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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