Heart DiseasesDL-SB-CMRNCT07029789
Deep Learning Super-Resolution Single-Beat CMR
An observational study of Heart Diseases and Myocardial Disease, run by University Hospital, Bonn at 1 site in Germany.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Diseases, Myocardial Disease
- How many people
- 107 participants
- Where
- 1 siteGermany
- Study dates
- 1 May 2024to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with clinical indication for CMR
Who may be able to join
- Clinical indication for CMR
- Aged 18 years or older.
- Willing to participate in the study.
- Able and willing to provide signed informed consent.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Clinical Evaluation of Deep Learning-Enhanced Super-Resolution Single-Beat CMR: Prospective Comparison Study
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.
Read the 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
- Keywords
- deep learning, cardiovascular magnetic resonance, image reconstructions, cardiac imaging techniques, single-beat, super-resolution, accelerated imaging
- Medical subject headings
- Heart Diseases, Cardiomyopathies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patient cohort | * suspected myocardial disease with clinical indication for CMR * undergoing one CMR with two integrated protocols (standard and DL single beat protocol) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: May - December 2024
Secondary outcomes (1)
Scan time
Comparison of total scan duration between the deep learning-based single-beat SuperRes CMR protocol and the standard segmented CMR protocol.
Measured: May - December 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jun 2025
- Study start1 May 2024
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 107 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Bonn
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alexander Isaak, University Hospital, Bonn
- Investigators
- Alexander Isaak, PD Dr., Principal investigator, University Hospital Bonn, GermanyJulian Luetkens, Prof., Study director, University Hospital Bonn, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07029789
- Sponsor's protocol ID
- 2024-379-BO
- Acronym
- DL-SB-CMR
- Status last verified
- Jun 2025
- First submitted
- 3 Jun 2025
- Last updated
- 26 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Evaluation of Deep Learning-Enhanced Super-Resolution Single-Beat CMR: Prospective Comparison Study
- ClinicalTrials.gov ID
- NCT07029789
- Acronym / sponsor ID
- DL-SB-CMR / 2024-379-BO
- Conditions
- Heart Diseases, Myocardial Disease
- Keywords
- deep learning, cardiovascular magnetic resonance, image reconstructions, cardiac imaging techniques, single-beat, super-resolution, accelerated imaging
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 107 participants (actual)
- Lead sponsor
- University Hospital, BonnUniversity, hospital or other
- Study start
- 1 May 2024 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 19 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patient cohort | – | –* suspected myocardial disease with clinical indication for CMR * undergoing one CMR with two integrated protocols (standard and DL single beat protocol) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with clinical indication for CMR
Key inclusion criteria (4 listed)
- Clinical indication for CMR
- Aged 18 years or older.
- Willing to participate in the study.
- Able and willing to provide signed informed consent.
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic interchangeability
Time frame: May - December 2024
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.
1 secondary outcome
Scan time
Time frame: May - December 2024
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alexander Isaak, PD Dr. principal investigator
University Hospital Bonn, Germany
Julian Luetkens, Prof. study director
University Hospital Bonn, Germany
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Diseases and Myocardial Disease, run by University Hospital, Bonn at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic interchangeability (time frame: May - December 2024). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2025.