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. 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.
翻译:多模式(或多通道)成像越来越重要,而且越来越普及,例如遥感中的超光谱成像、材料科学中的光谱CT以及医学中的多调MRI和PET-MR等,过去几十年的研究产生了大量数学方法,将多种模式的数据结合起来。通常作为变异性正规化的先进方法表明,在数量和质量上都大大改进了图像重建。几乎所有这些模型都基于这样一种假设,即模式已经完全登记,而在大多数现实世界应用中情况并非如此。我们提议了一个变通框架,共同进行重建和登记,从而克服这一障碍。模拟和实际数据的数字结果显示,拟议的多调MRI、PET-MR和超光谱成像的各种应用战略具有潜力:在重建过程中可以有效地纠正诸如轮换、翻译、缩影等模式之间的典型的不匹配。因此,拟议的框架允许在现实条件下,在多种模式中大力利用共享的信息。