Automatic and accurate classification of Alzheimer’s disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer’s disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer’s disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods.

Compressed sensing magnetic resonance imaging (CS-MRI) is a time-efficient method to acquire MR images by taking advantage of the highly under-sampled k-space data to accelerate the time consuming acquisition process. In this paper, we proposed a de-aliasing fine-tuning Wasserstein generative adversarial network (DA-FWGAN) for imaging reconstruction of highly under-sampled k-space data in CS-MRI. In the architecture, we used the fine-tuning method for accurate training of the neural network parameters and the Wasserstein distance as the discrepancy measure between the real and reconstructed images. Furthermore, for better preservation of the fine structures in the reconstructed images, we incorporated perceptual loss, image and frequency loss into the loss function for training the network. With experimental results from 3 different sampling schemes and 3 levels of sampling rates, we compared the reconstruction performance of the DA-FWGAN method with other state-of-the-art deep learning methods for CS-MRI reconstruction, including ADMM-Net, Pixel-GAN, and DAGAN. The proposed DA-FWGAN method outperforms all other methods and can provide superior reconstruction with improved peak signal-to-noise ratio (PSNR) and structural similarity index measure.

One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE). First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter. Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE. The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE. This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018–314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.

Objective: In recent years, the early diagnosis and treatment of coronary microvascular dysfunction (CMD) have become crucial for preventing coronary heart disease. This paper aims to develop a computer-assisted autonomous diagnosis method for CMD by using ECG features and expert features. Approach: Clinical electrocardiogram (ECG), myocardial contrast echocardiography (MCE), and coronary angiography (CAG) are used in our method. Firstly, morphological features, temporal features, and T-wave features of ECG are extracted by multi-channel residual network with BiLSTM (MCResnet-BiLSTM) model and the multi-source T-wave features (MTF) extraction model, respectively. And these features are fused to form ECG features. In addition, the CFR $_\text{MCE}$ is calculated based on the parameters related to the MCE at rest and stress state, and the Angio-IMR is calculated based on CAG. The combination of CFR $_\text{MCE}$ and Angio-IMR is termed as expert features. Furthermore, the hybrid features, fused from the ECG features and the expert features, are input into the multilayer perceptron to implement the identification of CMD. And the weighted sum of the soft maximum loss and center loss is used as the total loss function for training the classification model, which optimizes the classification ability of the model. Result: The proposed method achieved 93.36% accuracy, 94.46% specificity, 92.10% sensitivity, 95.89% precision, and 93.95% F1 score on the clinical dataset of the Second Affiliated Hospital of Zhejiang University. Conclusion: The proposed method accurately extracts global ECG features, combines them with expert features to obtain hybrid features, and uses weighted loss to significantly improve diagnostic accuracy. It provides a novel and practical method for the clinical diagnosis of CMD.

Impaired sarcoplasmic reticulum (SR) calcium transport ATPase (SERCA) gives rise to Ca²⁺ alternans and changes of the Ca2+release amount. These changes in Ca²⁺ release amount can reveal the mechanism underlying how the interaction between Ca²⁺ release and Ca²⁺ uptake induces Ca²⁺ alternans. This study of alternans by calculating the values of Ca²⁺ release properties with impaired SERCA has not been explored before. Here, we induced Ca²⁺ alternans by using an impaired SERCA pump under ischemic conditions. The results showed that the recruitment and refractoriness of the Ca²⁺ release increased as Ca²⁺ alternans occurred. This indicates triggering Ca waves. As the propagation of Ca waves is linked to the occurrence of Ca²⁺ alternans, the “threshold” for Ca waves reflects the key factor in Ca²⁺ alternans development, and it is still controversial nowadays. We proposed the ratio between the diastolic network SR (NSR) Ca content (Ca_nsr) and the cytoplasmic Ca content (Ca_i) (Ca_nsr/Ca_i) as the “threshold” of Ca waves and Ca²⁺ alternans. Diastolic Ca_nsr, Ca_i, and their ratio were recorded at the onset of Ca²⁺ alternans. Compared with certain Ca_nsr and Ca_i, the “threshold” of the ratio can better explain the comprehensive effects of the Ca²⁺ release and the Ca²⁺ uptake on Ca²⁺ alternans onset. In addition, these ratios are related with the function of SERCA pumps, which vary with different ischemic conditions. Thus, values of these ratios could be used to differentiate Ca²⁺ alternans from different ischemic cases. This agrees with some experimental results. Therefore, the certain value of diastolic Ca_nsr/Ca_i can be the better “threshold” for Ca waves and Ca²⁺ alternans.