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

Sponsor

Centre Hospitalier Universitaire, Amiens (Other)

Overall Status

Recruiting

CT.gov ID

NCT05105984

Collaborator

(none)

Enrollment

Location

12.8

Anticipated Duration (Months)

5.8

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Magnetic Resonance Imaging Cardiac Magnetic Resonance Imaging Deep-Learning Left Ventricular Ejection Fraction

Study Design

Study Type:

Observational

Anticipated Enrollment :

75 participants

Observational Model:

Case-Only

Time Perspective:

Prospective

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.

Actual Study Start Date :

Jul 6, 2021

Anticipated Primary Completion Date :

Aug 1, 2022

Anticipated Study Completion Date :

Aug 1, 2022

Outcome Measures

Primary Outcome Measures

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

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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