Clinical trial NCT05105984: Evaluation of a Free-breathing Cardiac Cine-MRI Sequence With Image Reconstructions by Deep-Learning in Ischemic Heart Disease
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.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Magnetic Resonance Imaging
Cardiac Magnetic Resonance Imaging
Deep-Learning
Left Ventricular Ejection Fraction
Keywords: Magnetic resonance imaging
Cardiac magnetic resonance imaging
Deep-Learning
Left ventricular ejection fraction
Left ventricular end systolic volume
left ventricular end diastolic volume
Lead sponsor: Centre Hospitalier Universitaire, Amiens
Sponsor class: OTHER
First posted: 2021-11-03
Enrollment: 75
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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

Primary outcomes:
measure: difference of LVEF measurements between Deep Learning reconstruction and the classic cine-MRI sequence
timeFrame: 5 minutes
Secondary outcomes:
Other outcomes:
Locations:
city: Amiens
country: France
facility: CHU Amiens-Picardie
Study officials:
References: