Evaluation of a Free-breathing Cardiac Cine-MRI Sequence With Image Reconstructions by Deep-Learning in Ischemic Heart Disease (CINEDL)

Evaluation of a Free-breathing Cardiac Cine-MRI Sequence With Image Reconstructions Developed by Deep-Learning Compared to the Classic Apnea Cine-MRI Sequence in the Assessment of Ischemic Heart Disease.

Today, MRI is the gold standard for the precise assessment of left ventricular volume and function, but presents the drawback of having a long acquisition time and of generating motion artifacts, in particular respiratory artifacts, requiring repeated sequences in apnea to cover the whole cardiac volume. These apneas are difficult to achieve in patients with ischemic heart disease and may lead to degradation of the images, an increase in the duration of the examination by repeated acquisitions and therefore to diagnostic inaccuracies.

Artificial intelligence, already used in practice in cardiac MRI for automatic segmentation of the heart chambers, improves radiological interpretation with rapid and precise measurements. Deep-learning, which is part of artificial intelligence, would allow the reconstruction of cine-MRI sequences in free breathing, in order to overcome the artifacts from respiratory motions, and the improvement of diagnostic performance while improving examination conditions for patients.

Patients coming for a cardiac MRI for the assessment of ischemic heart disease will be eligible to the protocol. If the patient agrees to participate, a free-breathing cardiac cine-MRI sequence with Deep Learning based image reconstruction will be added to the usual protocol.

No follow-up will be required in this study.

Study Overview

• Status: Completed
• Conditions: Magnetic Resonance Imaging; Left Ventricular Ejection Fraction; Cardiac Magnetic Resonance Imaging; Deep-Learning

• Study Type: Observational
• Enrollment (Actual): 54

Contacts and Locations

Study Locations

• France
  • France
    • Amiens, France, France, 80000
      • CHU Amiens-Picardie

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Cardiac MRI is one of the examinations prescribed as part of routine care for this pathology. When the patient come for a cardiac MRI in the workup for ischemic heart disease, the patient will be asked on the day of the exam if he agrees to participate in the study. An information letter will have been sent to the patient before the appointment is made.

In the event that he agrees to participate, a free-breathing cardiac cine-MRI sequence with Deep Learning based image reconstruction will be appended to the usual protocol.

Description

Inclusion Criteria:

• Age > or = 18 years old
• Ischemic heart disease
• Ability of the subject to understand and express his consent
• Affiliation to the social security scheme

Exclusion Criteria:

• Major obesity (> 140kg) not allowing the patient to enter the tunnel of the machine whose diameter is less than 70cm
• Under 18 years old
• Pregnant woman
• Known allergy to gadolinium chelates
• Claustrophobia
• Any contraindication to MRI
• Arrhythmia
• Difficulty in holding apneas of more than 10 seconds

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
difference of LVEF measurements between Deep Learning reconstruction and the classic cine-MRI sequence	difference of LVEF measurements between Deep Learning reconstruction and the classic cine-MRI sequence	5 minutes

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire, Amiens

Publications and helpful links

General Publications

• Monteuuis D, Bouzerar R, Dantoing C, Poujol J, Bohbot Y, Renard C. Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease. AJR Am J Roentgenol. 2024 May;222(5):e2330272. doi: 10.2214/AJR.23.30272. Epub 2024 Feb 7.

Study record dates

Study Major Dates

• Study Start (Actual): April 14, 2022
• Primary Completion (Actual): April 24, 2023
• Study Completion (Actual): January 29, 2024

Study Registration Dates

• First Submitted: November 2, 2021
• First Submitted That Met QC Criteria: November 2, 2021
• First Posted (Actual): November 3, 2021

Study Record Updates

• Last Update Posted (Estimated): November 19, 2025
• Last Update Submitted That Met QC Criteria: November 18, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: Magnetic resonance imaging; Left ventricular ejection fraction; Cardiac magnetic resonance imaging; Deep-Learning; Left ventricular end systolic volume; left ventricular end diastolic volume
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Coronary Disease; Heart Diseases; Coronary Artery Disease; Myocardial Ischemia
• Other Study ID Numbers: PI2021_843_0157

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No