We developed a novel temporal-subtraction (TS) technique combined with virtual dual-energyEtechnology for improved conspicuity of growing cancers and other pathologic changes in digital chest radiography (CXR). Digital CXR makes use of advanced image-processing techniques in the radiology viewing environment. A TS technique provides enhanced visualization of tumor growth and subtle pathologic changes between previous and current CXRs from the same patient. Our purpose was to develop a new TS technique incorporating virtual dual-energyEtechnology to improve its enhancement quality. Our TS technique consisted of ribcage edge detection, rigid body transformation based on a global alignment criterion, image warping based on local spatial displacement vectors under the maximum cross-correlation criterion, and subtraction between the registered previous and current images. A major problem with TS was obscuring of abnormalities by rib artifacts due to misregistration. To reduce the rib artifacts, we developed a massive-training artificial neural network (MTANN) for separation of ribs from soft tissue. The MTANN was trained with input CXRs and the corresponding teachingEsoft-tissue CXRs obtained with dual-energy radiography. Once trained, the MTANNs did not require a dual-energy system and provided soft-tissue images in which ribs were substantially suppressed (thus the term virtual dual-energyEtechnology). Our database consisted of 100 sequential pairs of digital CXR studies from 53 patients. To assess the registration accuracy and clinical utility, a chest radiologist subjectively rated original TS and rib-suppressed TS images on a 5-point scale. By use of virtual dual-energyEtechnology, the contrast of ribs in the original CXRs was reduced to 8% while maintaining that of soft tissue; thus, rib artifacts in the TS images were reduced substantially. The registration accuracy and clinical utility ratings for TS rib-suppressed images (3.7; 3.9) were significantly better than those for the original TS images (3.5; 3.6) (P<0.01; P<0.02, respectively). Our virtual dual-energyEtechnology reduced rib artifacts in TS CXRs and improved the enhancement quality of TS images for the assessment of pathologic change (see the figure). Thus, our TS combined with virtual dual-energyEtechnology would be useful for radiologists in the assessment of tumor growth and other pathologic changes between previous and current digital CXRs.