Deep Learning Reconstruction algorithm for T2-weighted Turbo Spin Echo Renal MRI

Motivation: MRI is a powerful diagnosis tool for renal tumors with a long acquisition time. How to improve image quality while shortening acquisition time is a major research focus. Goal(s): To use a deep learning (DL) algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) renal MRI scans and compare with standard-resolution T2-weighted TSE sequence. Approach: A total of 14 patients with clinically suspected renal tumors who underwent renal low-resolution DL-reconstructed T2-weighted TSE sequence(T2DL) and standard-resolution T2-weighted TSE sequence (T2S) were included. Results: T2DL reduced acquisition time by 32% and improved overall image quality compared with T2S. Impact: A DL reconstruction method for low-resolution renal T2-weighted TSE sequence has the potential to reduce acquisition time and improve image quality compared with standard acquisition method, which may help detect renal lesions early and improve the survival rates of patients.