Many visual difference predictors (VDPs) have used basic psychophysical data (such as ModelFest) to calibrate the algorithm parameters and to validate their performances. However, the basic psychophysical data often do not contain sufficient number of stimuli and its variations to test more complex components of a VDP. In this paper we calibrate the Visual Difference Predictor for High Dynamic Range images (HDR-VDP) using radiologists' experimental data for JPEG2000 compressed CT images which contain complex structures. Then we validate the HDR-VDP in predicting the presence of perceptible compression artifacts. 240 CT-scan images were encoded and decoded using JPEG2000 compression at four compression ratios (CRs). Five radiologists participated to independently determine if each image pair (original and compressed images) was indistinguishable or distinguishable. A threshold CR for each image, at which 50% of radiologists would detect compression artifacts, was estimated by fitting a psychometric function. The CT images compressed at the threshold CRs were used to calibrate the HDR-VDP parameters and to validate its prediction accuracy. Our results showed that the HDR-VDP calibrated for the CT image data gave much better predictions than the HDR-VDP calibrated to the basic psychophysical data (ModelFest + contrast masking data for sine gratings).