Deep Learning Super-Resolution Single-Beat CMR

NCT ID: NCT07029789

Last Updated: 2025-06-26

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 107 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-05-01
• Study Completion Date: 2024-12-31

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.

Detailed Description

Cardiac magnetic resonance (CMR) is the gold standard for non-invasive assessment of myocardial diseases, providing comprehensive information through e.g. cine imaging, T2-weighted sequences, and late gadolinium enhancement (LGE). Conventional CMR protocols typically rely on segmented (multi-shot) acquisitions over multiple heartbeats and require repeated breath-holds, which can limit patient comfort and compliance. While these segmented sequences offer high spatial resolution, they are prone to motion and respiratory artifacts-particularly in patients with arrhythmias or dyspnea-and contribute to long total examination times.

Recent advances in deep learning (DL) reconstruction techniques have enabled substantial acceleration of segmented CMR sequences, particularly for cine and LGE imaging. These approaches effectively reduce acquisition time but still rely on regular cardiac rhythm and adequate breath-holding capacity, limiting their applicability in more challenging patient populations. In contrast, single beat (or: single-shot) imaging acquires data within a single heartbeat, offering a motion-robust alternative, though at the cost of lower spatial resolution.

Efforts to streamline CMR are ongoing, with some studies proposing to reduce comprehensive exam times to 30 minutes or less. In parallel, full DL-based reconstruction MRI protocols are being increasingly explored across MRI domains, including neuroimaging and musculoskeletal imaging. Applying deep learning super-resolution to CMR, particularly in combination with single-beat acquisitions with the option of free-breathing acquisition, may enhance both speed and robustness.

This prospective investigates whether a deep learning-based single-beat super-resolution CMR protocol - including single-shot cine, T2-STIR, and LGE sequences in both short- and long-axis views - can provide diagnostic interchangeability to a standard segmented protocol. All participants undergo both protocols during the same exam session. Total scan times are compared between protocols using Student's t-test. Three blinded readers evaluate predefined diagnostic categories including wall motion abnormalities, pericardial effusion, myocardial edema, LGE, and the final CMR diagnosis. Generalized estimating equations with bootstrapped 95% confidence intervals and a predefined equivalence margin of ±5% was used for the interchangeability analysis. Agreement in categorical ratings was evaluated using Cohen's Kappa and Fleiss' Kappa, as appropriate. Diagnostic confidence was rated on a 5-point Likert scale.

Conditions

Heart Diseases; Myocardial Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Patient cohort

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

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

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

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University Hospital, Bonn

OTHER

Sponsor Role: lead

Responsible Party

Alexander Isaak

Radiologist, Radiology Clinic

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Alexander Isaak, PD Dr.

Role: PRINCIPAL_INVESTIGATOR

University Hospital Bonn, Germany

Julian Luetkens, Prof.

Role: STUDY_DIRECTOR

University Hospital Bonn, Germany

Locations

University Hospital Bonn

Bonn, North Rhine-Westphalia, Germany

Countries

Germany

Other Identifiers

2024-379-BO

Identifier Type: -

Identifier Source: org_study_id