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

Magnetic Resonance Imaging Clinical Trial

— CINEDL  

Official title:

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.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05105984
• Other study ID #: PI2021_843_0157
• Status: Recruiting
• Start date: July 6, 2021
• Est. completion date: August 2023

Study information

• Verified date: February 2023
• Source: Centre Hospitalier Universitaire, Amiens
• Contact: Cédric Renard, MD
• Phone: 03 22 08 75 37
• Email: renard.cedric[@]chu-amiens.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 75
• Est. completion date: August 2023
• Est. primary completion date: August 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Cardiac Magnetic Resonance Imaging
• Coronary Artery Disease
• Heart Diseases
• Left Ventricular Ejection Fraction
• Magnetic Resonance Imaging
• Myocardial Ischemia

Locations

Country	Name	City	State
France	CHU Amiens-Picardie	Amiens

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire, Amiens

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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