Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging

Condition(s) studied

Investigational drug(s) / intervention(s)

Cardiac MRI with 4D Flow acquisition and invasive signal routinely collected during transcatheter aortic valve replacement: Cardiac 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.

Study summary

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

Primary outcome measure(s)

• Accuracy of Automatic Left Ventricular Endocardial Segmentation — Completion of the retrospective analysis of cardiac MRI datasets (6 months)
  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.

Trial sites (2)

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