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

RATIONALE AND
OBJECTIVES: Multimodal imaging techniques for capturing normal and diseased human anatomy and physiology are being developed to benefit patient clinical care, research, and education. In the past, the incorporation of histopathology into these multimodal datasets has been complicated by the large differences in image quality, content, and spatial association.
MATERIALS AND METHODS: We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between nonstructurally destructive and destructive imaging such that reliable registration between radiological data and histopathology can be achieved. Registration algorithms have been designed to align the multimodal datasets, which include computed tomography, computed micro-tomography, LIMA, and histopathology data to a common coordinate system.
RESULTS: The resulting volumetric dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture, and cellular information in three dimensions. An image processing pipeline has been established to register all the multimodal data to a common coordinate system.
CONCLUSION: In this study, we have chosen to use human lung cancer nodules as an example; however, the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue. A novel process model has been established to generate cross registered multimodal datasets for the investigation of human lung cancer nodule content and associated image-based representation. Copyright 2010. Published by Elsevier Inc.