Detection of Intramyocardial Iron in Patients Following ST-Elevation Myocardial Infarction Using Cardiac Diffusion Tensor Imaging

Second-Order Motion-Compensated Echo-Planar Cardiac Diffusion-Weighted MRI: Usefulness of Compressed Sensitivity Encoding

BACKGROUND

Cardiac diffusion-weighted imaging (DWI) using second-order motion-compensated spin echo (M2C) can provide noninvasive in-vivo microstructural assessment, but limited by relatively low signal-to-noise ratio (SNR). Echo-planar imaging (EPI) with compressed sensitivity encoding (EPICS) could address these issues.

PURPOSE

To combine M2C DWI and EPCIS (M2C EPICS DWI), and compare image quality for M2C DWI.

STUDY TYPE

Prospective.

POPULATION

Ten ex-vivo hearts, 10 healthy volunteers (females, 5 [50%]; mean ± SD of age, 25 ± 4 years), and 12 patients with diseased hearts (female, 1 [8.3%]; mean ± SD of age, 44 ± 16 years; including coronary artery heart disease, congenital heart disease, dilated cardiomyopathy, amyloidosis, and myocarditis).

FIELD STRENGTH/SEQUENCE

3-T, M2C EPICS DWI, and M2C DWI.

ASSESSMENT

The apparent SNR (aSNR) and the rating scores were used to evaluate and compared image quality of all three groups. The aSNR was calculated using aSNR = Mean intensity myocardium / Standard deviation myocardium , and the myocardium was segmented manually. Three observers independently rated subjective image quality using a 5-point Likert scale.

STATISTICAL TESTS

Bland-Altman analysis and paired t-tests. The threshold for statistical significance was set at P < 0.05.

RESULTS

In healthy volunteers, the aSNR with a b-value of 450 s/mm2 acquired by M2C EPICS DWI was significantly higher than M2C DWI at in-plane resolutions of 3.0 × 3.0, 2.5 × 2.5, and 2.0 × 2.0 mm2. In patients with diseased hearts, the aSNR ofM2C EPICS DWI was also significantly higher than that for M2C DWI (bias of M2C EPICS-M2C = 1.999, 95% limits of agreement, 0.362 to 3.636; mean ± SD, 7.80 ± 1.37 vs. 5.80 ± 0.81). The ADC values of M2C EPICS was significantly higher than M2C DWI in in-vivo hearts. Over 80% of the images with rating scores for M2C EPICS DWI were higher than M2C DWI in in-vivo hearts.

DATA CONCLUSION

Cardiac imaging by M2C EPICS DWI may demonstrate better overall image quality and higher aSNR than M2C DWI.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Cardiac magnetic resonance imaging-derived pathophysiology and prognosis of diabetes mellitus with acute myocardial infarction after revascularization: a prospective cohort study

BACKGROUND

Little is known about the underlying factors contributing to unfavourable clinical outcomes in patients with diabetes mellitus (DM) complicated by new-onset acute myocardial infarction (AMI). The aim of this study was to investigate the impact of DM on the pathophysiologic features and prognosis of patients with new-onset AMI following successful revascularization by utilizing cardiac magnetic resonance (CMR).

METHODS

Consecutive patients diagnosed with new-onset AMI between June 2022 and January 2024 were included. All patients underwent culprit vessel revascularization upon admission and CMR imaging 3-7 days later. The primary clinical endpoint of this study was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs), for which the average follow-up was 10 months.

RESULTS

A total of 72 patients were divided into a DM group (n = 23) and a non-DM group (n = 49). Multivariate logistic regression analysis revealed that DM was an independent risk factor for the occurrence of microvascular obstruction. Multivariate linear regression analysis found that DM was the influencing factor of global radial strain (B = -4.107, t = -2.328, p = 0.023), while fasting blood glucose influenced infarct segment myocardial radial strain (B = -0.622, t = -2.032, p = 0.046). DM independently contributed to the risk of MACCEs following successful revascularization in patients with AMI (p < 0.05).

CONCLUSION

Comprehensive phenotypic characterization of myocardial injury and microcirculatory status could enable reliable identification of high-risk MACCEs in DM patients with new-onset AMI following successful revascularization.

