Perceived features reported as nodules: interpretation of spiral chest CT scans

The normal mode analysis shape detection method for automated shape determination of lung nodules

Surface morphology and shape in general are important predictors for the behavior of solid-type lung nodules detected on CT. More broadly, shape analysis is useful in many areas of computer-aided diagnosis and essentially all scientific and engineering disciplines. Automated methods for shape detection have all previously, to the author's knowledge, relied on some sort of geometric measure. I introduce Normal Mode Analysis Shape Detection (NMA-SD), an approach that measures shape indirectly via the motion it would undergo if one imagined the shape to be a pseudomolecule. NMA-SD allows users to visualize internal movements in the imaging object and thereby develop an intuition for which motions are important, and which geometric features give rise to them. This can guide the identification of appropriate classification features to distinguish among classes of interest. I employ normal mode analysis (NMA) to animate pseudomolecules representing simulated lung nodules. Doing so, I am able to assign a testing set of nodules into the classes circular, elliptical, and irregular with roughly 97 % accuracy. This represents a proof-of-principle that one can obtain shape information by treating voxels as pseudoatoms in a pseudomolecule, and analyzing the pseudomolecule's predicted motion.

A new software tool for removing, storing, and adding abnormalities to medical images for perception research studies

RATIONALE AND OBJECTIVES

Image perception studies have been difficult to perform using clinical images because of the problems associated with obtaining proven abnormalities and appropriate normal controls. The objective of this research was to develop and evaluate interactive software that allows the seamless removal, archiving and insertion of abnormal areas from computed tomography (CT) lung image sets for use in image perception research.

MATERIALS AND METHODS

The software tools for removing, archiving, and adding lesions are described in detail. The efficacy of the software to remove abnormal areas of lung CT studies was evaluated by having radiologists select the one altered image from a display of four. The software for adding lesions was evaluated by having radiologists classify displayed CT slices with lesions as real or artificial along with their confidence level.

RESULTS

Observers could not reliably detect when images had been altered by the software. In the lesion-removal experiment, the observers correctly identified the altered display in only 15.8 +/- 2.8 of 56 sets. In the lesion-add experiment, the observers correctly identified the artificially placed lesions in 38.2 +/- 3.9 of 77 sets. The frequency distribution of the correct responses did not differ from that expected from chance selection.

CONCLUSIONS

The results from both of these experiments demonstrate that radiologists could not distinguish between original and altered images. We conclude that this software can be used with volumetric CT lung images for creating normal control and target data sets for medical image perception research.

CT imaging of peripheral pulmonary vessel disease

The diseases concerning the small pulmonary vessels are difficult to diagnose. Pathologic findings are rarely limited to the small vessels, and a continuum between the involvement of small and large vessels is frequent. Moreover, small vessels can be affected by various disease entities with overlapping radiologic features and a wide spectrum of clinical manifestations. Nevertheless, these various entities can be easily separated into two different groups by imaging techniques, particularly by computed tomography: obstructive and inflammatory diseases. Radiologic findings of obstructive diseases are relatively constant, dominated by the manifestation of pulmonary hypertension. In contrast, radiologic manifestations of inflammatory diseases are often florid and nonspecific. After a recall of the classification of small vessel diseases and the imaging techniques, we show the computed tomography features of the principal diseases involving the small pulmonary vessels by classifying them in these two principal groups.