Extracting Cross-Sectional Clinical Images Based on Their Principal Axes of Inertia

Abstract

Cross-sectional imaging is considered the gold standard in diagnosing a range of diseases. However, despite its widespread use in clinical practice and research, no widely accepted method is available to reliably match cross-sectional planes in several consecutive scans. This deficiency can impede comparison between cross-sectional images and ultimately lead to misdiagnosis. Here, we propose and demonstrate a method for finding the same imaging plane in images obtained during separate scanning sessions. Our method is based on the reconstruction of a “virtual organ” from which arbitrary cross-sectional images can be extracted, independent of the axis orientation in the original scan or cut; the key is to establish unique body coordinates of the organ from its principal axes of inertia. To verify our method a series of tests were performed, and the same cross-sectional plane was successfully extracted. This new approach offers clinicians access, after just a single scanning session, to the morphology and structure of a lesion through cross-sectional images reconstructed along arbitrary axes. It also aids comparable detection of morphological and structural changes in the same imaging plane from scans of the same patient taken at different times—thus potentially reducing the misdiagnosis rate when cross-sectional images are interpreted.