Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission

Abstract

Objective

To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image quality.

Methods

One hundred twenty consecutive patients divided into four groups (30 for each group) were included in this study. All patients received a T2DL (version 2022/23) and a conventional T2 TSE (cT2) sequence on an implemented 3 T scanner and software system. Group A received cT2 with HBB compared to T2DL without HBB with a field of view (FOV) of 130 mm and group B with a FOV of 160 mm. Group C received both sequences with a FOV of 160 mm plus HBB and group D without HBB. Two radiologists independently evaluated all imaging datasets in a blinded reading regarding motion, sharpness, noise, and diagnostic confidence. Furthermore, we analyzed quantitative parameters by calculating edge rise distance (ERD), signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR). Friedman test was used for group comparisons.

Results

Baseline characteristics showed no significant differences between groups A-D. After HBB cT2 showed less motion artifacts, more sharpness, and a higher diagnostic confidence than T2DL, though DL sequences had significantly lower noise (p < 0.01). Quantitative analysis revealed higher SNR and CNR for T2DL sequences (p < 0.01), while edge rise distance (ERD) remained similar. Inter-reader agreement was good to excellent, with ICCs ranging from 0.84 to 0.93. T2DL acquisition time was significantly lower than for cT2.

Conclusions

In our study, cT2 sequences with HBB showed superior image quality and diagnostic confidence while the T2DL sequence offer promising potential for reducing MRI acquisition times and performed better in quantitative measures like SNR and CNR. Additional studies are required to evaluate further adjusted and developed DL applications for prostate MRI on upcoming scanner generations and to assess tumor detection rates.