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？构成手腕的八块骨头？当手占据不同的位置时，以复杂的3D模式移动。慢性腕关节疼痛由多种情况引起，如不稳定模式、骨折不愈合或畸形愈合、骨关节炎等。由于日常功能受损、工作时间损失和独立生活能力丧失，患者的生活质量严重降低。目前区分这些情况的方法是使用荧光透视？低剂量X射线技术根据手部移动时拍摄的2D视频荧光透视序列上可见的腕骨运动，经验丰富的临床医生可以推断腕骨的3D运动。不同的运动模式代表不同的条件。解释是困难的，诊断的准确性完全取决于医生？的经验。准确的诊断需要转介给专家手顾问，治疗往往被推迟，损害病人。为了研究的目的，可以使用成像技术，如计算机断层扫描或磁共振成像，直接可视化腕骨的三维运动，但这种成像不适合临床研究。在这个项目中，我们将开发计算机图像分析程序，可以从二维荧光透视序列推断出个体手腕的三维运动，其目的是生产一种工具，该工具将允许在手部诊所中进行准确的诊断而无需转诊进行专家检查。这将通过实现早期诊断和治疗改善病人护理，并降低保健费用。我们将使用图像分析方法，可以？学习？使用从正常志愿者收集的CT图像的腕骨的运动范围，并且还学习从相应的2D荧光透视序列识别这些运动。从2D序列推断3D移动是复杂的，因为除了在2D图像中移动之外，骨骼的投影2D形状随着它们旋转而改变，并且各个骨骼的图像重叠。一些腕骨具有复杂的形状，个体之间存在差异。在推断运动时，必须考虑到这种固有的形状变化。我们打算通过使用？统计形状模型腕骨的断裂这些数学描述也是？学会了？从CT图像，并表示每个骨的平均形状连同其变异性的正常范围。