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Abstract #4294

Better diagnostic value and feasibility of Deep Learning DWI in uterine malignant neoplasms

Jian Li1, Ling Song1, Yueluan Jiang2, and Thomas Benkert3
1The First Hospital of China Medical University, Shenyang, China, 2MR Research Collaboration, Siemens Healthineers, Bejing, China, 3MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany

Synopsis

Keywords: Pelvis, Uterus

Motivation: Conventional diffusion-weighted imaging (c-DWI) of the uterus is time-consuming, and the lesion details are not well-defined.

Goal(s): To introduce a deep learning (DL) DWI sequence in uterine MRI and compare it with conventional DWI (c-DWI) to investigate its impact on examination time, image quality, lesion significance, diagnostic reliability, as well as contrast ratio (CN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

Approach: 10 patients with uterine malignancy disease were included in this study.

Results: There is no significant difference in objective assessment between the two techniques, while the overall image quality of DL-DWI is better than c-DWI (p < 0.01).

Impact: The research investigated the utilization of DL-DW in the uterus, which led to shorter examination times and significantly improved image quality. This analysis has the potential to examine other pelvic organs, such as the prostate, to assess pelvic lesions.

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