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首页> 外文期刊>IEEE Transactions on Radiation and Plasma Medical Sciences >Reducing the Memory Requirements of High Resolution Voxel Phantoms by Means of a Binary Tree Data Structure
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Reducing the Memory Requirements of High Resolution Voxel Phantoms by Means of a Binary Tree Data Structure

机译：通过二叉树数据结构减少高分辨率体素幻像的内存需求

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Computational human phantoms are a well-established tool used to simulate the performance of medical imaging devices. The maximum resolution of voxelized phantoms is bounded in practice by the available computer memory, which is especially limited for graphics processing unit (GPU)-based simulations. For phantoms that have been segmented into a small number of tissue types, the use of a uniform grid is inefficient because large numbers of adjacent voxels are composed of the same tissue. The computer files used to store these phantoms can be compressed into a small fraction of their original size using standard lossless compression algorithms. Compression is used for storage and distribution, but not during the simulations because compression algorithms compress the data incrementally from the first byte to the last. This approach intrinsically prevents access to random locations along the data stream, which is an essential requirement of the majority of simulation algorithms. In this paper, we propose to store voxelized phantoms in memory using a binary tree structure, as a way to accomplish both data compression and fast random access. The composition of a particular voxel can be efficiently retrieved by traversing the binary tree down from the root node to the corresponding leaf node. As a drawback, the memory savings come at the cost of more frequent memory access, which might slow down simulations in which memory access constitutes a substantial fraction of the execution time. Methods to generate and efficiently store in memory a binary tree geometry, and an example implementation in a GPU-accelerated Monte Carlo code for X-ray imaging virtual clinical trials, are presented. Results of a simulation of mammography and tomosynthesis imaging with a computational breast phantom voxelized at 50 μm show that the implemented binary tree geometry can achieve a 50-fold reduction of memory use, with a 4.7% increase in simulation time.
机译：计算人体模型是用于模拟医学成像设备性能的完善工具。体素化体模的最大分辨率实际上受可用的计算机内存限制，这对于基于图形处理单元（GPU）的模拟尤其受到限制。对于已被分割为少量组织类型的体模，由于大量相邻的体素由同一组织组成，因此使用统一的网格效率不高。使用标准无损压缩算法，可以将用于存储这些幻像的计算机文件压缩为原始大小的一小部分。压缩用于存储和分发，但在仿真过程中不使用，因为压缩算法从第一个字节到最后一个字节递增地压缩数据。这种方法本质上阻止了对沿数据流的随机位置的访问，这是大多数仿真算法的基本要求。在本文中，我们建议使用二叉树结构将体素化的体模存储在内存中，以同时实现数据压缩和快速随机访问。通过将二叉树从根节点向下遍历到相应的叶节点，可以有效地检索特定体素的组成。缺点是，节省内存的代价是需要更频繁地访问内存，这可能会减慢仿真，其中内存访问占执行时间的大部分。提出了生成和有效地在内存中存储二叉树几何形状的方法，以及在GPU加速的蒙特卡洛代码中用于X射线成像虚拟临床试验的示例实现。用50μm体素计算的乳腺体模进行乳房X线照相和断层合成成像的模拟结果表明，所实现的二叉树几何结构可以减少50倍的内存使用，模拟时间增加4.7％。

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