Haidara Almansour

Background: 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. Objectives: 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. Materials and Methods: 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). Results: 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. Conclusion: 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.

Objectives: Vessel wall magnetic resonance imaging is important in the diagnosis of intracranial vascular diseases. Until now, adequate clinical implementation has required the use of 3 T scanners, which limits wider availability. This study aims to evaluate the interchangeability of a 3D turbo spin echo sequence with variable flip angle MRI sequence (T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution, SPACE) with deep learning image reconstruction for a 1.5 T scanner, with an established sequence (T1 3D SPACE) for a 3 T scanner. In addition, image quality parameters and diagnostic confidence are to be compared. Materials and Methods: Patients were prospectively examined using a T1-weighted turbo spin echo with variable flip angle at 3 T and a similar newly developed sequence with deep learning-based image reconstruction at 1.5 T. The images were read by 3 experienced radiologists regarding the presence of pathologic enhancement of the vessel wall for the interchangeability analysis. Image quality and diagnostic confidence were evaluated using 5-point Likert scales. Interreader and intrareader reliability were calculated for all parameters. Results: A total of 50 participants (42.6 ± 21.2 y, 26 males) were included. Regarding the presence of pathologic vessel wall enhancement, there was a high interreader agreement [Fleiss kappa = 0.72 (95% CI: 0.62; 0.81) for 1.5T, 0.78 (0.69; 0.88) for 3 T]. The sequences at 1.5 T and 3 T were interchangeable (IEI = 0.008). Image quality at 1.5 T was diagnostic in all cases, but rated superior at 3 T ( P < 0.001). Diagnostic confidence was also slightly but significantly higher at 3 T ( P < 0.001). Conclusions: The utilization of deep learning reconstruction enables vessel wall imaging in 1.5 T MRI. This new MRI sequence was interchangeable with established sequences at 3 T for the detection of pathologic vessel wall enhancement. This development could significantly improve the availability of vessel wall imaging in the clinical routine.

Purpose: As MRI frequently requires general anesthesia in pediatric patients, there is an undersupply in clinical routine. This may result in delayed examinations or use of CT, especially in emergency settings. To enable ad-hoc MRI scans and rule out increased intracranial pressure or intracranial mass lesions, we combined various acceleration techniques with Deep-Learning reconstruction, generating ultra-fast diagnostic images sufficient to exclude critical pathologies. Methods: Thirty-six MRI datasets of infants with a median age of 35.2 months (SD ± 23.2) and anesthesia-free imaging were retrospectively evaluated. Imaging was performed using ultra-fast T2-weighted sequences in three planes (slice thickness 5 mm; total acquisition time 47s). Four readers evaluated subjective image quality using a 5-point Likert-scale. Readers were asked to indicate how safe they felt about assessing possible midline displacement or mass lesion. For semi-quantitative analysis, readers reported diameters of lateral and third ventricles. Gwet’s AC2 and intraclass correlation (ICC) were used for interrater agreement. Results: 94.4% of datasets showed at least acceptable diagnostic confidence. Readers felt confident excluding acute intracranial pathology. 52.1% of ultra-fast sequences demonstrated good to excellent image quality. 72.6% were rated with good or excellent diagnostic confidence. Interrater reliability demonstrated almost excellent agreement of diagnostic confidence (Gwet’s AC2 ≥ 0.886) and image quality (Gwet’s AC2 ≥ 0.942). Excellent agreement regarding ventricular width (ICC values ≥ 0.966) was shown for all measurements. Conclusion: The use of deep-learning reconstruction algorithms in pediatric brain MRI is feasible, allowing anesthesia-free emergency imaging. This will reduce periprocedural risk, number of necessary CT scans, and lower healthcare costs.