发表机构
The University of Texas MD Anderson Cancer Center UTHealth Houston; The University of Texas MD Anderson Cancer Center; Geneva University Hospital; The University of Texas Health Science Center at Houston; Rice University（德克萨斯大学安德森癌症中心UT健康休斯顿研究生院; 德克萨斯大学安德森癌症中心; 日内瓦大学医院; 德克萨斯大学健康科学中心休斯顿分校; 莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Flash-Radiomics提出混合CPU-CUDA引擎，统一标量提取与空间映射，通过IBSI合规及一致性测试，在空间映射中CUDA加速显著，性能优于现有工具。
AI 中文摘要
背景与目标：空间映射保留了影像组学特征的空间分布，但计算成本和碎片化软件限制了其使用。我们开发了Flash-Radiomics，具备标量提取和空间映射功能，采用中央处理器（CPU）后端、混合计算统一设备架构（CUDA）后端、一致的特征名称以及层次数据格式第5版（HDF5）存储。方法：我们评估了图像生物标志物标准化倡议（IBSI）合规性、CPU-CUDA一致性以及端到端处理时间。合规性测试包括825项第1章（IBSI-1）测试，涵盖165个高共识特征，以及323项第2章（IBSI-2）测试，具有数值参考。一致性测试包括1,148对标量对和93对空间映射对。端到端处理时间对每个输入感兴趣体积（VOI）大小测量五次。比较包括医学图像影像组学处理器（MIRP）和PyRadiomics的102个共享标量特征，以及PyRadiomics的93个共享空间映射。结果：两个后端均通过了全部1,148项IBSI测试，所有配对结果均一致。在最大标量输入下，CPU需要76.343秒，混合CUDA需要81.915秒；CPU比MIRP快4.7倍，比PyRadiomics快190.7倍。在两个后端均完成的最大空间输入下，混合CUDA相对于CPU将处理时间减少了68.6%（79.280秒对252.791秒）。在PyRadiomics完成的最大空间输入下，混合CUDA快84.9倍。结论：Flash-Radiomics统一了标准化标量提取、空间映射、一致的CPU-CUDA结果和HDF5存储。在测试条件下，CPU处理时间在标量提取方面相似或更短，而混合CUDA在空间映射方面更快。
英文摘要
Background and Objectives: Spatial mapping retains the spatial distribution of radiomic features, but computational cost and fragmented software limit its use. We developed Flash-Radiomics with scalar extraction and spatial mapping, a central processing unit (CPU) backend, a hybrid Compute Unified Device Architecture (CUDA) backend, consistent feature names, and Hierarchical Data Format version 5 (HDF5) storage. Methods: We evaluated Image Biomarker Standardisation Initiative (IBSI) compliance, CPU-CUDA concordance, and end-to-end processing time. Compliance testing included 825 chapter 1 (IBSI-1) tests covering 165 high-consensus features and 323 chapter 2 (IBSI-2) tests with numerical references. Concordance testing included 1,148 scalar pairs and 93 spatial-map pairs. End-to-end processing time was measured five times per input volume of interest (VOI) size. Comparisons included the Medical Image Radiomics Processor (MIRP) and PyRadiomics for 102 shared scalar features and PyRadiomics for 93 shared spatial maps. Results: Both backends passed all 1,148 IBSI tests, and all paired results were concordant. At the largest scalar input, CPU required 76.343 s and hybrid CUDA 81.915 s; CPU was 4.7 times faster than MIRP and 190.7 times faster than PyRadiomics. At the largest spatial input completed by both backends, hybrid CUDA reduced processing time by 68.6% relative to CPU (79.280 versus 252.791 s). At PyRadiomics' largest completed spatial input, hybrid CUDA was 84.9 times faster. Conclusions: Flash-Radiomics unified standardized scalar extraction, spatial mapping, concordant CPU-CUDA results, and HDF5 storage. CPU processing time was similar or shorter for scalar extraction, whereas hybrid CUDA was faster for spatial mapping under the tested conditions.
Comments20 pages, 5 figures
