Cardiovascular Diseases
Conditions
Brief summary
Introduction: Inversion-recovery (IR) magnetic resonance (MR) sequences are commonly used to perform late-gadolinium enhancement (LGE) imaging during cardiac magnetic resonance (CMR) scans. Inversion Time (TI), i.e. the time between the 180° inverting pulse and the 90°-pulse, must be manually input to obtain optimal myocardium nulling. Determinants of this value are patient's, sequence, and contrast characteristics, and the time after contrast injection. The identification of the correct TI is pivotal to quality images. The determination of TI is mostly based on experience, and it can be challenging in some diseases and for less experienced operators. Aim of this study is to test in a clinical setting an Artificial Intelligence (AI) tool, which we developed to automatically predict TI in CMR post-contrast IR LGE sequences, named THAITI. THAITI performance will be evaluated in terms of 1) quality of images obtained using the AI-predicted TI with a 4-point Likert scale; 2) quality of images obtained using the AI-predicted TI in terms of Contrast-Enhancement ratio, i.e. the signal intensity of enhanced/remote myocardium in CMR-LGE images; 3) numbers of images that need to be reacquired; 4) average time duration of CMR-LGE imaging.
Interventions
DEVICETHAITI software
THAITI is an AI-based software which predicts on the fly personalised TI for late gadolinium enhancement imaging during cardiovascular magnetic resonance scans. The clinical investigators will be provided by the computer scientists investigators with a software, based on the developed AI model. During the CMR in the experimental group, investigators will input patients' data on the software (e.g. age, sex, dose of contrast…). The software will provide a TI value to be input in the MRI scanner. TI will be set accordingly to the AI prediction. A LGE series of 3 long axis (4-, 2- and 3-chambers view) and a short-axis stack will be acquired. For all the patients, a doctor expert in CMR will be at the scanner and quality check the images in real time. Every image where the myocardium is not optimally nulled will be repeated with a TI set by the CMR doctor.
Sponsors
Istituto Auxologico Italiano
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Patients in whom CMR-LGE is performed for a clinical reason * Mixed cardiac conditions (including cardiomyopathies, ischemic heart disease, normal scans, focal and diffuse myocardial pathological processes) * Both sexes * Any age * Availability of serum creatinine, measured within one month prior to CMR * Provision of the written informed consent
Exclusion criteria
* Non-contrast CMR * First-pass perfusion stress-CMR * Absolute contraindication to CMR * Inadequate overall image quality
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Images quality proportion | At examination | Proportion of images with optimal/good quality |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| contrast-enhancement ratio | At examination | Contrast-Enhancement ratio (CER) |
Countries
Italy
Outcome results
None listed