Hardware for Ultra-Fast CT Reconstruction

用于超快速 CT 重建的硬件

• 批准号: 7405864
• 负责人: Jeffrey Brokish
• 金额: $ 36.96万
• 依托单位: INSTARECON, INC.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7405864
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DESCRIPTION (provided by applicant): Image reconstruction in x-ray computer tomography (CT) scanners lags far behind the data acquisition. For example a whole-body scan using the latest 64-slice medical CT scanners, with sub-millimeter slice thickness, which takes about 10 seconds to acquire, requires about 20 times as long to reconstruct. This computational lag occurs despite the use of expensive special-purpose computing hardware. Faster image reconstruction is critical for life-threatening trauma cases, and is key to enhancing the use of x-ray CT as a dynamic real-time imaging modality for cardiac imaging, fluoroscopy and interventional applications. Furthermore, faster reconstruction is needed to enable new applications using computationally demanding iterative reconstruction methods that can overcome metal artifacts, improve image quality, and reduce the x-ray dose required to achieve acceptable image quality. Similarly, faster reconstruction is desired in CT security imaging, especially for scanning of checked luggage at airports. To date, acceleration of image reconstruction in CT scanners has been achieved only by scaling the computing hardware. However, because of the ever-increasing speed demands, simply scaling the hardware (parallelizing, upsizing, or using more processors) carries a prohibitive price tag. The objective of this project is to achieve very large speed-ups through the use of more clever image reconstruction algorithms (i.e. more clever mathematics), which were developed and patented at the University of Illinois and have been licensed to InstaRecon. These algorithms reduce the mathematical operation counts for the reconstruction by factors of 10 to 50 for 512 W 512 pixel images typical in medical applications. We propose to develop, evaluate, and validate a hardware prototype of an ultra-fast algorithmically-accelerated image reconstruction engine for three- dimensional cone-beam CT scanners. The hardware platform will be a reconfigurable field-programmable object array (FPOA), which offers an attractive tradeoff between cost, speed, and flexibility. Specific aims of this project are prototypes of (i) an ultra-fast algorithmically accelerated hardware backprojector for the 3D circular imaging scan geometry; (ii) a fast complete software reconstruction algorithm for the 3D helical cone beam geometry, for the so-called long object problem, applicable to diagnostic imaging; and (iii) an ultra-fast algorithmically accelerated complete hardware reconstructor for the 3D helical cone beam long object geometry. We aim to provide a speed-up of at least 20W relative to two benchmarks: (i) conventional algorithms implemented on comparable hardware resources, and (ii) current best-in class commercial CT reconstruction rates. These speed-ups can be used to implement more sophisticated algorithms to produce better image quality and for low-dose imaging. Stringent control of image quality by both objective and subjective measures and experiments will be applied throughout the course of the project to ensure that the unprecedented speed-up is achieved while maintaining pristine image quality.

描述（由申请人提供）：X射线计算机断层扫描（CT）扫描仪中的图像重建落后于数据采集。例如，使用最新的64板医疗CT扫描仪进行全身扫描，其次毫计厚度厚度大约需要10秒钟才能重建约20倍。尽管使用了昂贵的专用计算硬件，但仍会发生这种计算滞后。更快的图像重建对于威胁生命的创伤病例至关重要，并且是增强X射线CT用作心脏成像，荧光镜检查和介入应用的动态实时成像方式的关键。此外，需要更快的重建来使用计算苛刻的迭代重建方法启用新应用程序，以克服金属伪像，提高图像质量并减少获得可接受的图像质量所需的X射线剂量。同样，在CT安全成像中需要更快的重建，尤其是在机场扫描托运行李的情况下。迄今为止，仅通过缩放计算硬件才能实现CT扫描仪中图像重建的加速。但是，由于速度不断增长的需求，只需扩展硬件（并行化，升级或使用更多处理器）即可具有超值的价格标签。该项目的目的是通过使用更聪明的图像重建算法（即更聪明的数学）来实现很大的加速，这些算法是在伊利诺伊大学开发和专利的，并已获得Instarecon的许可。这些算法将重建的数学操作计数减少了512 W 512 W 512像素图像在医疗应用中典型的图像的因素。我们建议开发，评估和验证用于三维锥形梁CT扫描仪的超快速算法加速图像重建引擎的硬件原型。硬件平台将是可重新配置的现场可编程对象阵列（FPOA），该对象阵列（FPOA）在成本，速度和灵活性之间提供了有吸引力的权衡。该项目的具体目的是（i）3D圆形成像扫描几何形状的超快速算法加速硬件背面的原型； （ii）针对3D螺旋锥光束几何形状的快速完整软件重建算法，用于所谓的长对象问题，适用于诊断成像； （iii）3D螺旋锥长对象几何形状的超快速算法加速了完整的硬件重建器。我们的目标是相对于两个基准的速度至少达到20W：（i）在可比硬件资源上实施的常规算法，以及（ii）当前最佳类别的商业CT重建率。这些速度可用于实施更复杂的算法，以产生更好的图像质量和低剂量成像。在项目的整个过程中，将通过客观和主观措施和实验对图像质量进行严格的控制，以确保在保持原始图像质量的同时实现前所未有的加速。