Liver Diseases, Magnetic Resonance Imaging, Deep Learning
Conditions
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.
Interventions
DIAGNOSTIC_TESTLiver MRI
Gd-EOB-DTPA enhanced MRI consists of T2-weighted image (T2WI), diffusion weighted image (DWI) and precontrast T1-weighted image (T1WI), dynamic T1WI (arterial, portal and transitional phases), and hepatobiliary phase.
Sponsors
GE Healthcare
Seoul National University Hospital
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)
Masking description
blinded to the reconstruction types
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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) |
Countries
South Korea
Outcome results
None listed