Ella A. Kazerooni MD, Presenter: Nothing to Disclose

Craig Galban, Abstract Co-Author: Nothing to Disclose

Meilan Han, Abstract Co-Author: Nothing to Disclose

Charles R. Meyer PhD, Abstract Co-Author: Nothing to Disclose

Tim Johnson, Abstract Co-Author: Nothing to Disclose

Komal Chughtai, Abstract Co-Author: Nothing to Disclose

Jennifer Boes, Abstract Co-Author: Nothing to Disclose

Alnawaz Rehemtulla PhD, Abstract Co-Author: Stockholder, ImBio, LLC

Fernando J. Martinez MD, Abstract Co-Author: Nothing to Disclose

Brian Dale Ross PhD, Abstract Co-Author: ImBio, LLC

COPD is a complex disease with emphysema, large and small airway components. To evaluate the sensitivity of Parametric Response Mapping (PRM), a voxel-by-voxel image analysis technique, to identify diffuse and local changes in lung parenchyma that occur between inspiration and expiration in patients with COPD.

Quantitative lung CT maps of COPD patients (n=27) were acquired at inspiration and expiration as part of the COPDGene Study. Lung parenchyma was segmented and the inspiratory scan spatially aligned to the expiratory scan using a warping registration algorithm. PRM, applied to CT images in Hounsfield units (PRMHU), was determined by calculating the difference in voxel density within the lungs. Each voxel was categorized as having a significant increase (PRMHU+: designated red), decrease (PRMHU-: designated blue) or no change (PRMHU0: designated green) in lung attenuation. PRM and the volume fraction of lung with attenuation < -950HU (emph%), were also analyzed. Univariate and multivariate linear regression models were performed to assess the correlation of PRM with emphysema, respectively, to forced expiratory volume at one second (FEV1%).

PRMHU-, the volume fraction of lung with decreasing lung attenuation, was a strong univariate predictor of the severity of COPD, as determined by FEV1% (p<0.0001). PRMHU- and emph% were both significant parameters in the multivariate linear model predicting FEV1% (p=0.0005 and p=0.001, respectively), suggesting that PRMHU- contributes additional information beyond what is obtained from emph%.

PRM can detect variations in lung parenchyma between inspiration and expiration for which there is not currently a consistent and robust technique, and provides additional information beyond emph%. PRM may be an additional quantitative CT biomarker to use in characterizing COPD beyond measurements of airway wall thickness and emphysema currently in use.

Spirometry-based COPD prevalence estimates lack precision with respect to the underlying components of COPD. PRM provides more objective assessment and visualization of local lung progression of COPD

Kazerooni, E, Galban, C, Han, M, Meyer, C, Johnson, T, Chughtai, K, Boes, J, Rehemtulla, A, Martinez, F, Ross, B, Parametric Response Mapping of Quantitative Computed Tomography for Assessment of Local Lung Progression of COPD.  Radiological Society of North America 2011 Scientific Assembly and Annual Meeting, November 26 - December 2, 2011 ,Chicago IL. http://archive.rsna.org/2011/11016800.html