TF-Unet:An automatic cardiac MRI image segmentation method.

Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different types of arrhythmias in recent years. MRI images are now mostly used for model building. In cardiac modeling studies, the degree of segmentation of the heart image determines the success of subsequent 3D reconstructions. Therefore, a fully automated segmentation is needed. In this paper, we combine U-Net and Transformer as an alternative approach to perform powerful and fully automated segmentation of medical images. On the one hand, we use convolutional neural networks for feature extraction and spatial encoding of inputs to fully exploit the advantages of convolution in detail grasping; on the other hand, we use Transformer to add remote dependencies to high-level features and model features at different scales to fully exploit the advantages of Transformer. The results show that, the average dice coefficients for ACDC and Synapse datasets are 91.72 and 85.46%, respectively, and compared with Swin-Unet, the segmentation accuracy are improved by 1.72% for ACDC dataset and 6.33% for Synapse dataset.

Investigating two kinds of cellular alternans and corresponding TWA induced by impaired calcium cycling in myocardial ischemia.

BACKGROUND: The utility of T wave alternans (TWA) in identifying arrhythmia risk has been demonstrated. During myocardial ischemia (MI), TWA could be induced by cellular alternans. However, the relationship between cellular alternans patterns and TWA patterns in MI has not been investigated thoroughly. METHODS: We set MI conditions to simulate alternans. Either prolonging Ca2+ release or increasing spark-induced sparks (secondary sparks) can give rise to different patterns of APD alternans and TWA. In addition, different ischemic zones and reduced conduction velocity are also considered in one dimensional simulation. RESULTS: Delay of Ca2+ release can produce discordant Ca2+-driven alternans in single cell simulation. Increasing secondary sparks leads to concordant alternans. Correspondingly, morphology and magnitude of TWA vary in two different cellular alternans. Epi ischemia results in alternans concentrating in the first half of T wave. Endo and transmural ischemia lead to fluctuations in the second half of T wave. In addition, slowing conduction velocity has no effect on TWA magnitude. CONCLUSION: Specific ionic channel dysfunction and ischemic zones affect TWA patterns.

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling.

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.