MedTrace Logo

Deep Learning Super-Resolution Single-Beat CMR

NCT07029789 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 107

Last updated 2025-06-26

No results posted yet for this study

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

Sponsors & Collaborators

  • University Hospital, Bonn

    lead OTHER

Principal Investigators

  • Alexander Isaak, PD Dr. · University Hospital Bonn, Germany

  • Julian Luetkens, Prof. · University Hospital Bonn, Germany

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-01
Primary Completion
2024-12-31
Completion
2024-12-31

Countries

  • Germany

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07029789 on ClinicalTrials.gov