Hardware for Ultra-Fast CT Reconstruction

描述(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，使用最新的层厚医用CT扫描仪进行全身扫描，层厚为亚毫米，需要大约10秒的时间才能获得，重建时间大约是原来的20倍。尽管使用昂贵的专用计算硬件，这种计算延迟仍会发生。更快的图像重建对于危及生命的创伤病例至关重要，也是加强使用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，需要更快的重建来使用计算要求高的迭代重建方法来实现新的应用，这些方法可以克服金属伪影，改善图像质量，并减少获得可接受图像质量所需的X射线剂量。同样，CT安全成像需要更快的重建速度，特别是在机场扫描托运行李时。到目前为止，CT扫描仪中图像重建的加速仅通过对计算硬件的扩展来实现。然而，由于不断增长的速度需求，简单地扩展硬件(并行化、升级化或使用更多处理器)就会带来高昂的代价。这个项目的目标是通过使用更智能的图像重建算法(即更智能的数学算法)来实现非常大的加速，这些算法是由伊利诺伊大学开发并获得专利的，并已授权给InstaRecon。对于医学应用中典型的512 W 512像素图像，这些算法将重建的数学运算次数减少了10到50倍。我们建议开发、评估和验证用于三维锥束CT扫描仪的超高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新配置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诱人的折衷。该项目的具体目标是：(I)用于3D圆形成像扫描几何的超高速算法加速硬件背投影仪的原型；(Ii)用于3D螺旋锥束几何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；以及(Iii)用于3D螺旋锥束长对象几何的超快速算法加速的完整硬件重建器。我们的目标是提供相对于两个基准的至少20W的加速：(I)在可比硬件资源上实施的传统算法，以及(Ii)当前同类最佳的商业CT重建速率。这些加速可以用来实现更复杂的算法，以产生更好的图像质量和低剂量成像。在整个项目过程中，将通过客观和主观措施和实验对图像质量进行严格控制，以确保在保持原始图像质量的同时实现前所未有的速度。

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.