ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction

2026-02-17

AI Summary

This study addresses the challenges of storage, transmission, and real-time processing posed by massive data volumes in high-throughput X-ray computed tomography (X-CT), particularly in synchrotron radiation facilities. The authors propose a novel region-of-interest (ROI)-driven compression framework that uniquely integrates error-bounded quantization, ROI extraction, and a suite of heterogeneous compressors—encompassing both lossless and lossy algorithms—to achieve high compression efficiency while preserving critical structural information essential for downstream tasks. Experimental evaluation across seven X-CT datasets demonstrates that the proposed method achieves an average 12.34× improvement in compression ratio over standard techniques, effectively balancing compression performance with fidelity to task-relevant features.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Medical and Biological Imaging

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• X-ray Computed Tomography
• data reduction
• high-performance computing
• storage challenge
• computational efficiency

Innovation

• region-of-interest (ROI)
• error-bounded quantization
• X-ray Computed Tomography
• data compression
• high-performance computing