High Resolution HBA-MRI Using Deep Learning Reconstruction

Not Applicable

Active, not recruiting

• Conditions: Deep Learning; Liver Diseases; Magnetic Resonance Imaging

• Registration Number: NCT05182099
• Lead Sponsor: Seoul National University Hospital

Brief Summary

This study aims to compare image qualities between conventionally reconstructed MRI sequences and deep-learning reconstructed MRI sequences from the same data in patients who undergo Gd-EOB-DTPA enhanced liver MRI. The AIRTM deep learning sequence is applicable for various MRI sequences including T2-weighted image (T2WI), T1-weighted image and diffusion-weighted image (DWI). We plan to perform intra-individual comparisons of the image qualities between two reconstructed image datasets.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-12-16
• Study First Submitted QC: 2022-01-06
• Study Start: 2022-01-10
• Study First Posted: 2022-01-10
• Primary Completion: 2022-03-30
• Status Verified: 2023-05
• Last Update Submitted: 2023-05-15
• Last Update Posted: 2023-05-16
• Study Completion: 2023-09-30

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 52

Inclusion Criteria

• older than 20 years old
• scheduled for Gd-EOB-DTPA enhanced liver MRI at a 3T scanner (Premier, GE Healthcare) in our institution
• signed informed consent

Exclusion Criteria

• younger than 20 years old
• any absolute/relative contrast indication of Gd-EOB-DTPA enhanced MRI
• history of transient dyspnea after Gd-EOB-DTPA administration

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Overall image quality of arterial phase	3 months after enrollment completion	qualitative assessment of arterial phase on a five point scale (highest score indicates better image quality)

Trial Locations

Locations (1)

Seoul National University Hospital; 🇰🇷 Seoul, Korea, Republic of