Quantification of structural alterations in lung disease—a proposed analysis methodology of CT scans of preclinical mouse models and patients. (21st September 2015)

• Record Type: Journal Article
• Authors: Larsson, Emanuel; Tromba, Giuliana; Uvdal, Kajsa; Accardo, Agostino; Monego, Simeone dal; Biffi, Stefania; Garrovo, Chiara; Lorenzon, Andrea; Dullin, Christian
• Is Part Of: Biomedical physics & engineering express. Volume 1:Number 3(2015)
• Publication Date: 2015-09-21
• Subjects: lung imaging -- asthma mouse model -- quantitative image analysis -- computed tomography -- synchrotron microtomography -- phase contrast; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/1/3/035201
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: In this paper we have established a general investigative methodology for quantitative computed tomography (CT) lung image analysis in the sagittal, coronal and transversal orientation of lungs with various lung diseases. Mean values were recorded for the two parameters percentage volume and structural thickness based on stripe shaped volumes of interest (VOIs) from the XY (transversal), YZ (sagittal) and ZX (coronal) orientation, placed out in the left and right lung side. A one-way ANOVA with Tukey–Kramer 90% simultaneous confidence intervals for pair wise comparison of means was performed on each considered parameter, in order to detect any statistically significant differences in between the samples. This methodology was first tested on high resolution synchrotron micro-computed tomography images of a preclinical asthma mouse model, injected with barium sulfate filled alveolar macrophages, with the purpose of marking out asthmatic inflammation sites. Preclinical mouse models are today commonly used as artificial models for studying various human diseases, e.g. asthma. Therefore, in order to translate our methodology protocol also to clinical applications the proposed methodology was also tested on lung data sets of patients, with various lung diseases. The presented general methodology was proven to be successful for the quantification of lung structural differences in an asthma mouse model, as well as being applicable also on patient lungs with various lung diseases. The outlined analysis protocol was tested on images obtained only by means of CT, but could also potentially be applied on images of the lung obtained by other 3D-imaging techniques.