Use of distortion correction combined with deep learning reconstruction in DWI: how does image quality compare to conventional acquisition?

Motivation: The use of deep learning reconstruction, combined with Multiplexed Sensitivity Encoding (MUSE), can extend the benefit of distortion robustness in prostate DWI to poor SNR conditions while maintaining a large spatial matrix. Goal(s): The purpose of this study is to evaluate the quantitative image quality improvement provided by combining MUSE and DLR in DWI of the prostate. Approach: Quantitative analysis including SNR, CNR and ADC were compared through ROI analysis of MUSE DWI with conventional and DL reconstruction in 50 prostatic cancer patients. Results: DLR images demonstrated a significantly higher SNR and CNR. ADC values were consistent among methods. Impact: Deep learning reconstruction in combination with MUSE can be exploited for better prostate DWI image quality in cases of low SNR, or traded for increased resolution or reduced scan time.