Biplane correlation imaging for lung nodule detection: Initial human subject results
In this paper, we present performance of biplane correlation imaging (BCI) on set of chest x-ray projections of human data. BCI significantly minimizes the number of false positives (FPs) when used in conjunction with computer aided detection (CAD) by eliminating non-correlated nodule candidates. Sixty-one low exposure posterior projections were acquired from more than 20 human subjects with small angular separations (0.32 degree) over a range of 20 degrees along the vertical axis. All patients were previously diagnosed for the presence of lung nodules based on computed tomography (CT) examination. Images were processed following two steps. First, all images were analyzed using our CAD routine for chest radiography. This process proceeded with a BCI processing in which the results of CAD on each single projection were examined in terms of their geometrical correlation with those found in the other 60 projections based on the predetermined shift of possible nodule locations in each projection. The suspect entities with a geometrical correlation that coincided with the known location of the lesions were selected as nodules; otherwise they were ignored. An expert radiologist with reference to the associated CT dataset determined the truth regarding nodule location and sizes, which were then used to determine if the found nodules are true positive or false positive. The preliminary results indicated that the best performance was obtained when the angular separation of the projection pair was greater than about 6.7 degrees. Within the range of optimum angular separation, the number of FPs per image was 0-1 without impacting the number of true positives (TPs), averaged around 92%. (Supported by grants from the NIH: R01-CA80490 and R01CA109074).
