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重复性头部姿势的再定位方法在连续锥形束 CT 图像中。

Reorientation methodology for reproducible head posture in serial cone beam computed tomography images.

机构信息

Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, 03722, Korea.

HDXWILL, Seoul, 03162, Korea.

出版信息

Sci Rep. 2023 Feb 24;13(1):3220. doi: 10.1038/s41598-023-30430-4.

Abstract

Low dose and accessibility have increased the application of cone beam computed tomography (CBCT). Often serial images are captured for patients to diagnose and plan treatment in the craniofacial region. However, CBCT images are highly variable and lack harmonious reproduction, especially in the head's orientation. Though user-defined orientation methods have been suggested, the reproducibility remains controversial. Here, we propose a landmark-free reorientation methodology based on principal component analysis (PCA) for harmonious orientation of serially captured CBCTs. We analyzed three serial CBCT scans collected for 29 individuals who underwent orthognathic surgery. We first defined a region of interest with the proposed protocol by combining 2D rendering and 3D convex hull method, and identified an intermediary arrangement point. PCA identified the y-axis (anterioposterior) followed by the secondary x-axis (transverse). Finally, by defining the perpendicular z-axis, a new global orientation was assigned. The goodness of alignment (Hausdorff distance) showed a marked improvement (> 50%). Furthermore, we clustered cases based on clinical asymmetry and validated that the protocol was unaffected by the severity of the skeletal deformity. Therefore, it could be suggested that integrating the proposed algorithm as the preliminary step in CBCT evaluation will address a fundamental step towards harmonizing the craniofacial imaging records.

摘要

低剂量和易获取性增加了锥形束计算机断层扫描(CBCT)的应用。通常会为患者拍摄一系列图像,以便在颅面区域进行诊断和治疗计划。然而,CBCT 图像变化很大,缺乏协调的再现,尤其是在头部的方向上。尽管已经提出了用户定义的定向方法,但重复性仍存在争议。在这里,我们提出了一种基于主成分分析(PCA)的无地标重新定向方法,用于协调连续捕获的 CBCT 的定向。我们分析了 29 名接受正颌手术的个体收集的三个连续 CBCT 扫描。我们首先通过结合 2D 渲染和 3D 凸壳方法,按照提出的方案定义感兴趣区域,并确定中间排列点。PCA 确定了 y 轴(前后),然后是次要的 x 轴(横向)。最后,通过定义垂直 z 轴,赋予新的全局方向。配准的良好性(Hausdorff 距离)显示出明显的改善(>50%)。此外,我们根据临床不对称性对病例进行聚类,并验证该方案不受骨骼畸形严重程度的影响。因此,可以建议将所提出的算法集成到 CBCT 评估的初步步骤中,将解决协调颅面成像记录的基本步骤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/073e/9958024/7481a69c10b8/41598_2023_30430_Fig1_HTML.jpg

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