Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent‐Free T1‐Rho Mapping With Fully Automated Analysis

Author:

de Villedon de Naide Victor12,Narceau Kalvin1,Ozenne Valery1,Villegas‐Martinez Manuel12,Nogues Victor1,Brillet Nina1,Huiyue Zhang Jana3,Benlala Ilyes2,Stuber Matthias134,Cochet Hubert12,Bustin Aurélien123ORCID

Affiliation:

1. IHU LIRYC, Electrophysiology and Heart Modeling Institute Université de Bordeaux, INSERM, Centre de Recherche Cardio‐Thoracique de Bordeaux, U1045 Pessac France

2. Department of Cardiothoracic Imaging Hôpital Cardiologique du Haut‐Lévêque, CHU de Bordeaux Pessac France

3. Department of Diagnostic and Interventional Radiology Lausanne University Hospital and University of Lausanne Lausanne Switzerland

4. Center for Biomedical Imaging (CIBM) Lausanne Switzerland

Abstract

BackgroundMyocardial T1‐rho (T1ρ) mapping is a promising method for identifying and quantifying myocardial injuries without contrast agents, but its clinical use is hindered by the lack of dedicated analysis tools.PurposeTo explore the feasibility of clinically integrated artificial intelligence‐driven analysis for efficient and automated myocardial T1ρ mapping.Study TypeRetrospective.PopulationFive hundred seventy‐three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases.Field Strength/SequenceSingle‐shot bSSFP T1ρ mapping sequence at 1.5 T.AssessmentThe automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16‐segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured.Statistical TestsIntraclass correlation coefficients (ICCs) and Bland–Altman analysis (bias ±2SD); Paired Student's t‐tests and one‐way ANOVA. A P value <0.05 was considered significant.ResultsThe automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of −0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6–70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4–75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual‐derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75).Data ConclusionAutomated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease.Evidence Level3Technical EfficacyStage 1

Funder

European Research Council

Agence Nationale de la Recherche

Publisher

Wiley

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