Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain along a single axis, and (ii) generation of missing inter-slice data are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. The sequence of original 2D slices along a single axis is divided into smaller equal sub-parts which are then reconstructed using edge preserved kriging interpolation to predict the missing slice information. In order to speed up the process of interpolation, we have used multiprocessing by carrying out the initial interpolation on parallel cores. From the 3D matrix thus formed, shearlet transform is applied to estimate the edges considering the 2D blocks along the $Z$ axis, and to minimize the blurring effect using a proposed mean-median logic. Finally, for visualization, the sub-matrices are merged into a final 3D matrix. Next, the newly formed 3D matrix is split up into voxels and marching cubes method is applied to get the approximate 3D image for viewing. To the best of our knowledge it is a first of its kind approach based on kriging interpolation and multiprocessing for 3D reconstruction from 2D slices, and approximately 98.89\% accuracy is achieved with respect to similarity metrics for image comparison. The time required for reconstruction has also been reduced by approximately 70\% with multiprocessing even for a large input data set compared to that with single core processing.
磁共振成像(MRI)是一种非侵入性详细解剖学成像的技术。它可以用于对样本器官的功能性分析,但成本很高。在这项工作中,提出了方法(i)从一条轴上的单一序列2D切片的MR图像上的人类脊柱和大脑的横截面上虚拟三维重构,以及(ii)生成缺失slice数据的方法。我们的方法有助于保持物体的边缘、形状、大小以及内部组织结构。原2D切片沿着一条轴的序列被分解成较小的等分部分,然后使用边缘保留的Kriging插值预测缺失slice信息。为了加速插值过程,我们使用了并行核心的 multiprocessing。从形成3D矩阵的过程中, shearlet变换应用以估计边缘,考虑Z轴上的2D块,并使用 proposed的均值逻辑最小化模糊效应。最后,为了可视化,子矩阵被合并到最终3D矩阵中。接下来,新形成的3D矩阵被分解成 voxels 和步进立方体方法,以获取近似的3D图像以供观察。据我们所知,这是一种基于Kriging插值和 multiprocessing的从2D切片的三维重构的方法,与图像比较相似度 metrics的精度约为98.89%。即使对于一个巨大的输入数据集,使用 multiprocessing 相比 single-core processing,重建时间也减少了约70%。