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使用的增加、CT筛查检查市场的发展以及年轻患者的成像，人们越来越关注X射线CT辐射剂量引起的公共健康风险。因此，减少这一剂量变得越来越重要，最近的NIH CT剂量管理峰会证明了这一点，其任务是实现常规的亚毫西弗CT检查。迭代重建算法是实现这一目标的关键部分，通过结合数据采集过程的物理和统计数据的详细模型，从低剂量数据中生成高质量图像。基于这些系统模型的迭代算法开始进入市场，但目前这些算法受到三个主要限制：（i）它们是非常昂贵的附加组件;（ii）它们省略了物理学的详细建模，从而限制了可用的剂量减少;以及（iii）它们比标准重建慢10 - 100倍，阻止它们用作常规扫描的默认值。完全启用迭代算法的关键是加速反投影和重投影计算瓶颈，这是通过使用InstaRecon的快速分层反投影/重投影算子来实现的。加速迭代算法使其能够在更便宜的平台上运行，提供快速重建速率，并为合并其他系统建模打开大门，从而进一步提高图像质量和降低剂量。因此，低剂量成像和迭代重建可以从高端选项移动到广泛的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快迭代重建速率，并纳入额外的系统模型，以提高剂量和伪影减少能力。系统加速将通过对分层运算符和迭代重建循环本身的算法修改来实现。附加的系统建模将被引入到层次运算符本身中，以降低计算成本，提供先进的，加速的系统模型。由此产生的系统将比现有的迭代重建平台更快，在更便宜的硬件上运行，并进一步减少剂量和伪影水平。这项新技术的好处将包括在剂量关键型应用中的上级低剂量性能，如儿科、肺癌或心脏病筛查和介入成像，以及对大型患者、植入假体或心脏起搏器的患者的CT扫描诊断质量的显著改善。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模式，从而减少患者群体的CT X射线暴露负担。 公共卫生关系：该项目有望大幅加速CT中的先进成像算法，并提高剂量降低能力。重建率的提高使低剂量成像成为常规临床应用的可能。由此产生的产品将改善肺癌和心脏病的检测，实现3D CT图像引导手术和癌症的精确放射治疗，改善大型患者和植入假体植入物和心脏起搏器的患者的成像，并降低医疗保健成本。