I42 I51
Conditions
Interventions
Group 1: For healthy subjects:
A cardiac MRI without contrast agent.
Group 2: For patients:
Patients receive a contrast-enhanced cardiac MRI due to a clinical indication.
The additional study-related
Sponsors
Universitätsklinikum Tübingen
Eligibility
Sex/Gender
All
Age
No minimum to 99 Years
Inclusion criteria
Inclusion criteria: For healthy subjects: - Age =18 years, - Written informed consent, including consent to report incidental findings. For patients: - Suspicion of cardiomyopathy and / or myocardial fibrosis, - Clinical indication for cardiac MRI by treating cardiologist, - Age =18 years, - Written consent of the patient/ Legal guardians Amendment 1: Inclusion of children under 18 years of age possible: Children or adolescent patients with congenital and acquired heart defects and indication for cardiac MRI
Exclusion criteria
Exclusion criteria: For healthy subjects: - Subjects who are not capable of giving consent, - Presence of non-MR-compatible implants or any kind of metal in and on the body (e.g. pacemakers, artificial heart valves, implanted magnetic metal parts, etc. that are not MR-compatible), - Increased sensitivity to loud noises, - Claustrophobia, - Obesity with >150 kg body weight. For patients: - Presence of non-MR compatible implants or any type of metal in and on the body (e.g. pacemakers, artificial heart valves, implanted magnetic metal parts, etc. that are not MR compatible), - Increased sensitivity to loud noises, - Claustrophobia, - Obesity with >150 kg body weight, - Contraindication to MRI-contrast agent, - Limited capacity to give consent.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The primary objective is to investigate the feasibility of valid cardiac MRI imaging with deep learning image reconstruction in patients with cardiomyopathy. In healthy volunteers, the possible acceleration factor for cardiac MRI will first be narrowed down using MR sequences with different acceleration levels, in order to use the selected sequence modifications in the patients in a second step. | — |
Secondary
| Measure | Time frame |
|---|---|
| - Review of artefact susceptibility and identification of causes of artefacts in CMR, - Improve CMR image quality through deep learning image reconstruction, - Predictability of target areas for electrophysiological mapping and ablation based on CMR. | — |
Countries
Germany
Contacts
Public ContactSimon Greulich
Universitätsklinikum Tübingen, Innere Medizin III Tübingen, Abteilung für Kardiologie & Angiologie
Outcome results
None listed