Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 190
- 试验地点
- 1
- 主要终点
- Accuracy of Automatic Left Ventricular Endocardial Segmentation
研究概览
简要总结
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.
详细描述
The study includes a retrospective and a prospective arm, addressing methodological development, clinical validation, and translational implementation of advanced MRI-based analysis tools.
Within the retrospective arm, a database of short-axis cine balanced steady-state free precession (bSSFP) images of the LV will be retrieved retrospectively and anonymized prior to analysis. The dataset will be divided into a training set (approximately 75% of cases) and a test set (approximately 25%).
For all MRI datasets included in the training set, LV endocardial contours will be delineated throughout the cardiac cycle, employing semi-automatic segmentation tools (Medviso Segment) with manual corrections applied as necessary to ensure accuracy.
The training dataset, together with the corresponding ground-truth LV endocardial segmentations, will be used to train a deep learning convolutional neural network (CNN), e.g., a ResNet architecture, for the automatic delineation of LV endocardial contours from short-axis cine images.
The remaining test set will be used to evaluate the performance of the trained CNN. Automatically generated LV endocardial contours will be compared with the corresponding manual segmentations to benchmark segmentation accuracy and robustness.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
TRANSLATE Study Population
The retrospective phase includes adult patients who previously underwent clinically indicated cardiac MRI for left ventricular functional assessment. The prospective phase includes patients with severe aortic stenosis undergoing transcatheter aortic valve replacement, who undergo additional non-contrast 4D Flow MRI and standard invasive hemodynamic measurements as part of routine clinical care. Data from both phases are used for development and validation of automated MRI-based analysis methods.
干预措施: Cardiac MRI with 4D Flow acquisition and invasive signal routinely collected during transcatheter aortic valve replacement (Diagnostic Test)
结局指标
主要结局
Accuracy of Automatic Left Ventricular Endocardial Segmentation
时间窗: 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.
次要结局
- Agreement Between Non-Invasive MRI-Based and Invasive Pressure-Volume Loop Parameters(Up to 1 week after TAVR)
- Agreement Between Simplified and 4D Flow MRI-Based Hemodynamic Forces(Up to 1 week after TAVR)
- Correlation Between Hemodynamic Forces and LV Volumes(Up to 1 week after TAVR)
- Correlation Between Hemodynamic Forces and LV Global Longitudinal and Circumferential Strain(Up to 1 week after TAVR)