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？八根骨头组成了手腕？当手占据不同的位置时，会以复杂的3D模式移动。慢性手腕疼痛是由几种情况引起的，如不稳定模式、骨折不愈合或不愈合、骨关节炎等。对患者来说，其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，生活质量严重下降。目前区分这些疾病的方法是透视检查。低剂量x射线技术从手部运动时拍摄的2D视频透视序列中可见的腕骨运动，经验丰富的临床医生可以推断出腕骨的3D运动。不同的运动模式代表不同的情况。解释是困难的，诊断的准确性完全取决于从业者。年代的经验。准确的诊断需要转诊到专业的手部咨询师，而治疗往往被延误，对患者造成损害。出于研究目的，可以使用计算机断层扫描或磁共振成像等成像技术直接可视化腕骨的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透视序列中推断个人手腕的3D运动，目的是生产一种工具，可以在手部诊所进行准确诊断，而无需转诊专家检查。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。我们将使用可以学习的图像分析方法。使用从正常志愿者收集的CT图像来识别腕骨的运动范围，并学习从相应的二维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图像中移动外，骨骼的投影2D形状随着旋转而变化，并且单个骨骼的图像重叠。有些腕骨形状复杂，因人而异。在推断运动时，必须考虑到这种内在的形状变化。我们打算用？来解决这个问题。统计形状模型？腕骨。这些数学描述也是可以习得的。这些图像代表了每块骨头的平均形状及其正常的变化范围。