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

腕骨？手腕上有八块骨头？当手采取不同的位置时，在复杂的3D模式中移动。慢性手腕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。患者的结果是由于日常功能受损、失去工作时间和丧失独立生活能力而严重降低生活质量。目前区分这些疾病的方法是使用透视吗？一种低剂量的X射线技术。经验丰富的临床医生可以从手部移动时拍摄的2D视频透视序列上可见的腕骨运动来推断腕骨的3D运动。不同的运动模式代表着不同的条件。解释困难，诊断准确率完全依靠从业人员--S的经验。准确的诊断需要转诊到专业的手部顾问，治疗往往会延误，从而损害患者的健康。为了研究目的，可以使用成像技术，如计算机断层扫描或磁共振成像，直接显示腕骨的3D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2D透视序列推断个人手腕的3D运动，目的是制造一种工具，使手部诊所无需转介专家检查即可进行准确的诊断。这将通过实现更早的诊断和治疗来改善患者护理，并降低卫生保健成本。我们会用图像分析的方法来学习吗？使用从正常志愿者收集的CT图像来测量腕骨的运动范围，并学习从相应的2D透视序列中识别这些运动。从2D序列推断3D运动是复杂的，因为除了在2D图像中移动之外，骨骼的投影2D形状随着它们的旋转而改变，并且单个骨骼的图像重叠。一些腕骨具有复杂的形状，不同的个体有不同的形状。在推断运动时，必须考虑到这种内在的形状变化。我们打算通过使用统计形状模型来解决这个问题。在腕骨上。这些都是数学描述，也是可以学习的？它代表了每个骨骼的平均形状及其正常的变异性范围。

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.