Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-30 @ 9:55 PM
Study NCT ID:
NCT07455292
Status:
RECRUITING
Last Update Posted:
2026-03-06
First Post:
2026-02-20
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001024', 'term': 'Aortic Valve Stenosis'}], 'ancestors': [{'id': 'D000082862', 'term': 'Aortic Valve Disease'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D014694', 'term': 'Ventricular Outflow Obstruction'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': 'Arm description (retrospective and prospective phases):\n\n* retrospective phase includes adult patients who previously underwent clinically indicated cardiac MRI for LV functional assessment;\n* prospective phase includes patients with severe aortic stenosis undergoing TAVR, who undergo additional non-contrast 4D Flow MRI and standard invasive hemodynamic measurements as part of routine clinical care.\n\nApproximately 150 participants will be included in the retrospective phase and up to 40 participants i\n\nIntervention Description (prospective phase, low-risk and observational in nature):\n\n* cardiac magnetic resonance imaging, including standard cine imaging and non-contrast 4D Flow MRI acquisition;\n* invasive and non-invasive hemodynamic signals routinely collected during TAVR procedure are recorded for research analys. .'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 190}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-10-13', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2026-09-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-02', 'studyFirstSubmitDate': '2026-02-20', 'studyFirstSubmitQcDate': '2026-03-02', 'lastUpdatePostDateStruct': {'date': '2026-03-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of Automatic Left Ventricular Endocardial Segmentation', 'timeFrame': 'Completion of the retrospective analysis of cardiac MRI datasets (6 months)', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Agreement Between Non-Invasive MRI-Based and Invasive Pressure-Volume Loop Parameters', 'timeFrame': 'Up to 1 week after TAVR', 'description': '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.'}, {'measure': 'Agreement Between Simplified and 4D Flow MRI-Based Hemodynamic Forces', 'timeFrame': 'Up to 1 week after TAVR', 'description': '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.'}, {'measure': 'Correlation Between Hemodynamic Forces and LV Volumes', 'timeFrame': 'Up to 1 week after TAVR', 'description': '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.'}, {'measure': 'Correlation Between Hemodynamic Forces and LV Global Longitudinal and Circumferential Strain', 'timeFrame': 'Up to 1 week after TAVR', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['4D Flow MRI'], 'conditions': ['Left Ventricle Function', 'Transcatheter Aortic Valve Replacement (TAVR)', 'Aortic Valve Stenosis']}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.\n\nThe 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.\n\nThe 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.', 'detailedDescription': "The study includes a retrospective and a prospective arm, addressing methodological development, clinical validation, and translational implementation of advanced MRI-based analysis tools.\n\nWithin 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%).\n\nFor 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.\n\nThe 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.\n\nThe 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.\n\nAs part of the prospective arm of the study, enrolled patients will undergo non-contrast 4D Flow MRI acquisition prior to the scheduled intervention for transcatheter aortic valve replacement (TAVR).\n\nMRI examinations will be acquired on a 1.5 Tesla scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). Standard bSSFP cine sequences with retrospective ECG gating will be used to acquire short-axis and long-axis LV views (slice thickness 8 mm, no inter-slice gap, 30 reconstructed cardiac phases). In the same imaging session, 4D Flow MRI data will be acquired using a prototype time-resolved three-dimensional gradient echo sequence with three-directional velocity encoding. A parasagittal-oriented field of view covering the entire LV will be employed. Acquisitions will be performed during free breathing with retrospective ECG triggering and adaptive respiratory gating. Scan parameters will comply with the 2023 update of the 4D Flow Cardiovascular Magnetic Resonance Consensus Statement. No contrast agent will be administered. Non-invasive continuous blood pressure and relevant hemodynamic parameters will be also recorded from the finger arterial pressure waveform using a clinical-grade device (e.g., Finapres® NOVA).\n\nDuring the TAVR procedure, simultaneous invasive blood pressure measurements will be obtained in the ascending aorta and in the LV using two pigtail catheters, both before and after valve implantation, as required by standard clinical practice during TAVR, thus without impacting on the clinical workflow.\n\nNo variations in prophylaxis or follow-up procedures are expected compared with standard clinical practice. Therefore, no additional physical, psychological, or social risks or direct benefits are associated with participation in the study.\n\nPost-processing of 4D Flow MRI data will be performed using a dedicated workflow incorporating in-house software written in Matlab (The MathWorks Inc., Natick, MA, USA), based on prior experience. Processing steps will include correction for eddy currents and velocity aliasing. Intracavitary LV velocity fields will be extracted using both an in-house semi-automatic segmentation tool and the automated deep learning-based LV masking tool developed in the retrospective arm.\n\nExtracted velocity fields will be used to evaluate LV blood flow energetics, including kinetic energy and viscous energy dissipation, flow component subdivision, intraventricular pressure gradients, and intracavitary flow-mediated hemodynamic forces (HDFs). The resulting HDF vectors will be decomposed into basal-apical, septal-lateral, and inferior-anterior components, and their root mean square (RMS) values will be computed over the cardiac cycle.\n\nNon-invasive continuous blood pressure signals derived from finger arterial waveforms will be combined with MRI-derived LV volumes to estimate non-invasive LV pressure-volume (PV) loops using an established methodology. In parallel, invasive LV pressure data acquired during the TAVR procedure via pigtail catheter will be used to generate catheter-based PV loops. Non-invasive MRI-based PV loop parameters will be quantitatively compared with catheter-based measurements using clinically relevant LV indices.