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Preface

机译:前言

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摘要

Recent novel methods and approaches in sparsity-based com-pressive sensing and sparse learning have shown promising results and are currently being investigated and applied in machine learning, computer vision, computer graphics and medical imaging. In the medical community, these methods have been used successfully to speed up and improve applications such as magnetic resonance (MR) acquisition time, MR image reconstruction, organ segmentation and disease classification methods. The goal of this special issue is to publish novel theory, algorithms and applications on sparse methods for medical image reconstruction and analysis. It will foster dialog and debate in this relatively new field, which includes Compressive Sensing, Sparse Learning and their applications to medical imaging.
机译:基于稀疏的压缩感测和稀疏学习的最新新颖方法和方法已显示出令人鼓舞的结果,目前正在研究并将其应用于机器学习,计算机视觉,计算机图形学和医学成像。在医学界,这些方法已成功地用于加速和改善诸如磁共振(MR)采集时间,MR图像重建,器官分割和疾病分类方法之类的应用。本期特刊的目的是发布有关稀疏方法的新颖理论,算法和应用,以进行医学图像重建和分析。它将在这个相对较新的领域(包括压缩感知,稀疏学习及其在医学成像中的应用)促进对话和辩论。

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