Department of Computer Science, University College London, London, UK.
Department of Mathematical Sciences, University of Bath, Bath, UK.
Philos Trans A Math Phys Eng Sci. 2021 Jun 28;379(2200):20200205. doi: 10.1098/rsta.2020.0205. Epub 2021 May 10.
Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
成像在现代社会中无处不在,成像设备基于各种各样的物理原理,在不同的波长、能量和时间下探测样本。近年来,成像领域发生了变化,越来越多的成像设备将以前分开使用的设备结合在一起。受这些硬件发展的推动,出现了越来越多的数学思想,涉及如何在图像重建过程中协同地组合来自不同成像模式或通道的数据,利用多个图像之间的结构和/或功能相关性。在这里,我们回顾这些发展,指出重要的挑战,并展望该领域在未来几年可能的发展方向。本文是特刊“协同层析图像重建:第 1 部分”的一部分。