Deep learning-based reconstruction for multi-shot breast DWI: Quantitative and qualitative improvements.

PURPOSE: To evaluate the effectiveness of deep learning (DL)-based reconstruction for improving image quality and reducing scan time in multi-shot diffusion-weighted imaging (DWI) of the breast, compared with standard readout-segmented echo-planar imaging (rs-EPI). MATERIALS AND METHODS: This retrospective study included 146 women (mean age: 55.8 years) with newly diagnosed breast cancer who underwent preoperative breast MRI with both rs-EPI and prototype DL-reconstructed (DLR) multi-shot DWI. Quantitative metrics-signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast, and apparent diffusion coefficient (ADC)-were compared between sequences. Two radiologists independently assessed overall image quality, artifact suppression, lesion conspicuity, and fibroglandular tissue suppression using a 5-point Likert scale. An independent validation cohort of 39 consecutive patients was analyzed using the same methodology. RESULTS: Compared with rs-EPI, DLR multi-shot DWI demonstrated significantly higher SNR, CNR, and lesion contrast at both b = 800 and synthetic b = 1500 s/mm2 (all p < 0.05). Tumor ADC values were slightly lower and fibroglandular tissue ADC values slightly higher with DLR (both p < 0.05). Qualitative assessments demonstrated higher scores for overall image quality and lesion conspicuity with DLR, with improved artifact suppression at b = 800 s/mm2. Interobserver agreement was substantial to almost perfect (weighted κ = 0.753-0.981). DL reconstruction reduced acquisition time by 29% compared with rs-EPI. In the validation cohort, improvements in SNR, CNR, and overall image quality were also observed, whereas ADC values did not differ significantly between techniques. CONCLUSION: DL-based reconstruction improves quantitative and qualitative image quality in multi-shot breast DWI while reducing scan time. These findings support its potential integration into clinical breast MRI.