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Abstract #0113

Fast T2WI with DLR-Enhanced SSFSE: A Reliable Solution for diagnosing Acute Abdominal Pain in Emergency Settings

Jia Xu1, Liang Zhu1, Wei Liu1, Wenjing Liu2, Yitong Lu1, Jingjuan Liu1, Chenxue Ma1, Yifei Zhang3, Xuan Wang1, and Feng Feng1
1Department of Radiology, Peking Union Medical College Hospital, Beijing, China, 2Department of General Surgery, Peking Union Medical College Hospital, Beijing, China, 3Healthcare MR research, Beijing, China

Synopsis

Keywords: Hepatobiliary, Machine Learning/Artificial Intelligence, Acute Abdomen

Motivation: To determine if SSFSE T2-weighted imaging with deep learning reconstruction (DLR) can provide a reliable and time-efficient diagnostic tool for acute abdominal pain in emergency settings.

Goal(s): To compare the image quality and diagnostic performance of SSFSE with DLR, SSFSE alone, and PROPELLER T2WI.

Approach: The study included 35 healthy volunteers and 35 patients with acute abdomen from emergency room. Image quality and diagnostic performance were assessed across three T2WI datasets: SSFSE-DLR, SSFSE, and PROPELLER. Each dataset contained coronal fat-suppressed, non-fat-suppressed, and axial non-fat-suppressed images.

Results: SSFSE-DLR provided superior image quality and diagnosis performance while significantly reduced acquisition time.

Impact: This study highlights the potential of combining deep learning reconstruction with SSFSE T2WI to significantly improve image quality and diagnostic performance compared to conventional T2WI, offering a rapid and reliable diagnostic option for evaluating acute abdominal conditions in emergency settings.

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