Deep Learning Super-Resolution Single-Beat CMR

NCT07029789 | Completed

• 1 other identifier: 2024-379-BO
• Study Type: observational
• Target: 107
• Locations: 1 country
• Sites: 1

Brief Summary

Deep learning super-resolution reconstruction is an emerging technique that enhances the resolution of cardiac magnetic resonance (CMR) scans beyond the original acquisition through post-processing. This study investigates whether a deep learning-based single-beat super-resolution CMR protocol-including cine, T2-STIR, and LGE sequences-can provide diagnostic equivalence to a standard segmented CMR protocol. Total scan time, diagnostic confidence, and diagnostic interchangeability are compared between protocols, with particular focus on wall motion abnormalities, myocardial edema, pericardial effusion, late gadolinium enhancement and final diagnosis. The goal is to assess diagnostic interchangeability while improving efficiency and motion robustness.

Conditions

Heart Diseases; Myocardial Disease

Keywords

deep learning; cardiovascular magnetic resonance; image reconstructions; cardiac imaging techniques; single-beat; super-resolution; accelerated imaging

Outcome Measures

Primary Outcomes (1)

• Diagnostic interchangeability

  Assessment of diagnostic interchangeability between the deep learning-based single-beat SuperRes CMR protocol and the standard segmented CMR protocol. Diagnostic categories include wall motion abnormalities, pericardial effusion, myocardial edema, late gadolinium enhancement, and the final CMR diagnosis. Interchangeability was evaluated using generalized estimating equations with bootstrapped 95% confidence intervals and a predefined equivalence margin of ±5%. For each category, the outcome is expressed as an individual equivalence index (%), defined as the difference in agreement probabilities.

  May - December 2024

Secondary Outcomes (1)

• Scan time

  May - December 2024

Study Arms (1)

Patient cohort

* suspected myocardial disease with clinical indication for CMR * undergoing one CMR with two integrated protocols (standard and DL single beat protocol)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with clinical indication for CMR

You may qualify if:

• Clinical indication for CMR
• Aged 18 years or older.
• Willing to participate in the study.
• Able and willing to provide signed informed consent.

You may not qualify if:

• Pregnant or breastfeeding women
• Non-removable magnetic metallic implants, prosthetic devices, or extensive tattoos covering large areas of the body
• Presence of a non-MRI safe pacemaker or neurostimulator

Sponsors & Collaborators

• University Hospital, Bonn: lead

Study Sites (1)

University Hospital Bonn

Bonn, North Rhine-Westphalia, 53123, Germany

Location

MeSH Terms

Conditions

Heart Diseases; Cardiomyopathies

Condition Hierarchy (Ancestors)

Cardiovascular Diseases

Study Officials

• Alexander Isaak, PD Dr.

  University Hospital Bonn, Germany

  PRINCIPAL INVESTIGATOR

• Julian Luetkens, Prof.

  University Hospital Bonn, Germany

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Radiologist, Radiology Clinic

Study Record Dates

• First Submitted: June 3, 2025
• First Posted: June 19, 2025
• Study Start: May 1, 2024
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: June 26, 2025

Record last verified: 2025-06

Locations

DE (1)