Deep Learning-Based Distortion Correction for Brain Diffusion-weighted Imaging: A Prospective Comparison With Single-shot and Readout-segmented Echo-planar Imaging.
Authors
Affiliations (1)
Affiliations (1)
- Department of Neuroradiology, University Medical Center Mainz, Langenbeckstraße 1, Mainz, Germany (M.K., S.A., A.K., V.I.S., M.A.B., A.E.O., H.A.); Institute of Medical Biostatistics, Epidemiology and Informatics, Johannes Gutenberg University, Langenbeckstr 1, Mainz, Germany (R.P.); Siemens Healthineers AG Magnetic Resonance, Erlangen, Germany (T.F.); Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ (S.Q.); Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, Germany (A.E.O.).
Abstract
Single-shot echo-planar imaging DWI (ss-EPI DWI) is susceptible to geometric distortions near air-tissue interfaces, limiting diagnostic accuracy. Multishot readout-segmented DWI (RESOLVE DWI) mitigates these artifacts but requires longer acquisition times. This study evaluated whether ss-EPI DWI with a deep learning-enabled correction of static field inhomogeneities (DL DWI) can achieve effective distortion correction while preserving diagnostic reliability and reducing scan time. To compare image quality, geometric distortion, lesion detectability, and diagnostic confidence of ss-EPI DWI with DL-enabled correction of static field inhomogeneities (DL DWI) against conventional ss-EPI DWI (without correction) and RESOLVE DWI. In this prospective single-center study (April 2025 to August 2025), 100 patients undergoing clinically indicated brain MRI were enrolled following written informed consent. All patients underwent DL ss-EPI DWI with DL correction (1 min 29 s) and RESOLVE DWI (3 min 17 s); ss-EPI DWI was reconstructed by disabling DL correction for all patients. Qualitative and quantitative analyses were performed. Three board-certified radiologists rated image quality, lesion detectability, distortion, and diagnostic confidence on a 5-point Likert Scale. Two quantitative measures of distortion were applied: geometric conformity with anatomic 3D T1-weighted data through coregistration methods and gray-value distributions within volumes of interest in susceptibility-affected regions (temporal lobe, cerebellum, and brainstem). DL DWI significantly outperformed ss-EPI DWI across all qualitative parameters, including diagnostic confidence (median, 4.0 vs. 3.0; P < 0.001), overall image quality (median, 4.0 vs. 3.0; P < 0.001), and geometric distortion (median, 4.0 vs. 3.0; P < 0.001). RESOLVE DWI achieved the highest ratings overall (median, 5.0). Lesion detectability was comparable between DL DWI and RESOLVE DWI (median, 4.0 for both; P > 0.08), while both significantly outperformed ss-EPI DWI (P < 0.001). Quantitative analysis confirmed superior geometric accuracy of DL DWI over ss-EPI DWI, with lower coregistration cost-function values and normalization of gray-value distributions in susceptibility-affected regions. DL DWI improved image quality and geometric accuracy compared with uncorrected ss-EPI DWI. Subjective lesion-detectability ratings were similar between DL DWI and RESOLVE DWI, while DL DWI required less than half the acquisition time. These findings support DL-based distortion correction as a promising, time-efficient approach for brain diffusion-weighted imaging that warrants confirmation in a predefined noninferiority design.