Deep learning method with integrated invertible wavelet scattering for improving the quality ofin vivocardiac DTI

Objective.Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitations ofin vivocardiac diffusion tensor imaging (DTI). We propose a novel enhancement method that uses unsupervised learning based invertible wavelet scattering (IWS) to improve the quality ofin vivocardiac DTI.Approach.Our method starts by extracting nearly transformation-invariant features from multiple cardiac diffusion-weighted (DW) image acquisitions using multi-scale wavelet scattering (WS). Then, the relationship between the WS coefficients and DW images is learned through a multi-scale encoder and a decoder network. Using the trained encoder, the deep features of WS coefficients of multiple DW image acquisitions are further extracted and then fused using an average rule. Finally, using the fused WS features and trained decoder, the enhanced DW images are derived.Main result.We evaluate the performance of the proposed method by comparing it with several methods on threein vivocardiac DTI datasets in terms of SNR, contrast to noise ratio (CNR), fractional anisotropy (FA), mean diffusivity (MD) and helix angle (HA). Comparing against the best comparison method, SNR/CNR of diastolic, gastric peristalsis influenced, and end-systolic DW images were improved by 1%/16%, 5%/6%, and 56%/30%, respectively. The approach also yielded consistent FA and MD values and more coherent helical fiber structures than the comparison methods used in this work.Significance.The ablation results verify that using the transformation-invariant and noise-robust wavelet scattering features enables us to effectively explore the useful information from the limited data, providing a potential mean to alleviate the dependence of the fusion results on the number of repeated acquisitions, which is beneficial for dealing with the issues of noise and residual motion simultaneously and therefore improving the quality ofinvivocardiac DTI. Code can be found inhttps://github.com/strawberry1996/WS-MCNN.

Deciphering the Enigma of Intramyocardial Hemorrhage Following Reperfusion Therapy in Acute ST-Segment Elevation Myocardial Infarction: A Comprehensive Exploration from Mechanisms to Therapeutic Strategies

Acute ST-segment elevation myocardial infarction (STEMI) is a formidable challenge in cardiovascular medicine, demanding advanced reperfusion strategies such as emergency percutaneous coronary intervention. While successful revascularization is pivotal, the persistent "no-reflow" phenomenon remains a clinical hurdle, often intertwined with microvascular dysfunction. Within this intricate scenario, the emergence of intramyocardial hemorrhage (IMH) has garnered attention as a significant contributor. This review offers a detailed exploration of the multifaceted relationship between IMH and the "no-reflow" phenomenon, delving into the mechanisms governing IMH occurrence, state-of-the-art diagnostic modalities, predictive factors, clinical implications, and the evolving landscape of preventive and therapeutic strategies. The nuanced examination aims to deepen our comprehension of IMH, providing a foundation for the identification of innovative therapeutic avenues and enhanced clinical outcomes for STEMI patients.

The relationship between myocardial microstructure and strain in chronic infarction using cardiovascular magnetic resonance diffusion tensor imaging and feature tracking

BACKGROUND

Cardiac diffusion tensor imaging (cDTI) using cardiovascular magnetic resonance (CMR) is a novel technique for the non-invasive assessment of myocardial microstructure. Previous studies have shown myocardial infarction to result in loss of sheetlet angularity, derived by reduced secondary eigenvector (E2A) and reduction in subendocardial cardiomyocytes, evidenced by loss of myocytes with right-handed orientation (RHM) on helix angle (HA) maps. Myocardial strain assessed using feature tracking-CMR (FT-CMR) is a sensitive marker of sub-clinical myocardial dysfunction. We sought to explore the relationship between these two techniques (strain and cDTI) in patients at 3 months following ST-elevation MI (STEMI).

METHODS

32 patients (F = 28, 60 ± 10 years) underwent 3T CMR three months after STEMI (mean interval 105 ± 17 days) with second order motion compensated (M2), free-breathing spin echo cDTI, cine gradient echo and late gadolinium enhancement (LGE) imaging. HA maps divided into left-handed HA (LHM, - 90 < HA < - 30), circumferential HA (CM, - 30° < HA < 30°), and right-handed HA (RHM, 30° < HA < 90°) were reported as relative proportions. Global and segmental analysis was undertaken.

RESULTS

Mean left ventricular ejection fraction (LVEF) was 44 ± 10% with a mean infarct size of 18 ± 12 g and a mean infarct segment LGE enhancement of 66 ± 21%. Mean global radial strain was 19 ± 6, mean global circumferential strain was - 13 ± - 3 and mean global longitudinal strain was - 10 ± - 3. Global and segmental radial strain correlated significantly with E2A in infarcted segments (p = 0.002, p = 0.011). Both global and segmental longitudinal strain correlated with RHM of infarcted segments on HA maps (p < 0.001, p = 0.003). Mean Diffusivity (MD) correlated significantly with the global infarct size (p < 0.008). When patients were categorised according to LVEF (reduced, mid-range and preserved), all cDTI parameters differed significantly between the three groups.

CONCLUSION

Change in sheetlet orientation assessed using E2A from cDTI correlates with impaired radial strain. Segments with fewer subendocardial cardiomyocytes, evidenced by a lower proportion of myocytes with right-handed orientation on HA maps, show impaired longitudinal strain. Infarct segment enhancement correlates significantly with E2A and RHM. Our data has demonstrated a link between myocardial microstructure and contractility following myocardial infarction, suggesting a potential role for CMR cDTI to clinically relevant functional impact.