Proof of Principle: Inferring three dimensional kinematics of carpal bones in the wrist from fluoroscopic images

原理证明：从荧光图像推断手腕腕骨的三维运动学

• 批准号: G0801282/1
• 负责人: James Graham
• 金额: $ 27.42万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0801282%2F1
• 关键词: Proof; Principle; Inferring; three; dimensional

The carpal bones ? eight bones that form the wrist ? move in a complex 3D pattern as the hand takes up different positions. Chronic wrist pain arises from several conditions, such as instability patterns, non union or malunion of fractures, osteoarthritis etc. The result for patients is a severe reduction in quality of life due to impairment of everyday functions, lost work time and loss of the capacity to live independently. The current method of distinguishing between these conditions uses fluoroscopy ? a low dose X-ray technique. From the movement of the carpal bones visible on 2D video fluoroscopy sequences taken as the hand moves, experienced clinicians can infer the 3D movements of the carpal bones. Different patterns of movement characterise different conditions. The interpretation is difficult and the diagnostic accuracy depends wholly on the practitioner?s experience. Accurate diagnosis requires referral to a specialist hand consultant, and treatment is often delayed to the detriment of the patient. For research purposes, it is possible to visualise the 3D movement of the carpal bones directly using imaging techniques such as computed tomography or magnetic resonance imaging, but this kind of imaging is not appropriate for clinical investigations.In this project we will develop computer image analysis programmes that can infer the 3D movements in an individual wrist from the 2D fluoroscopy sequences, with the aim of producing a tool that will allow accurate diagnosis to be performed in the hand clinic without referral for specialist examination. This will improve patient care by achieving earlier diagnosis and treatment and reduce health-care costs. We will use image analysis methods that can ?learn? the range of movements of the carpal bones using CT images collected from normal volunteers, and also learn to identify these movements from corresponding 2D fluoroscopy sequences. Inferring 3D movement from 2D sequences is complicated because, as well as moving in the 2D images, the projected 2D shapes of the bones change as they rotate and the images of the individual bones overlap. Some of the carpal bones have complex shapes, which vary among individuals. This intrinsic shape variation must be taken into account in inferring the movement. We intend to address this problem by using ?statistical shape models? of the carpal bones. These are mathematical descriptions that are also ?learned? from the CT images and which represent the average shape of each bone together with its normal range of variability.

腕骨？八只骨头形成手腕？随着手占据不同的位置，以复杂的3D模式移动。慢性腕部疼痛源于多种疾病，例如不稳定性模式，骨折的非联合或骨关节炎。骨关节炎等。患者的结果是由于日常功能的损害，工作时间损失以及独立生活的能力而严重降低了生活质量。当前区分这些条件的方法使用荧光镜检查？低剂量X射线技术。从在手动移动的2D视频荧光镜序列上可见的腕骨运动中，经验丰富的临床医生可以推断腕骨的3D运动。运动的不同模式表征了不同的条件。解释很困难，诊断精度完全取决于从业者的经验。准确的诊断需要转介给专家手顾问，并且治疗通常会延迟以损害患者。出于研究目的，可以使用诸如计算机断层扫描或磁共振成像等成像技术直接直接可视化腕骨的3D运动，但是这种成像不适合用于临床研究。在此项目中，我们将开发计算机图像分析程序，这些程序可以从2D荧光范围内进行临床的临床序列，以允许在2D液体序列中推断出3D运动的手术，从而准确地诊断，该手术是临床的，这些手术均可以精确地诊断，该手术是临床的，该手术均可诊断临床的临床序列。进行专业考试。这将通过实现早期诊断和治疗并降低医疗保健成本来改善患者护理。我们将使用可以学习的图像分析方法？使用从正常志愿者那里收集的CT图像的腕骨运动范围，还学会从相应的2D荧光镜序列中识别这些运动。从2D序列推断出3D运动是复杂的，因为以及在2D图像中移动，骨骼旋转时的预计2D形状会发生变化，并且单个骨骼的图像重叠。一些腕骨具有复杂的形状，在个体之间有所不同。在推断运动时，必须考虑这种内在形状的变化。我们打算通过使用“统计形状模型”来解决这个问题？腕骨。这些是学习的数学描述吗？从CT图像，代表每个骨骼的平均形状以及其正常的可变性范围。