Improving deep PROPELLER MRI via synthetic blade augmentation and enhanced generalization.

In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction models are deteriorated. Due to narrow rotated and translated blades acquired in PROPELLER, the technique of data augmentation that is used for deep learning-based Cartesian MRI reconstruction cannot be directly applied. To address the issue, this paper introduces a novel approach for the generation of synthetic PROPELLER blades, and it is subsequently employed in data augmentation for undersampled blades reconstruction. The principal aim of this study is to address the challenges of reconstructing undersampled blades to enhance both image quality and computational efficiency. Evaluation metrics including PSNR, NMSE, and SSIM indicate superior performance of the model trained with augmented data compared to non-augmented counterparts. The synthetic blade augmentation significantly enhances the model's generalization capability and enables robust performance across varying imaging conditions. Furthermore, the study demonstrates the feasibility of utilizing synthetic blades exclusively in the training phase, suggesting a reduced dependency on real PROPELLER blades. This innovation in synthetic blade generation and data augmentation technique contributes to enhanced image quality and improved generalization capability of the associated deep learning model for PROPELLER MRI reconstruction.

Suppressing image blurring of PROPELLER MRI via untrained method.

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 bladek-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.