Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorIRCCS Policlinico S. Donato
About this trial
This study aims to enhance and streamline intracardiac 4D Flow magnetic resonance imaging (MRI) processing by increasing automation for the quantitative and systematic assessment of left ventricular (LV) dysfunction. The study is designed to achieve the following three objectives.
The primary objective is to develop a convolutional neural network (CNN)-based deep learning model for the automatic segmentation of the LV endocardial contour throughout the cardiac cycle using intracavitary MRI data. To support model training, a dataset of LV endocardial wall segmentations will be generated from balanced steady-state free precession (bSSFP) images. A purpose-built retrospective MRI database of bSSFP images will be retrieved to accelerate training set creation.
The secondary objective is to develop a numerical framework for non-invasive MRI-based pressure-volume (PV) loop reconstruction and calculation of simplified hemodynamic force descriptors (HDFs). A prospective cohort of patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR) will be enrolled. Pre-procedural non-contrast 4D Flow MRI will be acquired, and non-invasive MRI-derived PV loops will be quantitatively compared with invasive catheter-based PV loop measurements. In addition, simplified HDFs will be compared with 4D Flow-derived HDFs to assess their agreement and their potential to elucidate specific features of heart failure-related LV dysfunction.
The tertiary objective is to establish the foundation for a unified, standalone, and clinically deployable framework for comprehensive, automated, and clinician-friendly analysis of LV hemodynamics based on 4D Flow MRI. Internal testing, benchmarking, and structured evaluation by clinical end-users with prior 4D Flow MRI research experience will be conducted to collect feedback and guide further development and clinical translation.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Adult patients (age > 18 years old);
Diagnosis of severe AS defined according to ESC guidelines with indication to TAVR;
Severe aortic stenosis both in normal/high flow status and in low flow status;
Signed informed written consent.
Disqualifiers
Contraindication to cardiac MRI due to previous implant with ferromagnetic components;
Poor MRI quality impairing image post-processing;
Claustrophobia;
Unwilling to sign the informed consent.
Trial design
Design model
Single group
Treatments tested in this trial
Cardiac MRI with 4D Flow acquisition and invasive signal routinely collected during transcatheter aortic valve replacement
Diagnostic testCardiac magnetic resonance imaging, including standard cine imaging and non-contrast 4D Flow MRI acquisition. In the prospective phase, invasive and non-invasive hemodynamic signals routinely collected during the transcatheter aortic valve replacement procedure are recorded for research analysis. No additional procedures beyond standard clinical practice are required. This is a low-intervention interventional study in which all imaging acquisitions and hemodynamic measurements are performed according to standard clinical practice, with no modification of diagnostic or therapeutic pathways.
Treatment groups
190 Participants
are divided into 1 treatment groupGroup A: TRANSLATE Study PopulationOther 1 intervention
Trial outcomes
Primary outcomes
1
Accuracy of Automatic Left Ventricular Endocardial Segmentation
Accuracy of a convolutional neural network (CNN)-based model for automatic delineation of the left ventricular (LV) endocardial contour from short-axis cine balanced steady-state free precession (bSSFP) MRI images throughout the cardiac cycle. Automatically generated contours will be compared with expert manual segmentations (ground truth). Segmentation performance will be quantified using the Dice Similarity Index (DICE) and Hausdorff Distance (HD). Inter- and intra-operator variability of manual segmentation and agreement between manual and automatic contours will also be assessed using Bland-Altman analysis.
Time frame
Completion of the retrospective analysis of cardiac MRI datasets (6 months)Secondary outcomes
1
Agreement Between Non-Invasive MRI-Based and Invasive Pressure-Volume Loop Parameters
Quantitative agreement between non-invasive MRI-derived left ventricular pressure-volume (PV) loop parameters and invasive catheter-based PV loop measurements acquired during the transcatheter aortic valve replacement (TAVR) procedure. Agreement and correlation will be assessed for clinically relevant LV parameters using correlation analysis and limits of agreement.
Time frame
Up to 1 week after TAVR2
Agreement Between Simplified and 4D Flow MRI-Based Hemodynamic Forces
Level of agreement and correlation between simplified hemodynamic force descriptors (HDFs) and HDFs calculated using standard intracardiac 4D Flow MRI-based analysis (reference standard). Comparisons will be performed for basal-apical, septal-lateral, and inferior-anterior force components using correlation analysis and Bland-Altman methods. Validation will also include comparison with HDF measurements obtained using a commercial prototype software tool.
Time frame
Up to 1 week after TAVR3
Correlation Between Hemodynamic Forces and LV Volumes
Correlation between LV hemodynamic forces (quantified using 4D Flow MRI and a simplified cine MRI-based method) and LV volumes, namely LV end-diastolic volume (LVEDV, expressed in ml) and LV end-systolic volume (LVESV, expressed in ml). Separate correlations will be calculated for LVEDV and LVESV, respectively.
Time frame
Up to 1 week after TAVR4
Correlation Between Hemodynamic Forces and LV Global Longitudinal and Circumferential Strain
Correlation between LV hemodynamic forces (quantified using 4D Flow MRI and a simplified cine MRI-based method) and LV endocardial parameters of deformation, namely global longitudinal strain (GLS) and global circumferential strain (GCS). Strain values will be expressed as percentage (%) deformation with separate correlations calculated for GLS and GCS, respectively.
Time frame
Up to 1 week after TAVRSponsors and contacts
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Source ClinicalTrials.gov