecting clinical workflow and the standard care for patients.

DPP: deep phase prior for parallel imaging with wave encoding

“…Furthermore, improvements have been made to accelerate dynamic imaging by utilizing UNN regularized by spoiled gradient echo (SPGR) (Slavkova et al 2023). Sequentially, leveraging UNN to generalize the degradation process of blurred images provides a solid foundation and also demonstrates the insensitivity of UNN to distribution shifts (Saju et al 2023), which can be viewed as the characterization of image low-level statistical priors through a parameterized convolutional neural network. Our earlier study has also already demonstrated the feasibility of UNN in representing physical information, including the latent images and approximations of CSM (Liu et al 2023).…”