【摘要】 脑组织病灶切片的三维重构对于了解神经胶质神经瘤状态具有重要意义，可用于鉴别诊断、手术模拟等。移动立方体(MC)算法是经典的多边形曲面重建算法，具有简单易实现的优点，但其存在低效且梯级现象明显的情况。针对该问题，本研究提出一种细化等值面拓扑的病灶切片空间堆叠重建方法(SSR-RI),旨在实现拓扑构形与运行效率的优化。SSR-RI通过构建空间坐标系对磁共振图像(MRI)切片邻近图进行处理，为了改善双线性插值法中图像分量易受损的问题，提出一种自适应空间插值法，根据灰度值变化自适应选择插值点，对周围进行扩充。在结合等法线顶点的基础上，设计了一种细化等值面提取方式的堆叠重建方法，以提高堆叠速度，并减少梯级问题。为了进一步优化SSR-RI的三维重建效果，提出一种改进的局部反射光照法(PR)以绘制三维病变，利用镜面颜色反射(SCR)与镜面指数(SE)对重建体进行渲染优化。使用公开脑肿瘤分割数据集BraTS的618例病例开展三维重建实验，以验证所提出方法性能。实验结果显示，所提算法的重建时间只需2.124 s, F-score值达到0.845,SSIM值达到0.81,相较于MC算法减少了38%的重建时间，F-score值和SSIM值分别提高了30.89%和38.4%。重构体序列间结构紧密，视觉效果更富立体感和纹理感，有效地提高了三维重建的绘制效率。更多还原

【Abstract】 Three-dimensional reconstruction of brain tissue lesion slices is of great significance for understanding status of glioblastoma, and can be used for various clinical applications including differential diagnosis and surgical simulation. The marching cubes(MC) algorithm is a classic polygonal surface reconstruction algorithm with the advantages of simplicity and ease of implementation, however, its efficiency is low with obvious cascade phenomena. To solve this problem, this study proposed a lesion slice spatial stacking reconstruction method(SSR-RI) refining the isosurface topology, aiming to achieve optimization of topology configuration and operational efficiency. SSR-RI was applied to process adjacent images of MRI slices by constructing a spatial coordinate system. In order to improve the vulnerability of image components in the bilinear interpolation method, an adaptive spatial interpolation method was proposed, which adaptively selected interpolation points according to the change of gray value to expand the surrounding. On the basis of combining isonormal vertices, a stacked reconstruction method for refining isosurface extraction was designed to improve stacking speed and effectively reduce cascade problems. In order to further optimize the iterative reconstruction effect of SSR-RI, an improved local reflection illumination(PR) was proposed to draw 3D lesions, and the reconstruction volume was optimized by using specular color reflection(SCR) and specular exponent(SE). There were 618 cases of brain tumor segmentation dataset BraTS used to carry out iterative reconstruction experiments to verify the performance of this research method. Experimental results showed that the reconstruction time of the proposed algorithm was only 2.124 seconds, with an F-score value of 0.845 and an SSIM value of 0.81. Compared to the MC algorithm, the reconstruction time was reduced by 38%, and the F-score value and SSIM value were increased by 30.89% and 38.4%, respectively. The structure of the reconstructed volume sequence was compact, and the visual effect was more stereoscopic and textured, which effectively improved the rendering efficiency of iterative reconstruction.更多还原