\n\nIntracavitary pressure gradients will be computed from the Navier-Stokes equations to derive HDF vectors for each cardiac frame by integrating pressure gradients over the entire LV volume. HDF vectors will be projected onto three orthogonal anatomical directions (basal-apical, septal-lateral, inferior-anterior), and root mean square (RMS) values of each component calculated over the cardiac cycle. Simplified HDFs will be computed using a recently developed method that does not require 4D Flow MRI data and they will be systematically compared with standard 4D Flow MRI-based HDFs on a patient-specific basis. For validation purposes, both simplified and 4D Flow-derived HDFs will also be compared with corresponding measurements obtained using the prototype HDF tool available in the commercial Medis Suite MR software.\n\nConventional MRI-derived parameters routinely used to assess LV function, including end-diastolic volume, end-systolic volume, and global longitudinal strain, will also be collected.\n\nDemographic variables (e.g., age and sex), relevant clinical characteristics, administered medical therapy, procedural TAVR data, and concomitant medications will be collected and anonymized prior to analysis.\n\nNo follow-up visits or assessments are planned as part of this study.\n\nA standalone application for intracardiac 4D Flow MRI analysis will be designed following an incremental build development model. The process will focus on defining the system requirements specification (SyRS), software requirements specification (SRS), and overall software architecture.\n\nA critical evaluation of existing state-of-the-art 4D Flow MRI workflows used in clinical research and of prototype tools available in commercial software (e.g., Medis Suite MR) will be conducted to identify strengths and limitations. Development will proceed through successive stages, with each stage implementing a specific functional component of the overall framework.\n\nThe standalone application will build upon existing in-house Matlab-based algorithms but will be implemented in a compiled programming language, allowing execution without requiring pre-installed software on the end-user's system. Development will be supported by a specialized consulting service.\n\nA preliminary version of the standalone application will undergo internal testing, benchmarking, and structured evaluation by a small group of clinical end-users with prior experience in 4D Flow MRI research. Users will independently test the software on predefined use cases. Clinical feedback, criticisms, and suggestions for further development will be collected through structured questionnaires or interviews and used to guide subsequent refinement and clinical translation."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adult patients (age \\> 18 years old);\n* Diagnosis of severe AS defined according to ESC guidelines with indication to TAVR;\n* Severe aortic stenosis both in normal/high flow status and in low flow status;\n* Signed informed written consent.\n\nExclusion Criteria:\n\n* Contraindication to cardiac MRI due to previous implant with ferromagnetic components;\n* Poor MRI quality impairing image post-processing;\n* Claustrophobia;\n* Unwilling to sign the informed consent.'}, 'identificationModule': {'nctId': 'NCT07455292', 'acronym': 'TRANSLATE', 'briefTitle': 'Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging', 'organization': {'class': 'OTHER', 'fullName': 'IRCCS Policlinico S. Donato'}, 'officialTitle': 'Phenotyping Left Ventricle Failure With Hemodynamic Biomarkers From 4D Flow Magnetic Resonance Imaging', 'orgStudyIdInfo': {'id': 'TRANSLATE'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'TRANSLATE Study Population', 'description': '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.', 'interventionNames': ['Diagnostic Test: Cardiac MRI with 4D Flow acquisition and invasive signal routinely collected during transcatheter aortic valve replacement']}], 'interventions': [{'name': 'Cardiac MRI with 4D Flow acquisition and invasive signal routinely collected during transcatheter aortic valve replacement', 'type': 'DIAGNOSTIC_TEST', 'otherNames': ['transcatheter aortic valve implantation', '4D Flow MRI', 'intracardic invasive pressure', 'non-invasive blood pressure'], 'description': '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.\n\nThis 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.', 'armGroupLabels': ['TRANSLATE Study Population']}]}, 'contactsLocationsModule': {'locations': [{'zip': '20097', 'city': 'San Donato Milanese', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Giandomenico Disabato, MD', 'role': 'CONTACT', 'email': 'giandomenico.disabato@grupposandonato.it', 'phone': '+390252774804'}, {'name': 'Francesco Sturla, PhD', 'role': 'CONTACT', 'email': 'francesco.sturla@grupposandonato.it', 'phone': '+390252774353'}, {'name': 'Giandomenico Disabato, MD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Riccardo Gorla, MD, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Francesco Sturla, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Massimo Lombardi, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Francesco Bedogni, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Sofia Di Filippo, MSc', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'IRCCS Policlinico San Donato', 'geoPoint': {'lat': 45.41047, 'lon': 9.26838}}, {'zip': '20097', 'city': 'San Donato Milanese', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Francesco Sturla, MD', 'role': 'CONTACT', 'email': 'francesco.sturla@grupposandonato.it', 'phone': '+390252774353'}, {'name': 'Riccardo Gorla, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Francesco Bedogni, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Massimo Lombardi, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Sofia Di Filippo, MSc', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'IRCCS Policlinico San Donato', 'geoPoint': {'lat': 45.41047, 'lon': 9.26838}}], 'centralContacts': [{'name': 'Giandomenico Disabato, MD', 'role': 'CONTACT', 'email': 'giandomenico.disabato@grupposandonato.it', 'phone': '+390252774804'}, {'name': 'Francesco Sturla, PhD', 'role': 'CONTACT', 'email': 'francesco.sturla@grupposandonato.it', 'phone': '+390252774353'}], 'overallOfficials': [{'name': 'Giandomenico Disabato, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'IRCCS Policlinico S. Donato'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'IRCCS Policlinico S. Donato', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}