Temporal Subtraction of Virtual Dual-Energy Chest Radiographs for Improved Conspicuity of Growing Cancers and Other Pathologic Changes

A temporal-subtraction (TS) technique provides enhanced visualization of tumor growth and subtle pathologic changes between previous and current chest radiographs (CXRs) from the same patient. Our purpose was to develop a new TS technique incorporating "virtual dual-energy" technology to improve its enhancement quality. Our TS technique consisted of ribcage edge detection, rigid body transformation based on a global alignment criterion, image warping 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 "teaching" soft-tissue CXRs obtained with real dual-energy radiography. Once trained, the MTANNs did not require a dual-energy system and provided "soft-tissue" images. Our database consisted of 100 sequential pairs of CXR studies from 53 patients. To assess the registration accuracy and clinical utility, a chest radiologist subjectively rated the original TS and rib-suppressed TS images on a 5-point scale. By use of "virtual dual-energy" technology, 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 original TS images (3.5; 3.6) (P<0.01; P<0.02, respectively). Our "virtual dual-energy" TS CXRs can provide improved enhancement quality of TS images for the assessment of pathologic change.