A comparison of human observer LROC and numerical observer ROC for tumor detection in SPECT images

Numerical observers that predict human performance in medical detection tasks can relieve some of the burden of conducting psychophysical studies. Research for this purpose has dealt primarily with receiver operating characteristic (ROC) studies with "signal-known-exactly" (SKE) detection tasks. However, clinical tasks requiring searching for tumors are more closely associated with localization ROC (LROC) studies. The authors have compared performances of humans in a LROC study to performances of a channelized Hotelling observer (CHO) in a SKE ROC study. The task was tumor detection in simulated Ga-67 scans of the chest region. The studies compared different image filters created by varying the dimensionality and cut-off frequency of a 5th-order Butterworth filter. Image reconstruction was by filtered backprojection (FBP) with multiplicative Chang attenuation correction. A total of 35 tumor locations were used. Human LROC results for 4 participants were acquired from a study of 140 images per strategy. The LROC ratings are given as areas under the LROC curve. For the ROC study, 2 constant-Q channel models were used, with parameters determined from a previous comparison of human and CHO performance in a SKE ROC study. The CHO's were applied to 200 noise realizations per location and strategy. The CHO ratings of the filtering strategies are given as areas under the ROC curve averaged over location. Correlation between the human and numerical observers was quantified with Spearman rank correlation tests. Rank correlation coefficients of 0.857 and 0.952 were found. The authors conclude that a ROC study with these constant-Q CHO's may be used to distinguish between considerably superior and inferior strategies, and thus reduce the number of strategies considered by human observers in an LROC study.