AI for automatic vertebral motion tracking of fluoroscopic images

几个世纪以来，医生一直被认为是由于受伤或过度使用而导致的脊柱疾病，因此被认为是由于失去了脊柱的稳定性。在过去，这一点无法得到证实，但使用一种名为定量荧光检查(QF)的技术，它依赖于在运动过程中捕获的低剂量运动X射线图像，现在它可以。QF被认为是脊柱运动测量的黄金标准，但由于缺乏常规使用的准备，科学家、研究人员和临床医生(主要是脊柱外科医生和理疗师)很少使用QF。在这个项目中，我们的目标是将人工智能(AI)应用于QF，使其能够更广泛地应用于研究和患者护理。QF没有得到广泛应用的原因是，它涉及到由计算机定位和跟踪脊椎运动图像，从而允许自动测量椎骨的运动。如果没有自动化，这个过程对于常规使用来说太耗时了。与此同时，如果图像不是非常清晰和对齐的，自动测量会带来多种挑战。图像失真(例如，由于脊柱侧弯)、退化(例如，肥胖者的脂肪)或褪色(例如，患有骨质疏松症的人)目前几乎不可避免地导致测量过程失败--剥夺了广泛使用QF来研究脊柱疾病的能力。我们将应用人工智能方法，这样计算机就可以接受训练，以令人难以置信的速度对脊椎图像进行注册和跟踪，即使使用质量较差的图像也是如此。这将允许非常快速地执行大量检查，从而产生比人工操作员更详细的检查和核实，并且速度要快得多。这将开启脊柱护理研究的新纪元。这个项目将利用已经收集了多年的先前研究的QF数据，参与者同意将他们的数据用于未来的研究。不会招募更多的参与者，因此这个项目不需要额外的X射线辐射风险。

For centuries, doctors have been presented with spinal disorders that followed injury or overuse and were therefore thought to be due to loss of spinal stability. In the past, this could not be confirmed, but using a technology called Quantitative Fluoroscopy (QF), which relies on low-dose motion X-ray images captured during motion, it now can. QF is regarded as the gold standard for spinal motion measurement, but due to lack of readiness for routine use, it is rarely available to scientists, researchers and clinicians (mainly spinal surgeons and physical therapists). In this project we aim to bring Artificial Intelligence (AI) to bear on QF so it can be used more widely in research and patient care. The reason QF is not in wide use is that it involves the locating and tracking of vertebral motion images by a computer, allowing movement of vertebrae to be measured automatically. Without automation, the process is too time consuming for routine use. At the same time, automatic measurement poses multiple challenges if the images are not very clear and well aligned. Distorted (e.g., due to scoliosis), degraded (e.g., by fat in obese people) or fading (e.g., people with osteoporosis) images currently lead to almost inevitable failure of measurement process - denying the ability to use QF widely for investigating spinal disorders. We will apply AI methods so that computers can be trained to do both the registration and the tracking of vertebral images at incredible speed, even using images of poor quality. This will allow a huge number of inspections to be performed very quickly, resulting in much more detailed scrutiny and verification and at a much higher speed than a human operator. This will initiate a new era in spine care research.This project will leverage the QF data which has already been collected over multiple years of prior research, where the participants consented for their data to be used for future research. No additional participants will be recruited, hence no additional X-ray radiation risk will be required for this project.