title: A process model for direct correlation between computed tomography and histopathology application in lung cancer.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Internal Medicine, C325-GH, University of Iowa, Iowa City, IA 55242, USA.
authors.forename: Jessica C
authors.initials: JC
authors.lastname: Sieren
authors.forename: Jamie
authors.initials: J
authors.lastname: Weydert
authors.forename: Eman
authors.initials: E
authors.lastname: Namati
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Thiesse
authors.forename: Jered P
authors.initials: JP
authors.lastname: Sieren
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Reinhardt
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: Geoffrey
authors.initials: G
authors.lastname: McLennan
mesh_terms: D000368:Aged; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011859:Radiography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology*; D013223:Statistics as Topic; D013382:Subtraction Technique