This is a single-center, prospective observational study at Peking Union Medical College Hospital. The main goal is to evaluate the image quality and clinical value of a new fast abdominopelvic MRI sequence that uses deep learning-based reconstruction.
The study will include adult patients (18 years or older) who need an MRI scan of the abdomen or pelvis as part of their routine care. These patients will receive both the standard MRI sequence and the new fast sequence, with the goal of comparing the two. The fast sequence is designed to be shorter (5-8 minutes) while maintaining or improving image quality, which can help reduce patient discomfort and motion-related artifacts.
Radiologists will assess the quality of the images from both sequences. The study will also look at how well the fast sequence can detect common abdominal and pelvic diseases, such as gallstones, inflammation, and tumors. This study does not involve new drugs, invasive procedures, or changes to standard care. No additional risks to patients are expected beyond those of a standard MRI scan.
Age ≥ 18 years
Clinical indication for abdominopelvic MRI examination
Able to understand and sign the informed consent form
Able to lie supine and cooperate with breath-holding/respiratory commands during MRI
Presence of absolute contraindications to MRI (e.g., pacemaker, ferromagnetic foreign body, severe claustrophobia)
Inability to complete the MRI scan due to severe pain, instability, or altered mental status
Pregnant or breastfeeding women (unless explicitly approved by protocol)
Previous abdominal/pelvic surgery with contraindications to MRI sequences
The study population consists of adult patients (aged 18 years and older) with clinically suspected acute calculous cholecystitis, who are admitted to Peking Union Medical College Hospital and planned for cholecystectomy. All patients will undergo preoperative fast MRI with deep learning reconstruction for risk stratification and severity grading. Patients with absolute MRI contraindications or non-biliary causes of abdominal pain are excluded.
Cohort
Prospective
Not listed
Image Quality Score of Fast Abdominopelvic MRI With Deep Learning Reconstruction
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