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Automated MRI for Liver Conditions

This study is testing a new, fully automated MRI (magnetic resonance imaging) method that uses special software with artificial intelligence (AI) to take pictures of your liver. The goal is to make liver MRIs faster and more accurate, especially for conditions like liver fat (steatosis) and iron overload. We want to see if this new method is as good as current methods. You might be able to join if you are a healthy adult, or if you have known or suspected liver disease or iron overload. The study involves one visit, lasting up to 3.5 hours.

Study design
This is an observational study planning to enroll 200 participants. It is looking at how well a new MRI software works.
What's involved
You would participate in the study for one day, with the visit lasting up to 3.5 hours.
Compensation
Not stated in the trial record.
Follow-up
The primary accuracy of the MRI method will be measured at 1 day.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT05294471

Fully Automated High-Throughput Quantitative MRI of the Liver

Recruiting
Not specifiedAges 7+Observational
University of Wisconsin, Madison
~204 participants
Updated 2026-09-16 on ClinicalTrials.gov
What's tested:Non-contrast MRI with novel MRI software

At a glance

Recruiting sites
1 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Confirm the accuracy (ie: bias) of the proposed CSE-MRI method in patients with liver steatosis and in patients with liver iron overload
Measured over 1 day (1 study visit, up to 3.5 hours)

Conditions

Healthy
Iron Overload
Liver Fat

Explore related trials

Fatty liverHealthyIron OverloadLiver and Intrahepatic Bile Duct Disorder

NCT05294471

Where you'd take part

This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.

  • University of Wisconsin

    Madison, Wisconsinno site contact published

    Recruiting

Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.

Study leadership

  • Scott Reeder, MD, PhD · PRINCIPAL_INVESTIGATOR · University of Wisconsin, Madison

Who to contact

Radiology Studies
[email protected]
608-282-8349
Email the study team

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Eligibility criteria

Inclusion

Age 18 years or older
Age 7 years or older
One of:
Known or suspected liver iron overload
Known or suspected elevated liver fat
Age 7 years or older
Scheduled for a clinical abdominal MRI exam

Exclusion

Patients with contraindication to MRI (e.g. pacemaker, contraindicated metallic implants, claustrophobia, etc)
Pregnant or trying to become pregnant (as determined by self-report during MRI safety screening)
Patients with contraindication to MRI (e.g. pacemaker, contraindicated metallic implants, claustrophobia, etc)
Patients requiring intravenous (IV) conscious sedation for imaging are not eligible; patients requiring mild, oral anxiolytics for the MRI will be allowed to participate as long as the following criteria are met:
The subject has their own prescription for the medication.
The informed consent process is conducted prior to the self-administration of this medication
They come to the research visit with a driver
Pregnant or trying to become pregnant (as determined by self-report during MRI safety screening)
Patients with contraindication to MRI (e.g. pacemaker, contraindicated metallic implants, claustrophobia, etc)
Sedation required for MRI
Pregnant or trying to become pregnant (as determined by self-report during MRI safety screening)

What this trial measures

  • Confirm the accuracy (ie: bias) of the proposed CSE-MRI method in patients with liver steatosis and in patients with liver iron overload1 day (1 study visit, up to 3.5 hours)

    For each liver segment, and for whole-liver PDFF and R2\* measurements, we will determine bias of CSE 2D using CSE 3D BH as the reference from Bland-Altman analysis. The range of R2\* values leading to reliable measurements of PDFF and R2\* will be determined using a two-segment piecewise linear model with the change point estimated from the data.