AI for automatic vertebral motion tracking of fluoroscopic images

用于荧光透视图像自动椎体运动跟踪的人工智能

• 批准号: MR/X005372/1
• 负责人: Marcin Budka
• 金额: $ 8.29万
• 依托单位: Bournemouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX005372%2F1
• 关键词: AI; automatic; vertebral; motion; tracking

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.

几个世纪以来，医生们一直被认为是由于受伤或过度使用而导致的脊柱疾病，因此被认为是由于脊柱稳定性的丧失。在过去，这是无法证实的，但使用一种称为定量荧光透视（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，现在可以了。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物理治疗师）很少使用QF。在这个项目中，我们的目标是将人工智能（AI）引入QF，以便它可以更广泛地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椎体运动图像，允许自动测量椎体的运动。如果没有自动化，这个过程对于日常使用来说太费时了。同时，如果图像不是非常清晰和对齐良好，自动测量会带来多重挑战。图像失真（例如，由于脊柱侧弯）、退化（例如，肥胖人群中的脂肪）或褪色（例如，骨质疏松症患者）目前几乎不可避免地导致测量过程失败，从而剥夺了广泛使用QF来研究脊柱疾病的能力。我们将应用人工智能方法，使计算机能够以惊人的速度进行椎体图像的注册和跟踪，即使使用质量较差的图像。这将允许非常快速地执行大量检查，从而产生更详细的审查和验证，并且比人工操作员的速度要快得多。这将开启脊柱护理研究的新时代。该项目将利用QF数据，这些数据已经在多年的先前研究中收集，参与者同意将他们的数据用于未来的研究。本项目不需要额外招募参与者，因此不需要额外的x射线辐射风险。

项目成果

Automated Lumbar Spine Tracking in Quantitative Fluoroscopy (in preparation)

定量透视中的自动腰椎追踪（准备中）

• 期刊: IEEE Transactions on Medical Imaging
• 影响因子: 10.6
• 作者: Samaratunga R