Visual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images

Xiaojie Duan,Qi Wang,Meichen Shi,Jianming Wang,Xiuyan Li,Ruixue Zuo,Dandan Chen,Qingliang Chen,He Zhao

doi:10.1155/2017/3163759

Abstract

With the rapid development of CT technology, especially the higher resolution of CT machine and a sharp increase in the amount of slices, to extract and three-dimensionally display aortic dissection from the huge medical image data became a challenging task. In this paper, active shape model combined with spatial continuity was adopted to realize automatic reconstruction of aortic dissection. First, we marked aortic feature points from big data sample library and registered training samples to build a statistical model. Meanwhile, gray vectors were sampled by utilizing square matrix, which set the landmarks as the center. Posture parameters of the initial shape were automatically adjusted by the method of spatial continuity between CT sequences. The contrast experiment proved that the proposed algorithm could realize accurate aorta segmentation without selecting the interested region, and it had higher accuracy than GVF snake algorithm (93.29% versus 87.54% on aortic arch, 94.30% versus 89.25% on descending aorta). Aortic dissection membrane was extracted via Hessian matrix and Bayesian theory. Finally, the three-dimensional visualization of the aortic dissection was completed by volume rendering based on the ray casting method to assist the doctors in clinical diagnosis, which contributed to improving the success rate of the operations.

Highlights

Aortic dissection (AD) is a cardiovascular disease that is a dangerous threat to human health, which can quickly lead to death [1]
In order to verify the algorithm for segmentation of CT image sequence aortic extraction effect, compare the aortic arch and descending aorta extraction results on this proposed method with those based on GVF snake algorithm
This paper introduces a kind of medical CT image processing method to rapidly and accurately obtain aortic dissection characteristic from huge CT image data and to threedimensionally reconstruct the structure for doctors in clinical diagnosis

Summary

Introduction

Aortic dissection (AD) is a cardiovascular disease that is a dangerous threat to human health, which can quickly lead to death [1]. The main separation therapy of aortic dissection is the lumen isolation technique requiring that the clinicians can clearly know the crevasse position, range, quantity, severity, and so on before surgery. In order to improve positive rate of aortic dissection, realize automation guidance to interventional treatment, and achieve precise surgery or postoperative evaluation, the aortic dissection 3D reconstruction system is indispensable. Threshold-based methods of image segmentation are challenged by intensity gradients within the image volume [3]

Methods

Results

Conclusion