Clinical feasibility of a deep learning approach for conventional and synthetic diffusion-weighted imaging in breast cancer: Qualitative and quantitative analyses

•Deep learning DWI outperforms both conventional and synthetic DWI, with better lesion conspicuity, chest wall delineation, and fewer artifacts.•Deep learning DWI provides comparable normal parenchymal signals to both conventional and synthetic DWI.•Combining deep learning DWI with synthetic DWI can enhance cancer detection while reducing acquisition time. In this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstructions to cDWIs and sDWIs in patients with various breast malignancies. We retrospectively analyzed 115 patients with biopsy-proven breast malignancies who underwent breast magnetic resonance imaging from July 2022 to June 2023, including cDWI with b-value of 800 s/mm2 (cDWI800), sDWI with b-value of 1500 s/mm2 (sDWI1500), DWI using DL-based reconstruction (DL-DWI) with b-value of 800 s/mm2, and synthetic DL-DWI with b-value of 1500 s/mm2 (DL-DWI800 and sDL-DWI1500). Two radiologists independently performed the qualitative analyses using a 5-point Likert scale for all DWI sets. The quantitative analyses were also conducted for signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and cancer-to-parenchyma contrast ratio (CPR). DL-DWI800 and sDL-DWI1500 provided better lesion conspicuity and thoracic muscle and rib delineation than cDWI800 and sDWI1500 (all P < 0.05). DL-DWI800 and sDL-DWI1500 showed comparable normal parenchymal signals to those of cDWI800 and sDWI1500 (all P > 0.05). sDL-DWI1500 and sDWI1500 showed no significant differences in SNR and CNR (P = 0.908, and P = 0.081, respectively). DL-DWI800 and cDWI800 were not significantly different between SNR, CNR, and CPR (all P > 0.05). DL-DWI outperformed cDWI and sDWI in both qualitative and quantitative analyses at the high b-values, while also achieving a shorter acquisition time.