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.
翻译:磁共振成像(MRI)是一种非侵入性成像技术,可以详细地成像解剖特征。它可以协助对样本器官进行功能分析,但成本很高。本文提出了从单一轴向人类脊柱和大脑的二维(2D)MRI图像序列进行虚拟三维(3D)重建的方法,以及生成丢失的切片数据的方法。我们的方法有助于保护被捕获对象的边缘、形状、大小以及内部组织结构。原始沿单一轴向的2D图像序列被分成较小的等分,在边缘保留克里格插值的帮助下,对其进行重建以预测缺失的切片信息。为了加快插值过程,我们使用多进程在并行核心上进行初始插值。从这样形成的3D矩阵中,应用Shearlet变换来估算沿Z轴的2D块,并使用提出的平均值中位数逻辑来最小化模糊效果。最后,为了可视化,子矩阵合并成最终的3D矩阵。接下来,新形成的3D矩阵被分成体素,并应用Marching Cubes方法来获得近似的3D图像以供查看。就我们所知,这是第一种基于克里格插值和多进程的3D重建方法,与图像比较的相似性指标相比达到了约98.89%的准确率。即使对于大型输入数据集,相较于单核处理,使用多核处理减少了重构所需的时间约70%。