Comparative Evaluation of Deep Learning-Reconstructed FOCUS-MUSE, MUSE, FOCUS, and SS-DWI for Bladder Imaging

Motivation: Deep learning-based reconstruction (DLR) significantly improves image quality and addresses many limitations of traditional sequences. Building on DLR, this study compares the effectiveness of different diffusion sequences (FOCUS-MUSE, MUSE, FOCUS, and SS-DWI) for bladder imaging. Goal(s): The goal is to provide guidance for clinical diffusion scanning practices. Approach: A prospective study involving 23 patients included both qualitative assessments by radiologists and quantitative analyses of SNR, CNR, and ADC values, providing a detailed evaluation of each sequence's performance. Results: The results showed that FOCUS-MUSE and MUSE had higher SNR and CNR compared to other sequences, with MUSE achieving the highest overall ratings. Impact: Offers insights into selecting optimal diffusion sequences for bladder imaging under deep learning-based reconstruction, enhancing clinical efficiency and accuracy.