Deep learning rapid MRI for preoperative evaluation of acute cholecystitis.
Official title: Fast Abdominopelvic MRI With Deep Learning-Based Reconstruction Algorithm: Image Quality Evaluation and Application in Patients With Common Abdominal and Pelvic Diseases
Run by Peking Union Medical College Hospital · NCT07551375 · Data updated 2026-11-12
This will test whether a new fast abdominal MRI using deep learning can provide image quality comparable to standard MRI for adults with acute cholecystitis who need preoperative scans.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 300 (estimated) |
| Sponsor | Peking Union Medical College Hospital Academic / other |
| Locations | 1 site (Beijing, Beijing Municipality) |
| Status | Recruiting |
| Started | June 1, 2024 |
| Main results expected | August 12, 2026 |
| Study ends | December 31, 2026 |
| Registry record updated | April 24, 2026 |
| Trial ID | NCT07551375 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18 or older with a clinical indication for abdominopelvic MRI who can lie supine, follow breath-hold instructions, and provide informed consent are ideal candidates.
✕Probably not a fit
Patients with absolute MRI contraindications (e.g., pacemakers), severe instability or pain that prevents completing the scan, pregnant or breastfeeding women (unless allowed by protocol), or those unable to cooperate with breath-holds are unlikely to benefit or participate.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This single-center, prospective observational study at Peking Union Medical College Hospital enrolls adults who have a clinical indication for abdominopelvic MRI. Each participant will undergo both the hospital's standard MRI sequence and a new fast deep learning–reconstructed sequence (targeting a 5–8 minute acquisition). Radiologists will rate image quality and determine how well the fast sequence depicts common abdominal and pelvic findings such as gallstones, inflammation, and tumors. The protocol is noninvasive, does not change clinical care, and compares diagnostic performance and motion-related artifact between the two sequences.
Why it matters
Potential benefit: If successful, the fast deep learning MRI could shorten scan time to about 5–8 minutes while keeping diagnostic image quality, reducing patient discomfort and motion artifacts and speeding preoperative workup.
How similar studies have gone: Deep learning–based image reconstruction and accelerated MRI sequences have shown promising results in reducing scan time and preserving image quality in other organ systems and abdominal imaging, but their use specifically for preoperative acute cholecystitis is relatively novel and less extensively validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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 Exclusion Criteria: * 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
Where it's running
-
Peking Union Medical College Hospital
Beijing, Beijing Municipality, China Recruiting
Study contacts
- Study coordinator: li
- Email: m17685768911@163.com
- Phone: +86-13051096636
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.