Bogensperger Lea, Kobler Erich, Pernitsch Dominique, Kotzbeck Petra, Pieber Thomas R, Pock Thomas, Kolb Dagmar
Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
Institute of Computer Graphics, University of Linz, Linz, Austria.
Histochem Cell Biol. 2022 Jun;157(6):685-696. doi: 10.1007/s00418-022-02095-z. Epub 2022 Mar 23.
Electron tomography allows one to obtain 3D reconstructions visualizing a tissue's ultrastructure from a series of 2D projection images. An inherent problem with this imaging technique is that its projection images contain unwanted shifts, which must be corrected for to achieve reliable reconstructions. Commonly, the projection images are aligned with each other by means of fiducial markers prior to the reconstruction procedure. In this work, we propose a joint alignment and reconstruction algorithm that iteratively solves for both the unknown reconstruction and the unintentional shift and does not require any fiducial markers. We evaluate the approach first on synthetic phantom data where the focus is not only on the reconstruction quality but more importantly on the shift correction. Subsequently, we apply the algorithm to healthy C57BL/6J mice and then compare it with non-obese diabetic (NOD) mice, with the aim of visualizing the attack of immune cells on pancreatic beta cells within type 1 diabetic mice at a more profound level through 3D analysis. We empirically demonstrate that the proposed algorithm is able to compute the shift with a remaining error at only the sub-pixel level and yields high-quality reconstructions for the limited-angle inverse problem. By decreasing labour and material costs, the algorithm facilitates further research directed towards investigating the immune system's attacks in pancreata of NOD mice for numerous samples at different stages of type 1 diabetes.
电子断层扫描技术可从一系列二维投影图像中获取三维重建结果，从而呈现组织的超微结构。这种成像技术存在一个固有问题，即其投影图像包含不必要的偏移，为了获得可靠的重建结果，必须对这些偏移进行校正。通常，在重建过程之前，投影图像会通过基准标记相互对齐。在这项工作中，我们提出了一种联合对齐和重建算法，该算法迭代求解未知的重建结果和意外偏移，并且不需要任何基准标记。我们首先在合成体模数据上评估该方法，在此不仅关注重建质量，更重要的是关注偏移校正。随后，我们将该算法应用于健康的C57BL/6J小鼠，然后与非肥胖糖尿病（NOD）小鼠进行比较，目的是通过三维分析更深入地观察1型糖尿病小鼠体内免疫细胞对胰腺β细胞的攻击。我们通过实验证明，所提出的算法能够在仅亚像素水平上计算偏移并产生剩余误差，并且对于有限角度逆问题能够产生高质量的重建结果。通过降低劳动力和材料成本，该算法有助于针对1型糖尿病不同阶段的大量样本研究NOD小鼠胰腺中免疫系统攻击的进一步研究。
