A process model for direct correlation between computed tomography and histopathology application in lung cancer

Area of Science:

• Medical imaging
• Histopathology
• Computational anatomy

Background:

• Multimodal imaging aids clinical care, research, and education.
• Integrating histopathology with other imaging modalities is challenging due to differences in image quality, content, and spatial association.

Purpose of the Study:

• To develop a novel system for bridging non-destructive and destructive imaging techniques.
• To achieve reliable registration between radiological data and histopathology.
• To create a common coordinate system for multimodal datasets.

Main Methods:

• Developed the large-scale image microtome array (LIMA) system.
• Designed registration algorithms to align computed tomography, computed micro-tomography, LIMA, and histopathology data.
• Established an image processing pipeline to register multimodal data into a common coordinate system.

Main Results:

• Created a volumetric dataset with detailed tissue information (density, structure, cellular data) in 3D.
• Successfully registered multimodal data to a common coordinate system.
• Demonstrated the system's ability to integrate diverse imaging data.

Conclusions:

• The LIMA system and associated algorithms are flexible and applicable to any soft organ tissue, not just lung cancer nodules.
• Established a novel process model for generating cross-registered multimodal datasets.
• Enables detailed investigation of tissue content and its image-based representation.