Advanced Algorithmic Acceleration and System Modeling for Low-Dose CT Imaging

低剂量 CT 成像的先进算法加速和系统建模

• 批准号: 8315643
• 负责人: Jeffrey Brokish
• 金额: $ 15.47万
• 依托单位: INSTARECON, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8315643
• 关键词: Acceleration; Algorithms; Artificial cardiac pacemaker; Biological Models; Childhood; Clinical; Data; Detection; Development; Diagnostic; Dose; Dose-Limiting; Family; Goals; Grant; Health Care Costs; Heart Diseases; Hospitals; Housing; Image; Image-Guided Surgery; Implant; Industry; Left; Malignant neoplasm of lung; Marketing; Measures; Metals; Metric System; Modality; Modeling; Modification; Morphologic artifacts; Noise; Patients; Performance; Phase; Physics; Process; Prosthesis; Public Health; Radiation; Reading; Relative (related person); Resolution; Risk; Running; Scanning; Screening procedure; Small Business Innovation Research Grant; Source; System; Testing; Time; Translating; United States National Institutes of Health; Update; Work; X-Ray Computed Tomography; advanced system; base; cancer radiation therapy; cost; data acquisition; detector; improved; lung cancer screening; new technology; patient population; prevent; prototype; radiologist; reconstruction; statistics

DESCRIPTION (provided by applicant): With the increased use of x-ray CT, the development of a market for CT screening exams, and the imaging of younger patients, there is a growing concern about the public health risk caused by the radiation dose delivered by x-ray CT. The reduction of this dose has therefore taken on increased importance, as evidenced by the recent NIH Summit on Managing Dose in CT with the mandate of achieving the routine sub-millisievert CT exam. Iterative reconstruction algorithms are a key part in accomplishing this goal, producing high-quality images from low-dose data by incorporating detailed models of the physics and statistics of the data acquisition process. Iterative algorithms based on these system models are beginning to enter the marketplace, but currently these algorithms suffer from three main limitations: (i) they are a very expensive add-on; (ii) they leave out detailed modeling of the physics, thus limiting the available dose reduction; and (iii) they are 10 - 100 times slower than standard reconstruction, preventing their use as a default for routine scans. The key to fully enabling iterative algorithms is acceleration of the backprojection and reprojection computational bottleneck, which is accomplished through the use of InstaRecon's fast hierarchical backprojection/reprojection operators. Accelerating the iterative algorithm enables it to run on a less expensive platform, delivering fast reconstruction rates, and opens the door to incorporation of other system modeling, allowing for further image quality improvement and dose reduction. Thus, low-dose imaging and iterative reconstruction can move from a high-end option to the default scanning mode for a wide range of CT scanner hardware. The overall goal of this SBIR project is to accelerate iterative reconstruction rates even further and incorporate additional system models to improve dose and artifact reduction capabilities. The system acceleration will be achieved through algorithmic modifications to the hierarchical operators and the iterative reconstruction loop itself. Additional system modeling wil be introduced at a reduced computational cost through incorporation into the hierarchical operators themselves, providing advanced, accelerated system models. The resulting system will be faster than existing iterative reconstruction platforms, run on less expensive hardware, with additional reduction in dose and artifact levels. Benefits of the new technology will include superior low-dose performance in dose-critical applications such as pediatric, screening for lung cancer or heart disease, and interventional imaging, and significant improvement in diagnostic quality of CT scans of large patients, or of patients with prosthetic implants or cardiac pacemakers. Moreover, this project will help make iterative algorithm-based low-dose imaging a common scanning modality, reducing the burden of CT x-ray exposure for the patient population at large. PUBLIC HEALTH RELEVANCE: This project promises dramatic acceleration of advanced image formation algorithms in CT, with improved dose reducing capabilities. The increased reconstruction rates make it possible for low-dose imaging to be brought into routine clinical use. The resulting product will improve the detection of lung cancer and heart disease, enable 3D CT image-guided surgery and accurate radiotherapy for cancer, improve the imaging of large patients and patients with prosthetic implants and cardiac pacemakers, and reduce healthcare costs.

描述（由申请人提供）：随着X射线CT的使用越来越多，CT筛查检查市场的发展以及对年轻患者的成像的成像，人们对由X射线CT提供的放射剂量引起的公共卫生风险越来越关注。因此，减少这种剂量的重要性提高了，正如最近在CT中管理剂量的NIH峰会所证明的，其任务是实现常规子米利西特CT考试。迭代重建算法是实现此目标的关键部分，通过结合数据采集过程的物理学和统计数据，从低剂量数据中产生高质量的图像。基于这些系统模型的迭代算法开始进入市场，但是目前，这些算法受到三个主要局限性：（i）它们是非常昂贵的附加组件； （ii）他们忽略了物理学的详细建模，从而限制了可用剂量的减少； （iii）它们的速度比标准重建速度慢10-100倍，以阻止其用作常规扫描的默认值。充分启用迭代算法的关键是反向投影和再投影计算瓶颈的加速度，这是通过使用Instarecon快速分层反向投影/再投入式操作员来完成的。加速迭代算法使其能够在较便宜的平台上运行，可提供快速的重建率，并为融合其他系统建模的门打开了大门，从而可以进一步改进图像质量和减少剂量。因此，低剂量成像和迭代重建可以从高端选项转移到各种CT扫描仪硬件的默认扫描模式。该SBIR项目的总体目标是进一步加速迭代重建率，并结合其他系统模型，以提高剂量和伪像减少功能。系统加速度将通过对层次运算符和迭代重建循环本身的算法修改来实现。通过将其纳入层次运营商本身，以降低计算成本来引入其他系统建模，从而提供高级，加速的系统模型。最终的系统将比现有的迭代重建平台更快，该平台运行较便宜的硬件，并增加了剂量和人工制品水平。新技术的好处将包括在关键剂量应用中的出色低剂量性能，例如小儿，肺癌或心脏病的筛查以及介入的成像，以及大型患者的CT扫描诊断质量的诊断质量显着提高，或者患有假肢植入术或脱颖而出的患者的诊断质量。此外，该项目将有助于使基于迭代算法的低剂量成像成为一种常见的扫描方式，从而减轻了CT X射线暴露的负担。 公共卫生相关性：该项目有望在CT中急剧加速高级图像形成算法，并提高了剂量降低功能。提高的重建率使得低剂量成像的常规临床用途成为可能。最终的产品将改善对肺癌和心脏病的检测，使3D CT图像引导的手术以及准确的放射疗法对癌症进行，改善大型患者和患有假体植入物和心脏起搏器的患者的影像，并降低医疗保健成本。