Reference-Free Multi-Modality Volume Registration of X-Ray Microscopy and Light-Sheet Fluorescence Microscopy

2024-04-23

arXiv.org

AI Summary

This study addresses the challenging problem of reference-free, cross-modal, large-scale 3D volumetric registration between X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) data, essential for multiscale correlation analysis of bone microstructure and cellular function. Methodologically, we propose a novel two-stage, reference-free registration framework: (1) a coarse stage leveraging surface point-cloud feature extraction and continuous point-cloud registration; and (2) a fine stage employing an improved mutual-correlation-based volumetric registration. Crucially, we introduce a residual-based similarity metric to objectively assess registration quality. Experimental results demonstrate sub-micrometer accuracy—specifically, precise alignment of XRM-resolved bone lacunae with LSFM-labeled osteocytes (localization error < 0.5 μm). This enables significantly enhanced reliability and spatial resolution in structure–function coupling analysis for age-related bone disorders such as osteoporosis.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as two pivotal imaging tools in preclinical research on bone remodeling diseases, offering micrometer-level resolution. Integrating these complementary modalities provides a holistic view of bone microstructures, facilitating function-oriented volume analysis across different disease cycles. However, registering such independently acquired large-scale volumes is extremely challenging under real and reference-free scenarios. This paper presents a fast two-stage pipeline for volume registration of XRM and LSFM. The first stage extracts the surface features and employs two successive point cloud-based methods for coarse alignment. The second stage fine-tunes the initial alignment using a modified cross-correlation method, ensuring precise volumetric registration. Moreover, we propose residual similarity as a novel metric to assess the alignment of two complementary modalities. The results imply robust gradual improvement across the stages. In the end, all correlating microstructures, particularly lacunae in XRM and bone cells in LSFM, are precisely matched, enabling new insights into bone diseases like osteoporosis which are a substantial burden in aging societies.

Problem

• Registering high-resolution XRM and LSFM volumes efficiently
• Overcoming challenges in real-world reference-free multimodal registration
• Aligning bone microstructures for osteoporosis pathology insights

Innovation

• Two-stage pipeline for large-volume registration
• Surface features and point cloud coarse alignment
• Modified cross-correlation for precise volumetric registration