Motivation: The scanning time for Synthetic MRI (SyMRI) remains relatively long, which limits its widespread clinical use. Addressing the need to reduce scanning time while ensuring image quality is crucial. Goal(s): To explore the potential of deep learning-based reconstruction(DLR) in accelerating SyMRI while maintaining stable quantitative parameter values and image quality. Approach: A total of 58 female patients were included. 3x-SyMRI-DLR, 3x-SyMRI, 2x-SyMRI images were compared for quantitative parameters (T1, T2, PD), image signal-to-noise ratio (SNR), subjective image quality, and diagnostic performance. Results: The 3x-SyMRI-DLR protocol significantly reduces scanning time while maintaining stable T1, T2, and PD quantitative measurements. Impact: Deep learning reconstruction for accelerating synthetic MRI is poised to enhance the clinical application of synthetic MRI in diagnosing breast diseases, thereby improving examination efficiency.
Yang et al. (Tue,) studied this question.