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协同层析图像重建:第 1 部分。

Synergistic tomographic image reconstruction: part 1.

机构信息

Biomedical Imaging Science Department, University of Leeds, Leeds, West Yorkshire, UK.

Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Philos Trans A Math Phys Eng Sci. 2021 Jun 28;379(2200):20200189. doi: 10.1098/rsta.2020.0189. Epub 2021 May 10.

Abstract

This special issue focuses on synergistic tomographic image reconstruction in a range of contributions in multiple disciplines and various application areas. The topic of image reconstruction covers substantial inverse problems (Mathematics) which are tackled with various methods including statistical approaches (e.g. Bayesian methods, Monte Carlo) and computational approaches (e.g. machine learning, computational modelling, simulations). The issue is separated in two volumes. This volume focuses mainly on algorithms and methods. Some of the articles will demonstrate their utility on real-world challenges, either medical applications (e.g. cardiovascular diseases, proton therapy planning) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issue is to bring together different scientific communities which do not usually interact as they do not share the same platforms (such as journals and conferences). This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

摘要

本期特刊重点关注多学科和多个应用领域的协同层析图像重建方面的研究成果。图像重建主题涵盖了大量的反问题(数学),这些问题采用了各种方法来解决,包括统计方法(例如贝叶斯方法、蒙特卡罗方法)和计算方法(例如机器学习、计算建模、模拟)。本期特刊分为两卷。本卷主要侧重于算法和方法。其中一些文章将展示它们在实际挑战中的实用性,包括医学应用(如心血管疾病、质子治疗计划)或材料科学应用(如材料分解和特性描述)。本期特刊的一个预期成果是汇集不同的科学社区,这些社区通常不会互动,因为它们不使用相同的平台(例如期刊和会议)。本文是“协同层析图像重建特刊:第 1 部分”的主题文章之一。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b96/8107648/3de51d9dbc66/rsta20200189f01.jpg

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