Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Breast Imaging

Motivation: Synthetic MRI, with its unique advantages including unique signal acquisition, rapid synchronization, visualization and multiparameter maps, is gradually applied in breast cancer diagnosis. However, its extended scanning time restricts its broader use. Goal(s): To accelerate synthetic MRI while maintaining its quantitative parameters and image quality using deep learning-based reconstruction (DLR). Approach: 12 female patients were enrolled and scanned with two sets of synthetic MRI: a standard protocol and an accelerated protocol (before and after DLR). Quantitative parameters, SNR of lesion and subjective image quality were compared. Results: Comparable image quality was achieved using accelerated synthetic MRI with DLR. Impact: The combination of DLR with accelerated synthetic MRI protocol has significant benefits in promoting the practical application of synthetic MRI in breast imaging and enhancing examination efficiency.