Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning

Abstract

Purpose

To investigate the value of deep learning (DL) in T2-weighted imaging (T2_DL) of the bladder regarding acquisition time (TA), image quality, and diagnostic confidence compared to standard T2-weighted turbo-spin-echo (TSE) imaging (T2_S).

Methods

We prospectively enrolled a total of 28 consecutive patients for the evaluation of bladder cancer. T2_S and T2_DL sequences in three planes were performed for each participant, and acquisition time was compared between the two acquisition protocols. The image evaluation was conducted independently by two radiologists using a 5-point Likert scale for artifacts, noise, overall image quality, and diagnostic confidence, with 5 indicating the best quality. Additionally, T2 scoring based on Vesical Imaging-Reporting and Data System (VI-RADS) was performed by two readers.

Results

Compared to T2_S, the acquisition time of T2_DL was reduced by 49.4% in the axial and by 43.8% in the coronal and sagittal orientations. The severity and impact of artifacts and noise levels were superior in T2_DL versus T2_S (both p < 0.05). The overall image quality in T2_DL was demonstrated to be higher compared to that in T2_S in axial and sagittal imaging (both p < 0.05). The diagnostic confidence and T2 scoring of both sequences in all planes did not differ (p > 0.05).

Conclusions

Our study preliminarily demonstrated the feasibility of T2-weighted imaging with DL reconstruction of bladder MR in clinical practice. T2_DL achieved a reduction in acquisition time, superior lesion detectability, and overall image quality with similar diagnostic confidence and T2 score compared to the standard T2 TSE sequence.

Graphical abstract