Robust BCT for Clinical Use

适合临床使用的稳健 BCT

• 批准号: 7747873
• 负责人: David Kopperdahl
• 金额: $ 35万
• 依托单位: O. N. DIAGNOSTICS, LLC
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7747873
• 关键词: Abdomen; Accounting; Address; Adoption; Affect; Age; Aging; Algorithms; Angiography; Atlases; Automation; Biomechanics; Bone Density; Businesses; Cadaver; Calibration; Caring; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical assessments; Computed Tomographic Colonography; Computer Vision Systems; Computer software; Custom; Data; Densitometry; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Dose; Dual-Energy X-Ray Absorptiometry; Early identification; Economic Burden; Elderly; Elements; Environment; Exposure to; Fracture; Future; Goals; Growth; Guide prevention; Healthcare; Healthcare Systems; Hip Fractures; Hip region structure; Hospital Costs; Image; Individual; Intervertebral disc structure; Low Dose Radiation; Lung; Machine Learning; Marketing; Measures; Medical; Medicare; Methods; Minerals; Modality; Modeling; Monitor; Morphologic artifacts; Muscle; Osteoporosis; Outcome; Patients; Pelvis; Performance; Phase; Population; Postmenopause; Protocols documentation; Public Health; Radiation; Research; Research Personnel; Risk; Scanning; Screening procedure; Second lumbar vertebra; Sensitivity and Specificity; Series; Societies; Standardization; Structure; Techniques; Technology; Testing; Training; Tube; Variant; Vertebral column; Woman; Work; X-Ray Computed Tomography; aging population; base; bone; bone strength; cohort; cost; cost effective; image processing; improved; meetings; mortality; novel; osteoporosis with pathological fracture; product development; public health relevance; reconstruction; research study; soft tissue; spine bone structure; tool; treatment effect; virtual; voltage

DESCRIPTION (provided by applicant): Osteoporosis is a major public health threat for over 50% of the population over age 50. Despite its importance, osteoporosis is largely under-treated, with less than 20% of those recommended for testing being screened. With substantial reimbursement cuts being introduced by Medicare for bone densitometry by dual energy X-ray absorptiometry (DXA, the current clinical standard), with a sensitivity of DXA for fracture prediction of less than 50%, and with the rapidly increasing size of the aging population of the U.S., there is an urgent need for additional and more sensitive modalities than DXA for clinical assessment of fracture risk. Biomechanical Computed Tomography (BCT) has emerged as a powerful alternative to DXA. This CT-based technology creates a structural "finite element" model of a patient's bone from their CT scans, and subjects that model to virtual forces in order to provide an estimate of the strength of the bone. Well validated in cadaver studies and being a better predictor of bone strength than is bone mineral density by DXA, BCT has also been shown to be highly predictive of osteoporotic fractures in clinical research studies. However, robustness remains an issue - can the technique be used easily by non-experts in research and clinical environments? Addressing this issue, the overall goal of this research is to improve the robustness of our software, such that it can automatically analyze scans from a wide range of CT scanners and using a wide variety of CT acquisition protocols, including new low-dose protocols that limit radiation exposure to the patient. Such a robust BCT diagnostic tool could then be offered as a supplementary "add-on" analysis to many types of CT exams taken for other purposes such as CT colonography, pelvic, abdominal, and spine exams, thus reducing hospital costs, incurring no addition radiation to the patient, requiring no change in the CT acquisition protocols, and therefore greatly increasing the number of patients that could be screened at low cost. Specifically, we propose in this Phase-I project to combine expertise in computer vision, CT scanning, and biomechanics in order to develop an automated method of "phantomless" cross-calibration of CT scans for robust vertebral strength assessment. Focusing on the spine, our major tasks are to perform a series of clinical studies in which patients are scanned twice using a variety of CT acquisition protocols; develop a custom external-calibration phantom and use that to determine the effects of various CT acquisition parameters on scanning standardization; and use machine learning techniques to develop a "statistical atlas" of the spine for automation of all image processing. We will combine these efforts to develop a phantomless BCT method that accounts for differences in image quality due to variations in CT scanners and acquisition protocols, including low-dose protocols, and that does so in a highly automated fashion requiring minimal user expertise and input. Should this project be successful, future work will further refine the techniques, extend them to the hip and quantitative analysis of muscle and other soft tissues, and address robustness of longitudinal changes for clinical monitoring. PUBLIC HEALTH RELEVANCE: With a mortality rate up to 30% one year after hip fracture, and an economic burden exceeding $17 billion annually, osteoporotic fracture is a debilitating condition whose impact on our aging society is growing. Early identification of those at risk for fracture can guide prevention and treatment, and BCT will provide a means for such detection with a sensitivity and specificity lacking in DXA based bone densitometry. The greater radiation exposure from CT, however, limits the market for such a diagnostic. The proposed project will result in a robust diagnostic test that significantly lowers radiation dose to the patient, and in some implementations, completely eliminates additional radiation by using CT scans already ordered for other medical purposes. Successful development of this product will broaden the pool of individuals who will benefit from a more accurate and sensitive fracture risk prediction, expand the market for O. N. Diagnostics' business, and result in an important advance in the preventative care and treatment of osteoporosis.

