4D-MRI for Precision Medicine in Cancer Treatment

This study is developing a new way to create detailed images of the lungs and liver using a technique called four-dimensional magnetic resonance imaging (4D-MRI). This imaging method creates 3D movies of your chest and abdomen as you breathe, helping doctors see how tumors move. The goal is to improve radiation therapy for lung and liver cancer by providing more precise information about tumor size, shape, and location. Researchers will measure the quality of these images and how accurately they show movement in healthy volunteers and patients with liver or lung cancer. You may be eligible if you are an adult with lung or liver cancer that is less than 7 cm and you are scheduled for radiation therapy.

Study design: This is an observational study aiming to enroll 100 participants, including healthy volunteers and cancer patients.
What's involved: You would undergo a single imaging session lasting up to 2 hours.
Compensation: Not stated in the trial record.
Follow-up: Image quality and movement errors are measured during a single imaging session, lasting up to 2 hours.

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4D-MRI for Precision Medicine

Recruiting

Not specifiedAges 18–82Observational

University of Virginia

~100 participants

Updated 2026-05-06 on ClinicalTrials.gov

What's tested:Magnetic Resonance Imaging

At a glance

Recruiting sites

1 of 1 listed site is recruiting right now

RecruitingSuspended, closed, or not yet open

What they're measuring

Image quality metrics in healthy volunteers

Measured over single imaging session, lasting up to 2 hours

+2 more outcomes measured

Conditions

Healthy Volunteers

Liver Cancer

Lung Cancer

Where it's being run

1 sites across 1 states

Virginia1

Study leadership

G. Wilson Miller, PhD · PRINCIPAL_INVESTIGATOR · Univsersity of Virginia

Who to contact

Roselove N Nunoo-Asare, MA

[email protected]

4342436074

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Do you actually qualify for this trial?

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Eligibility criteria

Inclusion

Patient is 21 or older

Patient has primary or metastatic tumor(s) in the lungs or the liver

Diameter of the tumor(s) is less than 7 cm

Patient will receive radiation therapy (ordered by the treating Radiation Oncologist) as part of their treatment regimen

Patient will undergo a planning CT scan with tumor motion assessment (planning 4D-CT ordered by the treating Radiation Oncologist) as part of their treatment regimen

Patient has signed informed consent and is willing to comply with the 4D-MRI imaging protocol

Subject is 18 or older

Subject has signed informed consent and is willing to comply with the 4D-MRI imaging protocol

Exclusion

Any condition for which a MRI procedure is contraindicated including presence of metallic material in the body, such as pacemakers, non- MRI compatible surgical clips, shrapnel, etc.

Subjects who have difficulty lying flat on their back for extended periods of time

Patients with any serious/poorly controlled medical or psychological conditions that would complicate protocol compliance

Too large to adequately fit in the magnet bore or RF coils

Claustrophobia

Females who are pregnant or lactating

Presence of active or chronic infection

What this trial measures

Image quality metrics in healthy volunteerssingle imaging session, lasting up to 2 hours
General 4D-MRI image quality will be assessed based on signal-to-noise ratio, number of distinct images per breathing cycle, total necessary imaging time, and image quality index.
Image quality metrics in cancer patientssingle imaging session, lasting up to 2 hours
We hypothesize that our ultra-quality 4D-MRI methodology will outperform 4D-CT for motion management of radiotherapy in the lungs and the liver. We will test this hypothesis by comparing image quality based on tumor volume consistency, number of trackable landmarks, motion measurement accuracy, and image quality index.
DVF errors in healthy volunteers and cancer patientssingle imaging session, lasting up to 2 hours
We hypothesize that our motion modeling method based on 4D-MRI will outperform current DIR algorithms for respiratory motion estimation. We will test this hypothesis by comparing our method to five existing DIR algorithms, based on the magnitude error (Em) and the angular error (Ea) of the calculated deformation vector field (DVF).

Trialytics