Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses

Abstract

Objective

To assess the impact of a commercially available deep learning reconstruction (DLR) algorithm on qualitative and radiomics analyses in prostate MRI.

Methods

This single-centre retrospective cohort included 25 consecutive patients who underwent a prostate MRI (1.5 T) in 2022. T2-weighted (T2WI), diffusion-weighted (DWI; b = 50, 1000, extrapolated 2000 s/mm²) and apparent diffusion coefficient (ADC) images were reconstructed using DLR and standard (non-DLR) techniques. The two sets were mixed and blind-reviewed independently by six radiologists. Images were qualitatively scored according to PI-QUAL score, overall image quality, diagnostic confidence, anatomical conspicuity, artifact, and noise. Transition and peripheral zones were segmented and radiomics features extracted from region-of-interests using Pyradiomics package. Qualitative criteria and radiomics were compared using a pairwise Wilcoxon signed-rank test.

Results

PI-QUAL score was not significantly different (p = 0.32). Overall image quality was not significantly different (p = 0.21 on T2WI and 0.56 on DWI/ADC). Noise was lower on DLR images for T2WI (p < 0.01) and DWI/ADC (p = 0.04). Diagnostic confidence in excluding clinically significant cancer (PI-RADS ≥ 3) in the transition zone was lower with DLR images (p = 0.02). In the transition zone, 89/93 (96 %) of the radiomics features were significantly different between non-DLR and DLR images on T2WI, 68/93 (73 %) on DWI b-2000 s/mm², and 55/93 (59 %) on ADC images. In the peripheral zone, 91/93 (98 %) were significantly different on T2WI, 50/93 (54 %) on DWI b-2000 s/mm², and 70/93 (75 %) on ADC images.

Conclusion

Radiomics features were significantly different on DLR images which should encourage caution for clinical and research purposes. DLR algorithm decreases noise while preserving overall image quality.