Deep learning-based super-resolution imaging for routine clinical T1- and T2-weighted breast MRI at 1.5T

Motivation: High-resolution images in breast MRI are desired for lesion detection and characterization but are restricted due to scan time constraint in routine clinical settings. Goal(s): Our goal was to use deep learning (DL)-based reconstructions to improve image resolution and quality of routine clinical breast MRI. Approach: We applied a dedicated Precise-Image-Net for both 2D T1- and T2-weighted imaging in breast cancer patients at 1.5T and compared it to conventional parallel imaging, compress sensing, and convolutional neural network (CNN) reconstructions. Results: Initial clinical data demonstrated a clear improvement of sharpness in breast T1- and T2-weighted images compared with standard reconstructions. Impact: Deep learning-based super-resolution reconstruction provides improved image resolution and sharpness in breast MRI, showing promises for better lesion detection and characterization in routine clinical settings without prolonging scan time, which is of particular importance in dynamic contrast enhanced-MRI.