Deep Learning k‐Space‐to‐Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion‐Weighted Imaging Breast MRI.

Background: For time‐consuming diffusion‐weighted imaging (DWI) of the breast, deep learning‐based imaging acceleration appears particularly promising. Purpose: To investigate a combined k‐space‐to‐image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. Study Type: Retrospective. Population: 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. Field Strength/Sequence: 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). Assessment: DWI data were retrospectively processed using deep learning‐based k‐space‐to‐image reconstruction (DL‐DWI) and an additional super‐resolution algorithm (SRDL‐DWI). In addition to signal‐to‐noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL‐ and SRDL‐DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven‐point rating scale. Statistical Tests: Friedman's rank‐based analysis of variance with Bonferroni‐corrected pairwise post‐hoc tests. P < 0.05 was considered significant. Results: Both DL‐ and SRDL‐DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL‐DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818–0.848). Irrespective of b‐value, both standard and DL‐DWI produced superior SNR compared to SRDL‐DWI. ADC values were slightly higher in SRDL‐DWI (+0.5%) and DL‐DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL‐/SRDL‐DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel‐wise error. Data Conclusion: Deep learning‐based k‐space‐to‐image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super‐resolution interpolation allows for substantial improvement of subjective image quality. Evidence Level: 4 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]