Author/Authors :
Fan, Yifang School of Physical Education and Sport Science - Fujian Normal University - Fuzhou , China , Fan, Yuzhou School of Physical Education and Sport Science - Fujian Normal University - Fuzhou , China , Li, Zhiyu School of Physical Education and Sport Science - Fujian Normal University - Fuzhou , China , Sun, Yueyang School of Physical Education and Sport Science - Fujian Normal University - Fuzhou , China , Li, Ruining School of Physical Education and Sport Science - Fujian Normal University - Fuzhou , China , Luo, Liangping Medical Imaging Center - The First Affiliated Hospital of Jinan University - Guangzhou , China , Djuric, Marija Laboratory for Anthropology - Institute of Anatomy - School of Medicine - University of Belgrade - Belgrade, Serbia , Antonijevic, Djordje Laboratory for Anthropology - Institute of Anatomy - School of Medicine - University of Belgrade - Belgrade, Serbia , Milenkovic, Petar Institute for Oncology and Radiology of Serbia - University of Belgrade - Belgrade, Serbia
Abstract :
Cross-sectional imaging is considered the gold standard in diagnosing a range of diseases. However, despite its widespread use inclinical practice and research, no widely accepted method is available to reliably match cross-sectional planes in several consecutivescans. This deficiency can impede comparison between cross-sectional images and ultimately lead to misdiagnosis. Here, wepropose 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 itsprincipal axes of inertia. To verify our method a series of tests were performed, and the same cross-sectional plane was successfullyextracted. This new approach offers clinicians access, after just a single scanning session, to the morphology and structure of alesion through cross-sectional images reconstructed along arbitrary axes. It also aids comparable detection of morphological andstructural changes in the same imaging plane from scans of the same patient taken at different times—thus potentially reducingthe misdiagnosis rate when cross-sectional images are interpreted.
Keywords :
Extracting Cross-Sectional , Clinical Images , Their Principal Axes , Inertia , CSI
