Lechelek Loubna, Horna Sebastien, Zrour Rita, Naudin Mathieu, Guillevin Carole
XLIM Laboratory, Joint Research Unit, National Center for Scientific Research (UMR CNRS) 7252, University of Poitiers, CEDEX 9, 86073 Poitiers, France.
Common Laboratory Multi-Nuclear Multi-Organ Metabolic Imaging (I3M), CNRS-Siemens, University and Hospital of Poitiers, 86000 Poitiers, France.
J Imaging. 2022 Apr 7;8(4):103. doi: 10.3390/jimaging8040103.
Three-dimensional surface reconstruction is a well-known task in medical imaging. In procedures for intervention or radiation treatment planning, the generated models should be accurate and reflect the natural appearance. Traditional methods for this task, such as Marching Cubes, use smoothing post processing to reduce staircase artifacts from mesh generation and exhibit the natural look. However, smoothing algorithms often reduce the quality and degrade the accuracy. Other methods, such as MPU implicits, based on adaptive implicit functions, inherently produce smooth 3D models. However, the integration in the implicit functions of both smoothness and accuracy of the shape approximation may impact the precision of the reconstruction. Having these limitations in mind, we propose a hybrid method for 3D reconstruction of MR images. This method is based on a parallel Marching Cubes algorithm called Flying Edges (FE) and Multi-level Partition of Unity (MPU) implicits. We aim to combine the robustness of the Marching Cubes algorithm with the smooth implicit curve tracking enabled by the use of implicit models in order to provide higher geometry precision. Towards this end, the regions that closely fit to the segmentation data, and thus regions that are not impacted by reconstruction issues, are first extracted from both methods. These regions are then merged and used to reconstruct the final model. Experimental studies were performed on a number of MRI datasets, providing images and error statistics generated from our results. The results obtained show that our method reduces the geometric errors of the reconstructed surfaces when compared to the MPU and FE approaches, producing a more accurate 3D reconstruction.
三维表面重建是医学成像中一项广为人知的任务。在介入或放射治疗规划程序中，生成的模型应准确无误并反映自然外观。用于此任务的传统方法，如移动立方体法，会使用平滑后处理来减少网格生成过程中的阶梯状伪影，并呈现出自然的外观。然而，平滑算法往往会降低质量并降低准确性。其他方法，如基于自适应隐函数的MPU隐式法，本质上会生成平滑的三维模型。然而，在隐函数中整合形状近似的平滑度和准确性可能会影响重建的精度。考虑到这些局限性，我们提出了一种用于磁共振图像三维重建的混合方法。该方法基于一种名为飞边（FE）的并行移动立方体算法和多级单位分解（MPU）隐式法。我们旨在将移动立方体算法的稳健性与使用隐式模型实现的平滑隐式曲线跟踪相结合，以提供更高的几何精度。为此，首先从这两种方法中提取与分割数据紧密拟合的区域，即不受重建问题影响的区域。然后将这些区域合并并用于重建最终模型。我们对多个磁共振成像数据集进行了实验研究，给出了从我们的结果中生成的图像和误差统计数据。所得结果表明，与MPU和FE方法相比，我们的方法减少了重建表面的几何误差，实现了更精确的三维重建。
