Individualized assessment of osteoporotic fracture risk at the spine using ultra-low dose MDCT imaging techniques and non-dedicated routine MDCT exams
使用超低剂量 MDCT 成像技术和非专用常规 MDCT 检查对脊柱骨质疏松性骨折风险进行个体化评估
基本信息
- 批准号:432290010
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:
- 资助国家:德国
- 起止时间:
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Osteoporosis is defined as a skeletal disorder characterized by compromised bone strength predisposing an individual to an increased risk of fractures. In particular, osteoporotic vertebral fractures are associated with a reduced quality of life and increased morbidity and mortality leading to a severe socio-economic burden. Clinical risk factors and Dual-energy-X-ray-absorptiometry (DXA)-based Bone Mineral Density (BMD) measurements are currently used to determine fracture risk and to initiate appropriate (drug) therapy. However, BMD values of subjects with versus without osteoporotic fractures overlap. Vertebral BMD assessment in non-dedicated clinical routine Multi-Detector Computed Tomography (MDCT) exams was superior to DXA-based BMD to predict incidental vertebral fractures. Furthermore, MDCT-based Finite Element Models (FEM) have shown to improve vertebral bone strength prediction beyond BMD. The objectives of our research project are (i) to develop dedicated ultra-low dose MDCT imaging for quantitative bone assessment and (ii) to convert non-dedicated clinical routine MDCT exams to use them adequately to predict vertebral-specific fracture risk. For ultra-low dose imaging, iterative reconstruction algorithms for improved diagnostic image quality will be developed. Furthermore, a compressed sensing inspired strategy, widely known as sparse-sampling CT will be investigated to reduce radiation exposure in a fundamentally different way. This approach allows acquiring a reduced number of projections, while the radiation exposure remains high for each individual projection image. The clear benefit of sparse-sampling acquisitions is an improved quality for each projection while circumventing the influence of electronic readout noise. As the biomechanical modeling of bone strength is dependent on different acquisition parameters and the application of iodine-containing intravenous contrast agent, these have to be taken into account to perform a reliable opportunistic osteoporosis screening. A fully-automated pipeline will be established to read and standardize the non-dedicated routine MDCT data, segment each vertebra and determine vertebra-specific fracture risk using BMD, bone texture analysis, and FEM. The PIs can show project-related publications based on already completed DFG-funded research projects in the context of osteoporosis imaging to successfully perform the proposal, which requires interdisciplinary research effort by radiologists, physicists, and bioengineers.
骨质疏松症被定义为一种骨骼疾病,其特征是骨强度受损,使个体骨折风险增加。特别是,骨质疏松性椎体骨折与生活质量降低、发病率和死亡率增加相关,导致严重的社会经济负担。临床风险因素和基于双能X射线吸收法(DXA)的骨矿物质密度(BMD)测量目前用于确定骨折风险和启动适当的(药物)治疗。然而,有与无骨质疏松性骨折的受试者的BMD值重叠。在预测偶发性椎体骨折方面,非专用临床常规多探测器计算机断层扫描(MDCT)检查中的椎体BMD评估上级基于DXA的BMD。此外,基于MDCT的有限元模型(FEM)已被证明可以改善超出BMD的椎骨强度预测。我们研究项目的目标是(i)开发用于定量骨评估的专用超低剂量MDCT成像和(ii)转换非专用临床常规MDCT检查,以充分利用它们来预测椎体特异性骨折风险。对于超低剂量成像,将开发迭代重建算法以提高诊断图像质量。此外,将研究一种受到压缩传感启发的策略,即众所周知的稀疏采样CT,以从根本上不同的方式减少辐射暴露。这种方法允许采集减少数量的投影,而对于每个单独的投影图像,辐射曝光保持高。稀疏采样采集的明显好处是提高了每个投影的质量,同时避免了电子读出噪声的影响。由于骨强度的生物力学建模取决于不同的采集参数和含碘静脉造影剂的应用,因此必须考虑这些因素以进行可靠的机会性骨质疏松症筛查。将建立一个全自动管道,以读取和标准化非专用常规MDCT数据,分割每个椎骨,并使用BMD、骨纹理分析和FEM确定椎骨特异性骨折风险。 PI可以显示基于已经完成的DFG资助的骨质疏松成像研究项目的项目相关出版物,以成功执行该提案,这需要放射科医生,物理学家和生物工程师的跨学科研究工作。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Privatdozent Dr. Thomas Baum其他文献
Privatdozent Dr. Thomas Baum的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Privatdozent Dr. Thomas Baum', 18)}}的其他基金
Optimizing prediction and understanding of osteoporotic insufficiency fractures using surrogate models, numerical simulation and quantification of local anisotropies by X-ray Vector Radiography
使用替代模型、X 射线矢量放射成像局部各向异性的数值模拟和量化来优化骨质疏松性骨折的预测和理解
- 批准号:
234903508 - 财政年份:2014
- 资助金额:
-- - 项目类别:
Research Grants
相似国自然基金
基于重要农地保护LESA(Land Evaluation and Site Assessment)体系思想的高标准基本农田建设研究
- 批准号:41340011
- 批准年份:2013
- 资助金额:20.0 万元
- 项目类别:专项基金项目
城镇居民亚健康状态的评价方法学及健康管理模式研究
- 批准号:81172775
- 批准年份:2011
- 资助金额:14.0 万元
- 项目类别:面上项目
相似海外基金
Software Platform for Automatic, Opportunistic Screening of Vertebral Compression Fractures
用于自动、机会性筛查椎骨压缩性骨折的软件平台
- 批准号:
10755827 - 财政年份:2023
- 资助金额:
-- - 项目类别:
Pooling International Cohort Studies of Long-Term Bisphosphonate Use and Atypical Femur Fractures
长期使用双膦酸盐和非典型股骨骨折的汇集国际队列研究
- 批准号:
10516684 - 财政年份:2022
- 资助金额:
-- - 项目类别:
R-FIX (Rib-FIXation System) for Severe Progressive Spinal Deformity
R-FIX(肋骨固定系统)用于治疗严重进行性脊柱畸形
- 批准号:
10482559 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Raman spectroscopy as a non-invasive, transcutaneous tool for characterizing bone health
拉曼光谱作为一种非侵入性、经皮工具,用于表征骨骼健康
- 批准号:
10462040 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Predicting who will fracture: Exploration of machine learning in the observational Women's Health Initiative Study dataset.
预测谁会骨折:观察性妇女健康倡议研究数据集中机器学习的探索。
- 批准号:
10707881 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Pooling International Cohort Studies of Long-Term Bisphosphonate Use and Atypical Femur Fractures
长期使用双膦酸盐和非典型股骨骨折的汇集国际队列研究
- 批准号:
10706659 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Predicting who will fracture: Exploration of machine learning in the observational Women's Health Initiative Study dataset.
预测谁会骨折:观察性妇女健康倡议研究数据集中机器学习的探索。
- 批准号:
10370048 - 财政年份:2022
- 资助金额:
-- - 项目类别:
A patient-specific computational technique to predict spine injury risks associated with physical activities
一种针对患者的计算技术,用于预测与体力活动相关的脊柱损伤风险
- 批准号:
10393017 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Bone health assessment through magnetic susceptibility mapping
通过磁化率图评估骨骼健康
- 批准号:
10534656 - 财政年份:2021
- 资助金额:
-- - 项目类别:
A patient-specific computational technique to predict spine injury risks associated with physical activities
一种针对患者的计算技术,用于预测与体力活动相关的脊柱损伤风险
- 批准号:
10592260 - 财政年份:2021
- 资助金额:
-- - 项目类别: