Fast 3D Volumetric Image Reconstruction from 2D MRI Slices by Parallel Processing

• URL: http://arxiv.org/abs/2303.09523v1
• Date: Thu, 16 Mar 2023 17:39:11 GMT
• Title: Fast 3D Volumetric Image Reconstruction from 2D MRI Slices by Parallel Processing
• Authors: Somoballi Ghoshal, Shremoyee Goswami, Amlan Chakrabarti, Susmita Sur-Kolay
• Abstract summary: Methods for virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. To the best of our knowledge it is a first of its kind approach based on kriging and multiprocessing for 3D reconstruction from 2D slices.
• Score: 1.7778609937758323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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