Background: Multiparametric MRI (mpMRI) as a non-invasive imaging tool is important in prostate cancer (PCa) detection and localization. Combined with radiomics analysis, features extracted from mpMRI have been utilized to predict PCa aggressiveness. T2 mapping provides quantitative information in PCa diagnoses but is not routinely available in clinical practice. Previous work from our group developed a deep learning-based method to estimate T2 maps from clinically acquired T1- and T2-weighted images. This study aims to evaluate the added value of the estimated T2 map by combining it with conventional T2-weighted images for detecting clinically significant PCa (csPCa). Methods: An amount of 76 peripheral zone prostate lesions, including clinically significant and insignificant cases, were retrospectively analyzed. Radiomic features were extracted from conventional T2-weighted images and deep learning-estimated T2 maps, followed by feature selection and model development using five-fold cross-validation. Logistic regression and Gaussian Process classifiers were employed to develop the prediction models, with performance evaluated by area under the curve (AUC) and accuracy metrics. Results: The model incorporating features from both T2-weighted images and estimated T2 maps achieved an AUC of 0.803, significantly outperforming the model based solely on T2-weighted image features (AUC of 0.700, p = 0.048). Conclusions: Radiomics features extracted from deep learning-estimated T2 maps provide additional quantitative information that improves the prediction of peripheral zone csPCa aggressiveness, potentially enhancing risk stratification in non-invasive PCa diagnostics.

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.