WE‐C‐103‐09: Investigation of Demons Deformable Registration‐Based Methods to Measure Lung CT Texture Change Over Time

• Published in: Medical Physics 2013-06, Vol.40 (6), p.482-482
• Main Authors: Cunliffe, A, Armato, S, Fei, X, Tuohy, R, Al‐Hallaq, H
• Format: Article
• Language: English
• Subjects: Awards; Cancer; Computed tomography; Lungs; Spatial analysis; Undergraduates; Vector fields
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4815558

Purpose: To compare three demons registration‐based methods to identify spatially matched regions in serial computed tomography (CT) scans for use in texture analysis. Methods: Two normal thoracic CT scans were collected from 27 patients. Over 1,000 regions of interest (ROIs) were randomly placed in the lungs of each baseline scan. Anatomically matched ROIs in the corresponding follow‐up scan were placed by mapping the baseline scan ROI center pixel to (1) the original follow‐up scan, (2) the follow‐up scan resampled to match the baseline scan voxel size, and (3) the follow‐up scan aligned to the baseline scan through affine registration. Mappings used the vector field obtained through demons deformable registration of each follow‐up scan variant to the baseline scan. 140 texture features were calculated in all ROIs. Feature value differences between paired ROIs were evaluated using Bland‐Altman analysis. For each feature, (1) the mean feature value change and (2) the distance spanned by the 95% limits of agreement were normalized to the mean feature value to obtain, respectively, normalized bias (nBias) and normalized range of agreement (nRoA). Paired Student's t‐tests were used to compare nBias across the three methods and nRoA across the three methods. Results: For 20 features with low variability (nRoA