一种基于站立x线全景重建的无标记配准方法用于髋关节-膝关节-踝关节轴畸形评估。

IF 1.3 Q4 ENGINEERING, BIOMEDICAL Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization Pub Date : 2019-01-01 Epub Date: 2018-12-19 DOI:10.1080/21681163.2018.1537859
Yehuda K Ben-Zikri, Ziv R Yaniv, Karl Baum, Cristian A Linte
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引用次数: 2

摘要

通过髋-膝关节-踝关节(HKA)角度(内翻-外翻)来量化膝关节对齐的准确测量,是诊断各种骨科疾病和选择适当治疗方法的重要生物标志物。这种角度上的畸形是通过站立x射线全景来评估的。然而,传统x射线成像系统的视野有限,需要采集多个扇形图像来捕捉个体的站立姿势,并随后进行“拼接”以重建全景图像。这种全景图通常由x射线成像技术人员手动构建,通常使用附着在个人衣服上的各种外部标记,并在相邻的两个扇形图像中可见。为了消除人为错误,用户引起的变化,提高一致性和可重复性,并减少与传统手工“拼接”协议相关的时间,我们提出了一种自动全景构建方法,该方法仅依赖于图像中可靠检测到的解剖特征,无需任何外部标记或技术人员的手动输入。该方法首先对股骨和胫骨进行粗略分割,然后通过评估相应骨骼之间沿其内侧边缘的距离度量来注册扇形图像。然后使用识别的翻译生成站立全景图像。作为多地点临床试验筛选过程的一部分,该方法在来自10个临床地点的x射线图像数据库的95个患者图像数据集上进行了评估。将该方法得到的全景重建参数与人工全景重建参数进行了比较,并作为金标准。股骨和胫骨的水平平移差异分别为(0:43±1:95)mm(0:26±1:43)mm,垂直平移差异分别为(3:76±22:35)mm和(1:85±6:79)mm。我们的研究结果显示,使用自动和手动生成全景图测量的HKA角度之间没有统计学上的显著差异,并且在临床试验中患者的纳入/排除方面也导致了类似的决定。结果表明,该方法具有与手动全景构建相当的性能,并且具有更高的效率、一致性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A marker-free registration method for standing X-ray panorama reconstruction for hip-knee-ankle axis deformity assessment.

Accurate measurement of knee alignment, quantified by the hip-knee-ankle (HKA) angle (varus-valgus), serves as an essential biomarker in the diagnosis of various orthopaedic conditions and selection of appropriate therapies. Such angular deformities are assessed from standing X-ray panoramas. However, the limited field-of-view of traditional X-ray imaging systems necessitates the acquisition of several sector images to capture an individual's standing posture, and their subsequent 'stitching' to reconstruct a panoramic image. Such panoramas are typically constructed manually by an X-ray imaging technician, often using various external markers attached to the individual's clothing and visible in two adjacent sector images. To eliminate human error, user-induced variability, improve consistency and reproducibility, and reduce the time associated with the traditional manual 'stitching' protocol, here we propose an automatic panorama construction method that only relies on anatomical features reliably detected in the images, eliminating the need for any external markers or manual input from the technician. The method first performs a rough segmentation of the femur and the tibia, then the sector images are registered by evaluating a distance metric between the corresponding bones along their medial edge. The identified translations are then used to generate the standing panorama image. The method was evaluated on 95 patient image datasets from a database of X-ray images acquired across 10 clinical sites as part of the screening process for a multi-site clinical trial. The panorama reconstruction parameters yielded by the proposed method were compared to those used for the manual panorama construction, which served as gold-standard. The horizontal translation differences were 0:43 ± 1:95 mm 0:26 ± 1:43 mm for the femur and tibia respectively, while the vertical translation differences were 3:76 ± 22:35 mm and 1:85 ± 6:79 mm for the femur and tibia, respectively. Our results showed no statistically significant differences between the HKA angles measured using the automated vs. the manually generated panoramas, and also led to similar decisions with regards to the patient inclusion/exclusion in the clinical trial. Thus, the proposed method was shown to provide comparable performance to manual panorama construction, with increased efficiency, consistency and robustness.

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来源期刊
CiteScore
2.80
自引率
6.20%
发文量
102
期刊介绍: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.
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