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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. [ABSTRACT FROM AUTHOR]

Investigating the structure and function of alveoli in vivo is crucial for understanding the normal and diseased lungs. The authors image the alveoli of mice in vivo using a custom fibre optic catheter-based laser scanning confocal microscope. Images obtained using this system are analysed with an automated software application for alveolar size, wall thickness and number. Results show that direct dynamic visualisation of alveoli and surrounding structures is possible in vivo, with high resolution. Early results indicate high heterogeneity in alveolar structure in vivo, as opposed to an ordered uniform structure. Using the techniques presented here, there is great promise for advancing our knowledge of the functional unit of the lung, the alveolus, for alveolar mechanics, cell traffic and three-dimensional structural visualisation. [ABSTRACT FROM AUTHOR]

Abstract: Gaining a complete and comprehensive understanding of lung cancer nodule histological compositions and how these tissues are represented in radiological data is important not only for expanding the current knowledge base of cancer growth and development but also has potential implications for classification standards, radiological diagnosis methods and for the evaluation of treatment response. In this study we generate large scale histological segmentations of the cancerous and non-cancerous tissues within resected lung nodules. We have implemented a processing pipeline which allows for the direct correlation between histological data and spatially corresponding computed tomography data. Utilizing these correlated datasets we evaluated the statistical separation between Hounsfield Unit (HU) histogram values for each tissue type. The findings of this study revealed that lung cancer nodules contain a complex intermixing of cellular tissue types and that trends exist in the relationship between these tissue types. It was found that the mean Hounsfield Unit values for isolated lung cancer nodules imaged with computed tomography, had statistically significantly different values for non-solid bronchoalveolar carcinoma, solid cancerous tumor, blood, and inactive fibrotic stromal tissue. [Copyright &y& Elsevier]