Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA 19104, USA.
Phys Med Biol. 2010 Nov 7;55(21):6329-51. doi: 10.1088/0031-9155/55/21/001. Epub 2010 Oct 12.
Spline-based deformable registration methods are quite popular within the medical-imaging community due to their flexibility and robustness. However, they require a large amount of computing time to obtain adequate results. This paper makes two contributions towards accelerating B-spline-based registration. First, we propose a grid-alignment scheme and associated data structures that greatly reduce the complexity of the registration algorithm. Based on this grid-alignment scheme, we then develop highly data parallel designs for B-spline registration within the stream-processing model, suitable for implementation on multi-core processors such as graphics processing units (GPUs). Particular attention is focused on an optimal method for performing analytic gradient computations in a data parallel fashion. CPU and GPU versions are validated for execution time and registration quality. Performance results on large images show that our GPU algorithm achieves a speedup of 15 times over the single-threaded CPU implementation whereas our multi-core CPU algorithm achieves a speedup of 8 times over the single-threaded implementation. The CPU and GPU versions achieve near-identical registration quality in terms of RMS differences between the generated vector fields.
基于样条的变形配准方法在医学成像领域非常流行，因为它们具有灵活性和鲁棒性。然而，它们需要大量的计算时间才能得到足够的结果。本文对基于 B 样条的配准方法进行了两项改进。首先，我们提出了一种网格对齐方案和相关的数据结构，大大降低了配准算法的复杂度。在此网格对齐方案的基础上，我们为流处理模型中的 B 样条配准开发了高度数据并行的设计，适合在多核处理器（如图形处理单元（GPU））上实现。特别关注的是在数据并行方式下执行解析梯度计算的最佳方法。针对执行时间和配准质量对 CPU 和 GPU 版本进行了验证。在大型图像上的性能结果表明，我们的 GPU 算法相对于单线程 CPU 实现的加速比为 15 倍，而我们的多核 CPU 算法相对于单线程实现的加速比为 8 倍。在生成的矢量场之间的均方根差异方面，CPU 和 GPU 版本实现了几乎相同的配准质量。
