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Over 1000 regions of interest (ROIs) were randomly placed in the lungs of each baseline scan. Anatomically matched ROIs in the corresponding follow\u2010up scan were placed by mapping the baseline scan ROI center pixel to (1) the original follow\u2010up scan, (2) the follow\u2010up scan resampled to match the baseline scan voxel size, and (3) the follow\u2010up scan aligned to the baseline scan through affine registration. Mappings used the vector field obtained through demons deformable registration of each follow\u2010up scan variant to the baseline scan. 140 texture features distributed among five feature classes were calculated in all ROIs. Feature value differences between paired ROIs were evaluated using Bland\u2010Altman 95% limits of agreement. For each feature, (1) the mean feature value change and (2) the difference between the upper and lower limits of agreement were normalized to the mean feature value to obtain, respectively, the normalized bias and normalized range of agreement (nRoA). Nonparametric tests were used to evaluate differences in normalized bias and nRoA across the three methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>Because patient CT scans contained no pathology, minimal changes in feature values were expected (i.e., low nRoA and normalized bias). Seventy\u2010five features with very large feature value variability (nRoA \u2265 100%) were excluded from further analysis. Across the remaining 65 features, significant differences in normalized bias were observed among the three methods. The lowest normalized bias (median: 0.06%) was achieved when feature values were calculated on original follow\u2010up scans. The affine registration method achieved the lowest nRoA, though nRoA was not significantly increased using original follow\u2010up scans. Features with low nRoA values also had low normalized bias, though the converse was not necessarily true. Using nRoA as a metric, a set of 20 features having both low nRoA and normalized bias were identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>Three methods to facilitate texture analysis of serial CT scans using demons registration for ROI placement were evaluated. The bias in feature value change between matched ROIs was minimized when feature values were calculated on original baseline and follow\u2010up scans. A set of features that had both low bias and variability (nRoA) in feature value change using this method were identified. This texture analysis approach could facilitate future measurement of pathologic changes between CT scans without necessitating calculation of feature values on deformed scans.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4805110","type":"journal-article","created":{"date-parts":[[2013,5,23]],"date-time":"2013-05-23T22:02:48Z","timestamp":1369346568000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":19,"title":["Lung texture in serial thoracic CT scans: Registration\u2010based methods to compare anatomically matched regions"],"prefix":"10.1002","volume":"40","author":[{"given":"Alexandra R.","family":"Cunliffe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"III","given":"Samuel G.","family":"Armato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianhan M.","family":"Fei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachel E.","family":"Tuohy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hania A.","family":"Al\u2010Hallaq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2013,5,23]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1597431"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm.160.2.9804094"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/00004424\u2010199709000\u201000009"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2010.11.013"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470\u20107330.2010.0021"},{"key":"e_1_2_7_7_1","volume-title":"Pulmonary image registration with elastix using a standard intensity\u2010based algorithm","author":"Staring M.","year":"2010"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2828378"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/52\/19\/003"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2948318"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.07.025"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3264662"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4730505"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2007046"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/10543400701329422"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471221325.ch16"},{"key":"e_1_2_7_18_1","first-page":"275","volume-title":"Handbook of Computer Vision and Applications","author":"Wagner T.","year":"1999"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1644514"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1984.4767557"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.24861"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/41\/5\/007"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-51815-0_40"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.596224"},{"key":"e_1_2_7_25_1","unstructured":"K. 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