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Updated: Dec 20, 2025
Diffusion Imaging in the Rat Cervical Spinal Cord
Published on: April 7, 2015
Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in
Stephanie Tina Sauer1, Sara Aniki Christner1, Anna-Maria Lois1
1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
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.