描述（由申请人提供）：骨质疏松症是50岁以上50％以上人口的主要公共卫生威胁。尽管其重要性，但骨质疏松症的骨质疏松症基本不足，但不到20％的建议用于筛查的骨质疏松症。通过双重能量X射线吸收仪（DXA，当前的临床标准），Medicare对骨密度测定法引入了大量补偿，并且DXA对骨折预测的敏感性小于50％，并且随着美国的敏感范围的迫切范围，迫切需要进行更高的敏感性，而迫在眉睫的人群迅速增加了DXA的范围。生物力学计算机断层扫描（BCT）已成为DXA的强大替代品。这项基于CT的技术从其CT扫描中创建了患者骨骼的结构“有限元”模型，并将对虚拟力模型的受试者产生对象，以提供骨骼强度的估计。 BCT在尸体研究中得到了良好的验证，并且是DXA的骨矿物质密度比骨矿物质密度更好的预测指标，在临床研究中，BCT还被证明是对骨质疏松性骨折的高度预测。但是，鲁棒性仍然是一个问题 - 研究和临床环境中的非专家可以轻松使用该技术吗？在解决这个问题时，这项研究的总体目标是改善我们的软件的鲁棒性，以便它可以自动分析来自广泛的CT扫描仪的扫描，并使用各种CT获取协议，包括新的低剂量协议，包括限制对患者的辐射暴露。然后，可以将这种强大的BCT诊断工具作为用于其他目的进行的许多类型的CT考试的补充“附加性”分析，例如CT结肠造影，骨盆，腹部和脊柱检查，从而降低医院成本，从而降低医院的成本，从而导致患者不需要辐射，因此不需要对CT批准的患者进行筛选，从而使数量不断增加，并且可以增加数量的数量。具体而言，我们建议在此阶段I项目中结合计算机视觉，CT扫描和生物力学方面的专业知识，以开发一种自动化的CT扫描“无幻象”跨校准的方法，以进行稳健的椎体强度评估。关注脊柱，我们的主要任务是进行一系列临床研究，其中使用各种CT获取方案对患者进行两次扫描；开发自定义的外部校准幻影，并使用它来确定各种CT采集参数对扫描标准化的影响；并使用机器学习技术来开发脊柱的“统计地图集”，以自动化所有图像处理。我们将结合这些努力，以开发一种无幻象BCT方法，该方法由于CT扫描仪和采集协议的差异（包括低剂量协议）而导致图像质量的差异，并且以高度自动化的方式需要最少的用户专业知识和投入。如果该项目成功，未来的工作将进一步完善这些技术，将其扩展到对肌肉和其他软组织的髋关节和定量分析，并解决纵向变化的稳健性以进行临床监测。 公共卫生相关性：髋部骨折后一年的死亡率高达30％，经济负担每年超过170亿美元，骨质疏松性骨折是一种令人衰弱的状况，对我们的衰老社会的影响正在增长。早期鉴定有骨折风险的人可以指导预防和治疗，而BCT将提供一种用于检测的手段，而基于DXA的骨密度测定法缺乏灵敏度和特异性。但是，CT的较大辐射暴露限制了这种诊断的市场。拟议的项目将导致一项强大的诊断测试，可显着降低对患者的辐射剂量，并且在某些实施中，通过使用已订购的其他医疗目的的CT扫描完全消除了额外的辐射。该产品的成功开发将扩大将从更准确，更敏感的断裂风险预测中受益的个人库，扩大O. N. Diagnostics业务的市场，并在预防性护理和骨质疏松症的预防护理和治疗方面取得重要进步。