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

PURPOSE: To investigate the value of deep learning (DL) in T2-weighted imaging (T2DL) of the bladder regarding acquisition time (TA), image quality, and diagnostic confidence compared to standard T2-weighted turbo-spin-echo (TSE) imaging (T2S).

METHODS

We prospectively enrolled a total of 28 consecutive patients for the evaluation of bladder cancer. T2S and T2DL 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 T2S, the acquisition time of T2DL 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 T2DL versus T2S (both p < 0.05). The overall image quality in T2DL was demonstrated to be higher compared to that in T2S 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. T2DL 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.