Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study

Motivation: DWI’s challenging spatial resolution could be addressed by deep-learning-based image reconstruction, by reducing noise without increasing acquisition time. Goal(s): To compare the image quality of the Echo-Planar-Imaging-Deep-Learning (EPI-DL) DWI sequence with the clinically used simultaneous-multi-slice (SMS) RESOLVE in breast lesions. Approach: EPI-DL and RESOLVE breast images from 20 participants were qualitatively evaluated ed. Quantitative image quality metrics of SNR and CNR on both high b-value (b800) images and ADC maps were calculated. Results: SNR in RESOLVE vs. EP-DL differed statistically significantly in manually delineations for b800 (p=0.006), ADC maps (p=0.001), and in ADC circularly delineations (p=0.001). Impact: DWI-DL reconstruction may be clinically useful for addressing low-spatial resolution without compromising acquisition time and image quality. Such benefits coupled with the available methods of readout segmentation and SMS acquisitions may further enhance the value of DWI in breast imaging.