The early detection of lung cancer has been one of the outstanding challenges in radiographic imaging, the significance of which can be discerned only by considering the fact that lung cancer remains the leading cause of cancer death in the US, surpassing breast, prostate, colon, and cervical cancers combined. Prior research has shown that interference of the anatomical structure is the dominant factor in the low detection of early lung cancer in radiographic images. Computer aided detection (CAD) is an advanced image processing technique that can somewhat remedy that limitation by enabling a full evaluation of an image for the presence of cancer. However, the fundamental limitations of anatomical noise still persist.

A new imaging paradigm, correlation imaging (CI), is pursued at RAI Labs for improving the detection of subtle lung nodules. In CI, two or more digital images of the thorax are acquired within a short time interval from two slightly different posterior projections. The image data are then incorporated into an enhanced CAD algorithm in which nodules are detected by examining the geometrical correlation of the detected signals in multiple views. Angular information is used to minimize the limiting influence of anatomical noise by identifying and positively reinforcing the nodule signals, which remain relatively constant against a variation in the background structure. This approach does not promise to completely eliminate anatomical noise (as CT does), but aims to cost-effectively and dose-effectively reduce its influence with little or no increase in the patient dose. Using correlation of signals between multiple views to identify “true” signals, CAD is used at high sensitivity levels, lowering the detection thresholds, without an undesirable increase in the number of false positives. This hybrid approach of utilizing angular information in conjunction with digital acquisition and CAD addresses all three major obstacles to the detection of subtle lung nodules; the angular information reduces the effects of anatomical noise, the high signal-to-noise ratio of digital acquisition assures sufficient nodule contrast, and CAD incorporates a complete search. The figure above demonstrates a schematic of an acquisition system.

The images show the results of CI on a chest phantom. The highlighted areas are suspect lesions identified by CI, while circles show the location of true lesions

Original chest radiograph containing lung nodules.

Green circles indicate actual lesions, pink areas are computer-detected suspicious regions.