The Australian National University, Canberra, ACT, Australia.
Comput Methods Programs Biomed. 2010 Aug;99(2):133-46. doi: 10.1016/j.cmpb.2009.11.004. Epub 2009 Dec 9.
Due to processing constraints, automatic image-based registration of medical images has been largely used as a pre-operative tool. We propose a novel method named sort and count for efficient parallelization of mutual information (MI) computation designed for massively multi-processing architectures. Combined with a parallel transformation implementation and an improved optimization algorithm, our method achieves real-time (less than 1s) rigid registration of 3D medical images using a commodity graphics processing unit (GPU). This represents a more than 50-fold improvement over a standard implementation on a CPU. Real-time registration opens new possibilities for development of improved and interactive intraoperative tools that can be used for enhanced visualization and navigation during an intervention.
由于处理限制，自动基于图像的医学图像配准在很大程度上被用作术前工具。我们提出了一种名为排序计数的新方法，用于为大规模多处理架构设计的互信息 (MI) 计算进行有效的并行化。结合并行变换实现和改进的优化算法，我们的方法使用商业图形处理单元 (GPU) 实现了 3D 医学图像的实时 (小于 1 秒) 刚性配准。这比在 CPU 上的标准实现提高了 50 多倍。实时配准为开发改进的交互式术中工具开辟了新的可能性，这些工具可用于增强干预期间的可视化和导航。
