Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution

Purpose: To evaluate and compare the image quality and lesion conspicuity of prostate T2-weighted imaging (T2WI) using four reconstruction methods: conventional Sensitivity Encoding (SENSE), compressed sensing (CS), model-based deep learning reconstruction (DL), and deep learning super-resolution reconstruction (SR). Methods: This retrospective study included 49 patients who underwent multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) for suspected prostate cancer. Axial T2WI was acquired using two protocols: conventional SENSE and CS-based acquisition. From the CS-based data, three reconstruction methods (CS, DL, and SR) were applied to generate additional images. Two board-certified radiologists independently assessed overall image quality and sharpness using a 4-point Likert scale (1 = poor, 4 = excellent). Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and sharpness index. PI-RADS T2WI scoring and lesion conspicuity were preliminarily evaluated in 18 individuals with pathologically confirmed prostate cancer. Statistical comparisons were conducted using the Wilcoxon signed-rank test. Results: SR consistently achieved the highest scores in both qualitative (overall image quality, image sharpness) and quantitative (SNR, CNR, sharpness index) assessments, compared with SENSE, CS, and DL (all pairwise comparisons, Bonferroni-corrected p < 0.0001). In lesion-based analysis, SR showed a trend toward improved lesion conspicuity, although PI-RADS T2WI scores were similar across reconstruction. Conclusion: SR reconstruction demonstrated superior image quality in both qualitative and quantitative assessments and showed potential benefits for lesion visualization. These findings, although based on a small sample, suggest that SR may be a promising approach for prostate MRI and warrants further investigation in larger populations.