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A comparison of demons image registration algorithms to monitor longitudinal changes in knee cartilage: Data from the osteoarthritis initiative.

机译:监控膝盖软骨纵向变化的恶魔图像配准算法的比较:骨关节炎倡议的数据。

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摘要

Osteoarthritis (OA) is a slowly progressing disease characterized clinically by pain, deformity, enlargement of the joints, and limitation of motion. OA causes, among other changes, loss in cartilage volume that increases as the disease progresses. OA is a complex disease and objective documentation of disease progression or response to treatment is challenging. Approximately 27 million adults age 25 and older have clinically diagnosed OA; however, cartilage loss with disease progression is small and localized to sub-regions of the cartilage. Detection of these changes is challenging and manual methods are tedious and error prone.;Magnetic resonance imaging (MRI) is a non-invasive modality that provides high-resolution, 3-dimensional images with high contrast between cartilage and the surrounding anatomy. Highly accurate measures of cartilage volume, and thickness (global and local) can be extracted from morphological MR images. The focus of this research is on development of accurate tools to quantify the small and localized changes in cartilage morphology to facilitate comparisons between patient cohorts with varying degrees of OA as well as to track longitudinal changes (normal progression and response to treatment). The application area is the femoral cartilage but the methodology can be readily extended to the patellar and tibial cartilage.;We explored a fast, readily implementable algorithm called the 'Demons Algorithm'. We implemented and compared the registration accuracy of four variants of the algorithm on cartilage image volumes. The registration algorithms were also evaluated for the accuracy of the average Jacobians. Evaluation was performed on 36 subjects using the baseline and later time point images acquired after 12 months. The symmetric evolved demons algorithm provided the best in registration accuracy evaluated using quantitative metrics of mean squared error and voxel overlap. The average Jacobian of the cartilage was compared to the ratio of volume change for validation. The symmetric simple demons and symmetric evolved demons performed equally well in terms of the Jacobians. The techniques developed here will be used, in future studies, to explore differences in cohorts segregated by disease severity and correlation of local changes to clinical variables.
机译:骨关节炎(OA)是一种缓慢发展的疾病,临床特征是疼痛,畸形,关节肿大和运动受限。 OA会导致软骨体积损失,而这种损失会随着疾病的进展而增加。 OA是一种复杂的疾病,因此疾病进展或对治疗的反应的客观记录颇具挑战性。大约有2700万25岁及以上的成年人已经临床诊断为OA;然而,随着疾病进展的软骨损失很小,并且局限于软骨的子区域。检测这些变化具有挑战性,并且手动方法繁琐且容易出错。磁共振成像(MRI)是一种非侵入性方法,可提供高分辨率的3维图像,并在软骨与周围解剖结构之间具有高对比度。可以从形态学MR图像中提取高度准确的软骨量和厚度(整体和局部)度量。这项研究的重点是开发精确的工具,以量化软骨形态的小而局部的变化,以促进不同OA程度的患者队列之间的比较以及跟踪纵向变化(正常进展和对治疗的反应)。应用领域是股骨软骨,但该方法可以很容易地扩展到pa骨和胫骨软骨。我们探索了一种快速,易于实现的算法,称为“恶魔算法”。我们实现并比较了四个算法变体在软骨图像体积上的配准精度。还评估了注册算法的平均Jacobian精度。使用基线和12个月后获取的稍后时间点图像对36位受试者进行了评估。使用均方误差和体素重叠的定量度量,对称进化的恶魔算法提供了最佳的套准精度。将软骨的平均雅可比行列式与体积变化率进行比较以进行验证。就雅各布主义者而言,对称的简单恶魔和对称的进化恶魔的表现同样出色。在将来的研究中,将使用此处开发的技术来探索队列的差异,这些差异是由疾病的严重程度以及局部变化与临床变量的相关性分隔的。

著录项

  • 作者

    Hoang, Uyen N.;

  • 作者单位

    San Diego State University.;

  • 授予单位 San Diego State University.;
  • 学科 Information Science.;Health Sciences Medicine and Surgery.;Biology Bioinformatics.
  • 学位 M.S.
  • 年度 2015
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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