title: Computer-aided detection of colonic polyps using low-dose CT acquisitions.
abstract: 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.
authors.affiliation: 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
authors.forename: Gabriel
authors.initials: G
authors.lastname: Kiss
authors.forename: Stylianos
authors.initials: S
authors.lastname: Drisis
authors.forename: Didier
authors.initials: D
authors.lastname: Bielen
authors.forename: Frederik
authors.initials: F
authors.lastname: Maes
authors.forename: Johan
authors.initials: J
authors.lastname: Van Cleynenbreugel
authors.forename: Guy
authors.initials: G
authors.lastname: Marchal
authors.forename: Paul
authors.initials: P
authors.lastname: Suetens
mesh_terms: 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*