Hang Jin

Introduction: Persistent cardiac symptoms after COVID-19, termed post-acute sequelae of SARS-CoV-2 infection with cardiovascular involvement (PASC-CVS), remain poorly understood. This study aims to reveal myocardial involvement and long-term cardiac magnetic resonance (CMR) changes in PASC-CVS patients, providing insights into disease progression and recovery. Methods: This prospective single-center study in China enrolled 110 patients with PASC-CVS from March 2023 to April 2024. All participants underwent multiparametric CMR imaging to explore abnormal CMR manifestations in PASC-CVS patients. Clinical symptoms and CMR examinations were collected again one year later. Statistical analysis included the Wilcoxon signed-rank test and logistic regression. Results: Compared to controls, PASC-CVS patients exhibited significant differences in multiparametric CMR parameters, including Quantification of late gadolinium enhancement (LGE) (%) [odds ratio (OR): 6.36], Global extracellular volume (ECV) (ms) (OR: 1.33), Heart rate (bpm) (OR: 1.08), left ventricular global longitudinal strain (LV GLS) (%) (OR: 1.44), and Global native T1 (ms) (OR: 1.01). The combined multiparametric CMR parameters (Quantification of LGE, Global ECV, Heart rate, LV GLS, Global Post T1, and Global native T1) achieved an area under the curve (AUC) of 0.94, indicating excellent predictive value. At the one-year follow-up ( n = 101), significant clinical improvement was observed, with a reduction in PASC scores (a 12-point symptom-based threshold framework) from 14.1 to 5.4 ( P < 0.001). Repeat CMR in 20 patients revealed improvements in right ventricular ejection fraction (RV EF) (52.1% to 59.5%, P < 0.001) and global T2 (43.6 ms to 39.5 ms, P < 0.001). However, the proportion of patients showing recovery in Quantification of LGE was 0%, suggesting limited clinical significance of its subtle changes. Conclusions: Our study reveals myocardial involvement in PASC-CVS patients, with follow-up improvements in RV EF and global T2, underscoring the utility of multiparametric CMR in monitoring and managing PASC-CVS.

Myocardial infarction (MI) remains a major cause of death and disability. Although late gadolinium enhancement (LGE) cardiac MRI is the reference for assessing myocardial viability, it requires contrast injection, complex protocols, and added cost. Prior virtual LGE approaches-mostly GAN-based-mainly use cine or T1 mapping and ignore T2-weighted short-tau inversion recovery (T2-STIR), which is highly sensitive to edema in acute MI. They also typically require manual post-hoc delineation of infarcts. We propose SSDiff (Synthesis joint Segmentation Diffusion), a multitask conditional diffusion framework that synthesizes contrast-free virtual LGE from routine cine + T2-STIR for acute infarct assessment and simultaneously segments myocardium, ventricular blood pool, and infarct. SSDiff introduces a feature-disentangled attention module that isolates sequence-specific cues to steer the diffusion process, and a cross-fusion module that aligns synthesis and segmentation decoders for mutual optimization. Evaluated on a multi-center, multi-vendor dataset of 409 subjects (2,177 aligned cine-T2-STIR-LGE triplets), SSDiff yields significant gains in synthetic image quality and downstream segmentation accuracy over strong baselines. Beyond serving as a clinically feasible alternative when LGE is unavailable or contraindicated, SSDiff also generates paired image-mask samples that augment LGE-scarce training, highlighting its practical utility and translational potential. Code is available at: https://github.com/QijingGJ/SSDiff.

Novel deep learning–enhanced compressed sensing single-shot cine cardiac MRI, combined with deep learning reconstruction, delivered better image quality and comparable ventricular measurements to balanced steady-state free precession cine MRI.

BACKGROUND: This study aims to develop a diffusion model-based framework for generating late gadolinium enhancement (LGE)-like images without contrast. The resulting synthetic images are then comprehensively evaluated for subjective and objective image quality, as well as their clinical utility for quantifying scar in acute myocardial infarction. METHODS: In this retrospective study, we developed a diffusion mode-based framework, multisequence guided diffusion to generate synthetic native enhancement (SNE) images from cine magnetic resonance imaging, and T2 short tau inversion recovery sequences. Data were collected from 331 patients with acute myocardial infarction across 3 centers from January 2014 to July 2024. Subjective and objective image qualities were assessed using Likert scoring and contrast ratio analyses on both internal and external cohorts, comparing SNE with standard LGE to evaluate group differences. Myocardial contours were manually delineated, and scar size and transmurality were quantified using the full-width at half-maximum method to assess the accuracy of myocardial infarction detection. RESULTS: In comparisons with general generative models and multimodal fusion–based generative approaches, multisequence guided diffusion demonstrated more favorable visual perceptual quality and the closest data distribution alignment to conventional LGE. SNE demonstrated significantly higher quality than LGE (internal: 4.250 [4.000–4.750] versus 4.000 [3.750–4.500]; external: 4.250 [4.000–4.750] versus 4.000 [3.500–4.250]; P P R =0.839; external R =0.816; P R =0.792; external R =0.758; P <0.001) with minimal biases (scar size: 2.490% internal, 2.222% external; transmurality: 2.984% internal, 2.225% external), indicating accurate scar depiction and robust generalizability. CONCLUSIONS: SNE demonstrated strong agreement with LGE in quantitative assessment of acute myocardial infarction scar, with comparable or improved image quality.

Objective: To evaluate the image quality of the free-breathing, non-ECG-multitasking T1 mapping and to compare T1 values, lesion area, and ROC curve with those derived from the traditional MOLLI sequence in ischaemic and non-ischaemic cardiomyopathy. Methods: In vitro, T1 values from CMR multitasking and MOLLI images were compared in 4 phantoms. In vivo studies included 108 patients (38 ischaemic cardiomyopathy, 70 non-ischaemic cardiomyopathy) and 31 healthy volunteers. All underwent mid-ventricular short-axis CMR Multitasking (free-breathing) and MOLLI (breath-held). Image quality was scored (4-point scale). T1 values and lesion areas were compared via linear regression, Bland-Altman analysis, and ROC curves (LGE as reference). Results: Phantom T1 values showed excellent agreement (Multitasking versus reference: R2 = 0.99, P < .001). In vivo, multitasking strongly correlated with MOLLI for native T1 in healthy subjects (R2 = 0.69), ischaemic (R2 = 0.71), and non-ischaemic groups (R2 = 0.81, all P < .001). Multitasking demonstrated superior image quality in patients with breath-holding difficulties and adverse heart rates (P < .001). MOLLI image quality declined with breath-holding difficulties, arrhythmia, and high BMI (P < .05); multitasking was less affected but still impacted by BMI (P < .05). Lesion areas showed no inter-sequence differences (P > .05). ROC analysis revealed comparable diagnostic accuracy between sequences in stable patients (P > .05), but superior multitasking performance in breath-holding difficulties and arrhythmia subgroups (P < .05). Conclusion: Multitasking provides accurate T1 quantification and lesion delineation in cardiomyopathy, with enhanced robustness for patients with poor breath-holding or arrhythmia. BMI-related limitations warrant technical refinement. Advances in knowledge: Free-breathing, non-ECG CMR multitasking achieves accurate T1 mapping in ischaemic/non-ischaemic cardiomyopathy, overcoming ECG/respiratory limitation.