ROIX-Comp: 优化X射线计算机断层扫描成像策略以实现数据缩减与重建
ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction
AI总结：
ROIX-Comp通过基于ROI的提取框架，结合无损和有损压缩技术，实现X-CT数据的高效压缩，压缩比提升达12.34倍。
摘要：
在高性能计算（HPC）环境中，特别是在同步辐射设施中，生成了大量X射线图像。处理大规模X射线计算机断层扫描（X-CT）数据集带来了显著的计算和存储挑战，因为它们具有高维性和大数据量。传统方法往往需要大量的存储容量和高传输带宽，限制了实时处理能力和工作流程效率。为了解决这些限制，我们引入了一种基于感兴趣区域（ROI）的提取框架（ROIX-Comp），通过识别并保留仅有的关键特征来智能压缩X-CT数据。我们的工作在减少数据量的同时，保留了对下游处理任务至关重要的信息。在预处理阶段，我们利用误差有界的量化来减少需要处理的数据量，从而提高计算效率。在压缩阶段，我们的方法结合了物体提取与多种最先进的无损和有损压缩器，从而显著提高了压缩比率。我们对七个X-CT数据集评估了该框架，并观察到与标准压缩相比，压缩比提高了12.34倍。
英文摘要：
In high-performance computing (HPC) environments, particularly in synchrotron radiation facilities, vast amounts of X-ray images are generated. Processing large-scale X-ray Computed Tomography (X-CT) datasets presents significant computational and storage challenges due to their high dimensionality and data volume. Traditional approaches often require extensive storage capacity and high transmission bandwidth, limiting real-time processing capabilities and workflow efficiency. To address these constraints, we introduce a region-of-interest (ROI)-driven extraction framework (ROIX-Comp) that intelligently compresses X-CT data by identifying and retaining only essential features. Our work reduces data volume while preserving critical information for downstream processing tasks. At pre-processing stage, we utilize error-bounded quantization to reduce the amount of data to be processed and therefore improve computational efficiencies. At the compression stage, our methodology combines object extraction with multiple state-of-the-art lossless and lossy compressors, resulting in significantly improved compression ratios. We evaluated this framework against seven X-CT datasets and observed a relative compression ratio improvement of 12.34x compared to the standard compression.
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