Computer-aided detection of colonic polyps using low-dose CT acquisitions.
OBJECTIVE
This report proposes an alternative method for the automatic detection of colonic polyps that is robust enough to be directly applicable on low-dose computed tomographic data.


METHODS
The polyp modeling process takes into account both the gray-level appearance of polyps (intensity profiles) and their geometry (extended Gaussian images). Spherical harmonic decompositions are used for comparison purposes, allowing fast estimation of the similarity between a candidate and a set of previously computed models. Starting from the original raw data (acquired at 55 mA), five patient data sets (prone and supine scans) are reconstructed at different dose levels (to 5 mA) by using different kernel filters, slice overlaps, and increments. Additionally, the efficacy of applying an edge-preserving smoothing filter before detection is assessed.


RESULTS
Although image quality decreases when decreasing acquisition milliamperes, all polyps greater than 6 mm are detected successfully, even at 15 mA. Although not important at high doses, smoothing improves detection results for ultra-low-dose (tube current<15 mA) data.


CONCLUSIONS
The advantage of low-dose scans is a significant decrease in effective dose from 4.93 to 1.61 mSv while retaining high detection values, particularly important when thinking of population screening.
Kiss
Gabriel
G
Department of Medical Image Computing (Radiology-ESAT/PSI), University Hospital Gasthuisberg K.U. Leuven, Herestraat 49, B-3000, Leuven, Belgium. gabriel.kiss@uz.kuleuven.ac.be
Drisis
Stylianos
S
Bielen
Didier
D
Maes
Frederik
F
Van Cleynenbreugel
Johan
J
Marchal
Guy
G
Suetens
Paul
P
D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011829:Radiation Dosage; D011835:Radiation Protection / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*