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

Study Overview
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Brief Summary
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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.

Official Title
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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.

Conditions
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Magnetic Resonance Imaging, Cardiac Magnetic Resonance Imaging, Deep-Learning, Left Ventricular Ejection Fraction

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Magnetic resonance imaging, Cardiac magnetic resonance imaging, Deep-Learning, Left ventricular ejection fraction, Left ventricular end systolic volume, left ventricular end diastolic volume