Hardware for Ultra-Fast CT Reconstruction

描述（由申请人提供）：x射线计算机断层扫描（CT）扫描仪的图像重建远远落后于数据采集。例如，使用最新的64层医学CT扫描仪进行全身扫描，切片厚度为亚毫米，大约需要10秒才能获得，而重建需要大约20倍的时间。尽管使用了昂贵的专用计算硬件，这种计算滞后还是会发生。更快的图像重建对于危及生命的创伤病例至关重要，并且是增强x射线CT作为心脏成像、透视和介入应用的动态实时成像方式的关键。此外，需要更快的重建来实现使用计算要求高的迭代重建方法的新应用，这些方法可以克服金属伪影，提高图像质量，并减少实现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，特别是在机场扫描托运行李时。迄今为止，CT扫描仪图像重建的加速只能通过缩放计算硬件来实现。然而，由于不断增长的速度需求，简单地扩展硬件（并行化、放大或使用更多处理器）的代价令人望而却步。该项目的目标是通过使用更聪明的图像重建算法（即更聪明的数学）来实现非常大的加速，该算法由伊利诺伊大学开发并获得专利，并已授权给InstaRecon。这些算法将医学应用中典型的512 W 512像素图像的重建数学运算次数减少了10到50倍。我们建议开发、评估和验证一个用于三维锥束CT扫描仪的超快速算法加速图像重建引擎的硬件原型。硬件平台将是一个可重构的现场可编程对象阵列（FPOA），它在成本、速度和灵活性之间提供了一个有吸引力的权衡。该项目的具体目标是(i)用于3D圆形成像扫描几何形状的超快速算法加速硬件背投仪的原型；（ii）一种快速完整的软件重建算法，用于三维螺旋锥光束几何，用于所谓的长物体问题，适用于诊断成像；以及（iii）用于三维螺旋锥束长物体几何的超快速算法加速完整硬件重构器。我们的目标是提供相对于两个基准的至少20W的加速：(i)在可比较的硬件资源上实现的传统算法，以及（ii）当前同类最佳的商业CT重建率。这些加速可用于实现更复杂的算法，以产生更好的图像质量和低剂量成像。在整个项目过程中，将通过客观和主观的措施和实验来严格控制图像质量，以确保在保持原始图像质量的同时实现前所未有的加速。

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.