Suppr超能文献

基于混合生物力学强度的肺部4DCT可变形图像配准

A hybrid biomechanical intensity based deformable image registration of lung 4DCT.

作者信息

Samavati Navid, Velec Michael, Brock Kristy

机构信息

Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada.

出版信息

Phys Med Biol. 2015 Apr 21;60(8):3359-73. doi: 10.1088/0031-9155/60/8/3359. Epub 2015 Apr 1.

Abstract

Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. A hybrid DIR algorithm is proposed based on, a biomechanical model-based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone. The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm.

摘要

在过去二十年中,由于可变形图像配准(DIR)在许多图像引导介入(IGI)中起着至关重要的作用,因此得到了广泛研究。IGI需要高度精确的配准,并在整个感兴趣区域保持其准确性。这项工作通过改进基于生物力学模型的DIR算法Morfeus的结果,评估了准确性和一致性的提高。提出了一种混合DIR算法,该算法基于基于生物力学模型的DIR算法和基于B样条强度的算法的细化步骤。通过对肺部与胸腔之间的接触表面进行建模,最初使用生物力学DIR对31例患者的四维计算机断层扫描(4DCT)肺部图像的吸气和呼气重建进行配准。然后使用基于强度的算法对所得变形进行细化,以减少任何残留的不确定性。基于强度的算法中的重要参数,包括网格间距、金字塔层数和正则化系数,在10名随机选择的患者(共31名)上进行了优化。通过测量配准后两幅图像上共同解剖点的欧几里得距离来计算目标配准误差(TRE)。对于每位患者,每个肺至少使用30个点。发现在10名随机选择的患者上,8mm的网格间距、5级网格金字塔和3.0的正则化系数可提供最佳结果。总体而言,在整个患者群体(n = 31)中,混合方法的平均±标准差(第90百分位数)TRE为1.5±1.4(2.9)mm,而单独使用生物力学DIR为3.1±1.9(5.6)mm,单独使用基于强度的DIR为2.6±2.5(6.1)mm。所提出的基于混合生物力学建模强度的算法是一种很有前途的DIR技术,可用于各种IGI程序。当前研究表明该方法对肺部4DCT图像配准的有效性,平均精度为1.5mm。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7a3/4418808/6e58e8e9b8db/nihms683164f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验