Superresolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks Abstract   Background and objective  Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, highresolution MRI scans is time consuming and peak signaltonoise ratio (PSNR) is low. The existing superresolution methods attempt to resolve this issue, but there are still shortcomings, such as hallucinate details after superresolution, low precision after reconstruction, etc. To dispose these problems, we propose the Laplacian Pyramid Generation Adversarial Network (LSRGAN) in order to generate visually better cardiovascular ultrasound images so as to aid physician diagnosis and treatment.    Methods and results  In order to address the problem of low image resolution, we used the Laplacian Pyramid to analyze the highfrequency detail features of superresolution (SR) reconstruction of images with different pixel sizes. To eliminate gradient disappearance, we implemented the least squares loss function as the discriminator, we introduce the residualdense block (RDB) as the basic network building unit is used to generate higher quality images. The experimental results show that the LSRGAN can effectively avoid the illusion details after superresolution and has the best reconstruction quality. Compared with the stateoftheart methods, our proposed algorithm generates higher quality superresolution images that comes with higher peak signaltonoise ratio and structural similarity (SSIM) scores.    Conclusion  We implemented a novel LSRGAN network model, which solves reduces insufficient resolution and hallucinate details of MRI after superresolution. Our research presents a superior superresolution method for medical experts to diagnose and treat myocardial ischemia and myocardial infarction.

Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames Cardiac anatomy and function are interrelated in many ways, and these relations can be affected by multiple pathologies. In particular, this applies to ventricular shape and mechanical deformation. We propose a machine learning approach to capture these interactions by using a conditional Generative Adversarial Network (cGAN) to predict cardiac deformation from individual Cardiac Magnetic Resonance (CMR) frames, learning a deterministic mapping between enddiastolic (ED) to endsystolic (ES) CMR shortaxis frames. We validate the predicted images by quantifying the difference with real images using mean squared error (MSE) and structural similarity index (SSIM), as well as the Dice coefficient between their respective endo and epicardial segmentations, obtained with an additional UNet. We evaluate the ability of the network to learn xe2x80x9chealthyxe2x80x9d deformations by training it on (sim )33,500 image pairs from (sim )12,000 subjects, and testing on a separate test set of (sim )4,500 image pairs from the UK Biobank study. Mean MSE, SSIM and Dice scores were 0.0026 xc2xb1 0.0013, 0.89 xc2xb1 0.032 and 0.89 xc2xb1 0.059 respectively. We subsequently retrained the network on specific patient group data, showing that the network is capable of extracting physiologically meaningful differences between patient populations suggesting promising applications on pathological data.