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WE‐C‐103‐09: Investigation of Demons Deformable Registration‐Based Methods to Measure Lung CT Texture Change Over Time
Author(s) -
Cunliffe A,
Armato S,
Fei X,
Tuohy R,
AlHallaq H
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4815558
Subject(s) - feature (linguistics) , voxel , nuclear medicine , artificial intelligence , computed tomography , image registration , mathematics , pattern recognition (psychology) , medicine , computer science , radiology , image (mathematics) , philosophy , linguistics
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<20%), significant differences in nBias existed among the three methods. For every feature, the lowest nBias was achieved when feature values were calculated on original follow‐up scans. nRoA was not significantly increased, indicating low variability in feature value change. Conclusion: Three methods to facilitate texture analysis of serial CT scans were evaluated. The bias in feature value change between matched ROIs was minimized with original follow‐up scans, using demons deformation to identify corresponding ROIs between scans. This approach could facilitate future measurement of pathologic change between CT scans without necessitating calculation of feature values on deformed scans. This work was supported, in part, by The Coleman Endowment through The University of Chicago Comprehensive Cancer Center, National Science Foundation Research Experience for Undergraduates (NSF REU) Award No. 1062909, and National Institutes of Health (NIH) Grant Nos. S10 RR021039, P30 CA14599, and T32 EB002103‐23.