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

Recruiting

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

• Registration Number: NCT05105984
• Lead Sponsor: Centre Hospitalier Universitaire, Amiens

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-07-06
• Study First Submitted: 2021-11-02
• Study First Submitted QC: 2021-11-02
• Study First Posted: 2021-11-03
• Status Verified: 2023-02
• Last Update Submitted: 2023-02-07
• Last Update Posted: 2023-02-08
• Primary Completion: 2023-08
• Study Completion: 2023-08

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 75

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
difference of LVEF measurements between Deep Learning reconstruction and the classic cine-MRI sequence	5 minutes	difference of LVEF measurements between Deep Learning reconstruction and the classic cine-MRI sequence

Trial Locations

Locations (1)

CHU Amiens-Picardie; 🇫🇷 Amiens, France