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This is a Quantitative Study study.

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September 17, 2025

Feasibility of accelerated 3D T2-weighted bladder MRI using the deep learning-constrained compressed sensitivity encoding technique (SENSE)

Key Points

The application of a deep learning algorithm improved image quality in 3D T2-weighted MRI scans for bladder cancer.
Diagnostic efficiency, measured by AUC, showed high values of 0.895 for deep learning algorithms compared to 0.888 for traditional methods.
Sixty-seven patients with bladder cancer underwent high-resolution MRI scans at 3.0T, assessing the impacts of AI on imaging.
This research supports the use of AI techniques in enhancing diagnostic capabilities while reducing scan time in bladder cancer detection.

Abstract

Motivation: To compare the application value of AI algorithm in 3DT2WI of bladder cancer Goal(s): This study aimed to evaluate the clinical value of deep-learning algorithm reconstruction in 3DT2WI for bladder cancer. Approach: Sixty-seven patients with bladder cancer (MIBC /NMIBC= 12/42) underwent high-resolution 3DT2WI with and without the deep-learning reconstruction algorithm (DLA) at 3.0T. The pathological results were used as the gold standard for diagnostic evaluation. Results: Regarding the diagnostic efficiency, the AUCs of the two 3DT2WI-DLA, compressed SENSE(CS), and the compressed 32.5% scan time, were 0.895 and 0.888, respectively. In addition, 3DT2WIDLA had higher scores for image quality than 3DT2WI. Impact: DLA-CS cloud helped high-resolution 3DT2WI decrease the scantime, meet the diagnostic requirements, improve image quality and decrease artifacts in bladder cancer.

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Feasibility of accelerated 3D T2-weighted bladder MRI using the deep learning-constrained compressed sensitivity encoding technique (SENSE)

Key Points

Abstract

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