Stereo CT image compositing methods for lung nodule detection and characterization.
OBJECTIVE
Stereographic display has been proposed as a possible method of improving performance in reading computed tomographic (CT) examinations acquired for lung cancer screening. Optimizing such displays is important given the large volume of image data that must be evaluated for each of these examinations. This study is designed to explore certain tradeoffs between rendering methods designed for the stereo display of CT images.


METHODS
Stereo CT image compositing methods, including distance-weighted averaging, distance-weighted maximum intensity projection (MIP), and conventional MIP, were applied to lung CT images and compared for lung nodule detection and characterization.


RESULTS
Using the Jonckheere test indicated a statistically significant (P < .01) increase in contrast among the three compositing methods. Wilcoxon-Mann-Whitney test showed significant differences in contrast between distance-weighted averaging and conventional MIP (P < .01) and between averaging and distance-weighted MIP (P < .05), but not between distance-weighted MIP and conventional MIP (P > .05). Conventional MIP compositing provided the highest image contrast, but produced ambiguities in local geometric detail and texture, whereas averaging resulted in the lowest contrast, but preserved geometric detail. Distance-weighted MIP partially recovered geometric information, which was lost in images composited by means of conventional MIP.


CONCLUSIONS
Our results indicate that distance-weighted MIP may be a better choice for nodule detection in stereo lung CT images for its high local contrast and partial preservation of geometric information, whereas compositing by means of distance-weighted averaging is preferable for nodule characterization. The relative clinical value of these compositing methods needs to be evaluated further.
Wang
Xiao Hui
XH
Department of Radiology, University of Pittsburgh, 300 Halket Street, Suite 4200, Pittsburgh, Pennsylvania 15213, USA. xwang@mail.magee.edu
Good
Walter F
WF
Fuhrman
Carl R
CR
Sumkin
Jules H
JH
Britton
Cynthia A
CA
Golla
Saraswathi K
SK
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D013382:Subtraction Technique