BACKGROUND
Cardiac magnetic resonance (CMR) serves as a pivotal diagnostic tool for myocardial infarction with nonobstructive coronary arteries (MINOCA) and enables the precise quantification of epicardial adipose tissue (EAT).
OBJECTIVES
This study aimed to evaluate the predictive value of EAT for long-term major adverse cardiovascular events (MACE) in CMR-confirmed MINOCA and to determine whether EAT provides incremental prognostic utility.
METHODS
This single-center retrospective study enrolled 117 patients with a working diagnosis of MINOCA who demonstrated an ischemic myocardial injury pattern on CMR after exclusion of nonischemic mimics or normal CMR findings. EAT volume was divided by body surface area to yield the EAT volume index (EATVi).
RESULTS
During a median follow-up of 48 months (IQR: 39-63), MACE occurred in 28 of 117 patients (23.9%; 95% CI: 17.1%-32.4%). In multivariable Firth penalized Cox regression adjusting for age, global longitudinal strain, infarct size, and N-terminal pro-B-type natriuretic peptide, each 10 mL/m2 increase in EATVi was associated with a higher risk of MACE (HR = 1.35, 95% CI: 1.01-1.76, P = 0.040). The Harrell C-index increased from 0.728 (95% CI: 0.637-0.812) for the base model to 0.751 (95% CI: 0.653-0.839) after the addition of EATVi. Kaplan-Meier analysis demonstrated lower MACE-free survival among patients with EATVi >43.21 mL/m2 (selection-adjusted P = 0.002).
CONCLUSIONS
Among patients with CMR-confirmed MINOCA, higher EATVi was associated with an increased long-term risk of MACE. Given the potential influence of metabolic factors and residual confounding, these findings require validation in larger external cohorts.
Lei Chen, Wensu Chen, De-Xiang Zong et al.· JACC: Asia· 0 citations
BACKGROUND
Cardiac magnetic resonance feature tracking (CMR-FT) of left atrial (LA) strain is hindered by thin-wall contouring errors, motion heterogeneity, and temporal drift, while manual or landmark-based methods lack reproducibility and scalability.
METHODS
We retrospectively collected a multi-center, two-vendor cine MRI dataset. A multi-task learning model was developed to quantify LA strain directly from two-chamber and four-chamber cine images, by coupling a groupwise registration network with a segmentation network through a spatiotemporal cross-attention module and synergistic losses. Performance was benchmarked against common feature tracking algorithms, including optical flow, pairwise registration, and VoxelMorph, via various metrics such as mean-squared error, contour distance, mitral annular tracking accuracy, and drift error. Diagnostic performance to distinguish healthy from diseased subjects was assessed by ROC analysis.
RESULTS
546 subjects (142 healthy; age 49±18 years; 343 male) were included for method development and internal/external testing. The proposed method outperformed all other methods in tracking accuracy and reduced the drift effect commonly observed in optical flow and pairwise registration to a level comparable to fixed-reference learning-based registration. Inference required half a second. Automatic strains agreed closely with manual-segmentation-derived values (reservoir r=0.95, conduit r=0.96, booster r=0.92; all p<0.001). In the external dataset, all three strain components were lower in diseased subjects than normal controls (reservoir 24.4±13.2% vs 45.7±12.6%, conduit 14.1±8.8% vs 31.1±10.2%, and booster 10.3±6.4% vs 14.6±5.1%, all p<0.001). Compared with alternative methods, the automatic reservoir strain achieved the highest discriminative power across multiple diseased groups (AUC: 0.81-0.97).
CONCLUSION
A fully automatic, multi-task learning framework for LA strain quantification, validated in multi-center two-vendor data, enhances tracking accuracy and speed over prior methods, enabling rapid, scalable atrial function assessment in routine care. Source code is available at SJTU-CMRLab/Dual_Task_LA_Strain_Quantification.
Yi-Chen Zhao, Haiyang Chen, Yiwen Gong et al.· Journal of Cardiovascular Ma...· 0 citations
This document addresses the needs for an "ideal" post-processing software for 4D flow in patients with CHD and provides information on general requirements, velocity encoding, offset and aliasing correction, visualization, segmentation and reconstruction as well as output data.
Julio Garcia, Julia Geiger, Adam B. Christopher et al.· Journal of Cardiovascular Ma...· 0 citations
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