Robust Image Reconstruction With Misaligned Structural Information
Robust Image Reconstruction With Misaligned Structural Information
复制标题
DOI：
10.1109/access.2020.3043638
复制
发表时间：
2020-04
期刊：
影响因子：
3.9
通讯作者：
Leon Bungert;Matthias Joachim Ehrhardt
中科院分区：
文献类型：
--
作者：
Leon Bungert;Matthias Joachim Ehrhardt
多模态（或多通道）成像正变得越来越重要和越来越广泛，例如遥感中的高光谱成像，材料科学中的光谱CT以及医学中的多重对比MRI和PET-MR。过去几十年的研究产生了大量的数学方法来结合来自几种模式的数据。最先进的方法，通常被表述为变分正则化，已经显示出在定量和定性上显著改善图像重建。几乎所有这些模型都依赖于模态完美注册的假设，而在大多数实际应用中并非如此。我们提出了一个共同进行重建和配准的变分框架，从而克服了这一障碍。我们的方法是第一个在不同的模式下实现这一目标的方法，并且在重建和注册的准确性方面优于现有的方法。模拟和真实数据的数值结果显示了该策略在多对比MRI， PET-MR和高光谱成像中的各种应用潜力：在重建过程中可以有效地纠正旋转，平移，缩放等模式之间的典型失调。因此，所提出的框架允许在真实条件下跨多种模式健壮地利用共享信息。
Multi-modality (or multi-channel) imaging is becoming increasingly important and more widely available, e.g. hyperspectral imaging in remote sensing, spectral CT in material sciences as well as multi-contrast MRI and PET-MR in medicine. Research in the last decades resulted in a plethora of mathematical methods to combine data from several modalities. State-of-the-art methods, often formulated as variational regularization, have shown to significantly improve image reconstruction both quantitatively and qualitatively. Almost all of these models rely on the assumption that the modalities are perfectly registered, which is not the case in most real world applications. We propose a variational framework which jointly performs reconstruction and registration, thereby overcoming this hurdle. Our approach is the first to achieve this for different modalities and outranks established approaches in terms of accuracy of both reconstruction and registration. Numerical results on simulated and real data show the potential of the proposed strategy for various applications in multi-contrast MRI, PET-MR, and hyperspectral imaging: typical misalignments between modalities such as rotations, translations, zooms can be effectively corrected during the reconstruction process. Therefore the proposed framework allows the robust exploitation of shared information across multiple modalities under real conditions.