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

Jessica C Sieren; Jamie Weydert; Eman Namati; Jacqueline Thiesse; Jered P Sieren; Joseph M Reinhardt; Eric A Hoffman; Geoffrey McLennan

doi:10.1016/j.acra.2009.09.006

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

Journal article

Peer reviewed

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

Jessica C Sieren, Jamie Weydert, Eman Namati, Jacqueline Thiesse, Jered P Sieren, Joseph M Reinhardt, Eric A Hoffman and Geoffrey McLennan

Academic radiology, Vol.17(2), pp.169-180

02/2010

DOI: 10.1016/j.acra.2009.09.006

PMCID: PMC2993614

PMID: 19926496

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https://www.ncbi.nlm.nih.gov/pmc/articles/2993614View

Open Access

Abstract

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. 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. 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. 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.

Radiography

Aged

Female

Humans

Image Interpretation, Computer-Assisted - methods

Male

Middle Aged

Reproducibility of Results

Sensitivity and Specificity

Solitary Pulmonary Nodule - diagnostic imaging

Solitary Pulmonary Nodule - pathology

Statistics as Topic

Subtraction Technique

Details

Title: Subtitle: A process model for direct correlation between computed tomography and histopathology application in lung cancer
Creators: Jessica C Sieren - University of Iowa
Jamie Weydert - University of Iowa
Eman Namati - University of Iowa
Jacqueline Thiesse - University of Iowa
Jered P Sieren - University of Iowa
Joseph M Reinhardt - University of Iowa
Eric A Hoffman - University of Iowa
Geoffrey McLennan - University of Iowa
Resource Type: Journal article
Publication Details: Academic radiology, Vol.17(2), pp.169-180
DOI: 10.1016/j.acra.2009.09.006
PMID: 19926496
PMCID: PMC2993614
NLM abbreviation: Acad Radiol
ISSN: 1076-6332
eISSN: 1878-4046
Grant note: R01 CA129022-01 / NCI NIH HHS R01 CA129022 / NCI NIH HHS R01 CA129022-03 / NCI NIH HHS
Language: English
Date published: 02/2010
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984196995302771

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