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

NCT05105984 | Completed

• 1 other identifier: PI2021_843_0157
• Study Type: observational
• Target: 54
• Locations: 1 country
• Sites: 1

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.

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

Outcome Measures

Primary Outcomes (1)

• 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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Age \> or = 18 years old
• Ischemic heart disease
• Ability of the subject to understand and express his consent
• Affiliation to the social security scheme

You may not qualify if:

• 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

Sponsors & Collaborators

• Centre Hospitalier Universitaire, Amiens: lead

Study Sites (1)

CHU Amiens-Picardie

Amiens, France, 80000, France

Location

Related Publications (1)

• Monteuuis D, Bouzerar R, Dantoing C, Poujol J, Bohbot Y, Renard C. Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease. AJR Am J Roentgenol. 2024 May;222(5):e2330272. doi: 10.2214/AJR.23.30272. Epub 2024 Feb 7.

  PMID: 38323784 RESULT

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 2, 2021
• First Posted: November 3, 2021
• Study Start: April 14, 2022
• Primary Completion: April 24, 2023
• Study Completion: January 29, 2024
• Last Updated: November 19, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)