HPC Software for Medical Imaging

单核计算机的性能，即传统的中央处理器，预计不会像过去那样继续以同样的速度增长。为了提高计算机的整体性能，制造商现在提供多核处理器。为了开发更快的软件，程序员将被迫使用多核技术。大众市场的力量意味着现在有两种相对便宜的硬件配置，每一个都有大约100核。这些是计算机集群，其中个人电脑联网在一起，还有显卡。显卡的性能最近已经超过了cpu。显卡还有便宜和独立的优点。在医学成像中，图像的对齐或配准是临床图像分析的重要步骤。在阿尔茨海默氏症或其他形式的痴呆症等疾病中，大脑会在很长一段时间内萎缩。在较短的时间尺度上，当并排观察两幅图像时，这些变化并不明显。配准通过考虑患者位置和扫描仪变化的差异来对齐图像。它还提供了局部组织体积变化的测量。这些算法需要运行数小时，比病人接受扫描的时间还要长。如果数据处理可以在几分钟内完成，则可以进行更详细的后续扫描。配准也用于结合大量数据集来确定正常和异常解剖。这通常被称为地图集建设。地图集的创建需要大量的内存，这目前限制了可以包含的数据集的数量。构建地图集非常适合计算机集群。一旦建立，如果一个图谱可以在病人的图像上注册，而他们还在扫描仪中，它就可以提供关于病人异常解剖结构的现场信息。一些较新的磁共振成像（MRI）技术，如弥散加权成像和功能性磁共振成像，可以提供大脑中神经连接的详细信息或显示思想。这些数据的处理需要图像对齐，同样，如果这可以在几分钟内完成，诊断将得到加强。该提案旨在开发使用高级语言的算法，用于图形卡或计算机集群。以前在这个领域的许多工作都需要专门的硬件知识和相当的编程技能。通过使用较新的高级语言，我们将能够更多地关注应用程序和问题，而不是硬件和软件体系结构的细节。这有助于编写高质量的代码，因为它可以被其他人轻松阅读，在其他系统上进行检查，并与调试和可视化工具集成。我们的目标是演示和评估临床应用中的注册算法，这些算法目前由于数据处理太慢或内存密集而不可行。

The performance of computers with a single core , i.e. a traditional CPU, is not expected to continue increasing at the same rate as in the past. To boost overall computer performance, manufacturers now provide multi-core processors. For the development of faster software, programmers are going to be forced to make use of multi-core technology. The power of the mass market means that there are now two relatively cheap hardware configurations each with about 100 cores. These are computer clusters where PCs are networked together, and, graphics cards. The performance of graphics cards has recently been outstripping that of CPUs. Graphics cards have the added advantages of being cheap and self-contained.In medical imaging, the alignment or registration of images is an important step in clinical image analysis. In diseases such as Alzheimer's or other forms of dementia, the brain shrinks over an extended period of time. Over shorter time scales these changes are not obvious when looking at two images side-by-side. Registration aligns images by accounting for differences in the patient position and scanner variations. It also provides a measure of local changes in tissue volume. These algorithms take hours to run, longer than the time a patient is in a scanner. If the data processing could be complete with a few minutes, more detailed follow-up scanning could be possible.Registration is also used to combine large numbers of datasets to determine normal and abnormal anatomy. This is often called atlas building. The creation of atlases needs large amounts of memory and this currently limits the number of datasets that can be included. Building an atlas is well suited to a computer cluster. Once created, if an atlas could be registered with a patient's image whilst they were still in the scanner, it could provide information on-the-spot about abnormal anatomy in the patient. Some of the newer Magnetic Resonance Imaging (MRI) techniques such as diffusion weighted imaging and functional MRI provide detailed information about nerve connections in the brain or show up thoughts . The processing of this data requires images to be in alignment, again, if this could be complete in minutes, diagnosis would be enhanced. This proposal aims to develop algorithms using high-level languages for use on graphics cards or computer clusters. Much of the previous work in this area has required specialised knowledge of the hardware and considerable programming skills. By using the newer high-level languages, we will be able to concentrate more on the applications and problems than the details of the hardware and software architecture. This lends itself to high-quality code because it can be read easily by others, checked on other systems and integrates with debugging and visualisation tools.We aim to demonstrate and evaluate registration algorithms in clinical applications that are currently not feasible due to the data processing being too slow or memory-intensive.