Reduction of FPs for Lung Nodules in MDCT by Use of Temporal Subtraction with Voxel-Matching Technique

Abstract

Detection of subtle lesions on computed tomography (CT) images is a difficult task for radiologists, because subtle lesions such as small lung nodules tend to be low in contrast, and a large number of CT images must be interpreted in a limited time. One of the solutions to the problem, a temporal subtraction technique, has been introduced in the medical field as 2D visual screening. The temporal subtraction image is obtained by subtraction of a previous image from a current one, and can be used for enhancing interval changes on medical images by removing most of the normal background structures. In this study, we have developed a method for computerized detection of lung nodules by using temporal subtraction with a voxel-matching technique in multidetector-row CT (MDCT) images. First, the candidates for nodules were detected by use of a multiple threshold technique based on the pixel value in the temporal subtraction image obtained by the voxel-matching technique. Next, a number of features were quantified, and some false positives were removed by a rule-based method with an artificial neural network. We applied our computerized scheme to 6 chest MDCT cases including 94 lung nodules. Our scheme for detecting lung nodules provided a sensitivity of 71.2 % for lung nodules with sizes under 20 mm, with 9.8 and 11.5 false positives per scan on the consistency test and validation test, respectively.