SuperResolution reconstruction of Bone MicroStructure MicroCT Image based on AutoEncoder Structure * Computed Tomography (CT) is widely used for screening, diagnostic and imageguided therapy for clinical and research purposes. The current Finite Element Analysis for Research Microstructure based on MicroCT scan data has been considered the gold standard for noninvasive studies of bone microstructure. In medical diagnosis and treatment, the resolution of clinical medical images is low, and it is impossible to directly observe the fine structure of bone through MicroCT. However, there are many problems in high resolution image, such as long scan time, high radiation dose and complicated processing steps. Therefore, the research goal of this paper is to propose a new network structure which can reconstruct highresolution bone microstructure images with fine structure while reducing Xray radiation. The network model proposed in this paper combines the autoencoder structure and adopts a staged upsampling strategy to accurately reconstruct highresolution images from lowresolution images.Moreover, in order to be more realistic, this paper constructs a real training data set according to strict operation. In the data set of this paper, a variety of existing superresolution reconstruction methods are used for comparison test. The results show that the network model proposed in this paper achieves better reconstruction results. We found in the experiment that the noise of the sample itself has a certain influence on the evaluation of the reconstruction effect and the impact of noise reduction is also discussed on superresolution reconstruction.

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.