Intelligent Accelerated MRI for Pediatric Abdominal Pain

The goal of this observational study is to evaluate the effectiveness of an intelligent accelerated MRI technique in children with abdominal pain. The main question it aims to answer is: Does the intelligent accelerated MRI reduce scan time while maintaining diagnostic accuracy in pediatric patients? Participants will: Receive an abdominal MRI scan using the accelerated protocol as part of their clinical evaluation. Have their scan duration and image quality compared to conventional MRI standards.

The goal of this prospective observational study is to evaluate the effectiveness and feasibility of an intelligent accelerated MRI scanning technique in pediatric patients presenting with abdominal pain. The main questions it aims to answer are: Can the intelligent accelerated MRI technique achieve comparable or superior image quality to conventional MRI while significantly reducing scan time? Does the accelerated scanning protocol reduce motion artifacts and improve diagnostic accuracy in pediatric patients? Participants will: Undergo an intelligent accelerated MRI scan of the abdomen as part of their diagnostic workup. Have their scan duration, image quality, and diagnostic outcomes compared to historical or conventional MRI data (if available). Why this study? Abdominal pain is a common pediatric complaint with diverse causes. While MRI is a non-invasive, radiation-free diagnostic tool, its utility in children is limited by long scan times and motion artifacts. This study investigates a novel intelligent accelerated MRI sequence that promises faster scans without compromising quality. Validating this technology in children could transform MRI's role in diagnosing abdominal pain by improving patient comfort, reducing sedation needs, and enhancing diagnostic accuracy. Ethical approval: Conducted at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, with oversight by the Institutional Ethics Committee. Key innovation: Leveraging cutting-edge AI-driven acceleration to address pediatric imaging challenges. If successful, this approach could set a new standard for rapid, child-friendly MRI diagnostics.