CT colonography: clinical evaluation of a method for automatic coregistration of polyps at follow-up surveillance studies.

PURPOSE To evaluate the accuracy of a method of automatic coregistration of the endoluminal surfaces at computed tomographic (CT) colonography performed on separate occasions to facilitate identification of polyps in patients undergoing polyp surveillance. MATERIALS AND METHODS Institutional review board and HIPAA approval were obtained. A registration algorithm that was designed to coregister the coordinates of endoluminal colonic surfaces on images from prone and supine CT colonographic acquisitions was used to match polyps in sequential studies in patients undergoing polyp surveillance. Initial and follow-up CT colonographic examinations in 26 patients (35 polyps) were selected and the algorithm was tested by means of two methods, the longitudinal method (polyp coordinates from the initial prone and supine acquisitions were used to identify the expected polyp location automatically at follow-up CT colonography) and the consistency method (polyp coordinates from the initial supine acquisition were used to identify polyp location on images from the initial prone acquisition, then on those for follow-up prone and follow-up supine acquisitions). Two observers measured the Euclidean distance between true and expected polyp locations, and mean per-patient registration accuracy was calculated. Segments with and without collapse were compared by using the Kruskal-Wallace test, and the relationship between registration error and temporal separation was investigated by using the Pearson correlation. RESULTS Coregistration was achieved for all 35 polyps by using both longitudinal and consistency methods. Mean ± standard deviation Euclidean registration error for the longitudinal method was 17.4 mm ± 12.1 and for the consistency method, 26.9 mm ± 20.8. There was no significant difference between these results and the registration error when prone and supine acquisitions in the same study were compared (16.9 mm ± 17.6; P = .451). CONCLUSION Automatic endoluminal coregistration by using an algorithm at initial CT colonography allowed prediction of endoluminal polyp location at subsequent CT colonography, thereby facilitating detection of known polyps in patients undergoing CT colonographic surveillance.

[1]  S Halligan,et al.  Quantitative assessment of colonic movement between prone and supine patient positions during CT colonography. , 2009, The British journal of radiology.

[2]  J. Wardle,et al.  Computed tomographic colonography versus barium enema for diagnosis of colorectal cancer or large polyps in symptomatic patients (SIGGAR): a multicentre randomised trial , 2013, The Lancet.

[3]  Emma Helbren,et al.  GASTROINTESTINAL IMAGING : Registration Algorithm for CT , 2015 .

[4]  R. Jeffrey,et al.  Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: pilot study. , 2004, Medical physics.

[5]  David H. Kim,et al.  CT colonography versus colonoscopy for the detection of advanced neoplasia. , 2007, The New England journal of medicine.

[6]  Perry J Pickhardt,et al.  Assessment of volumetric growth rates of small colorectal polyps with CT colonography: a longitudinal study of natural history. , 2013, The Lancet. Oncology.

[7]  Karen M Horton,et al.  Accuracy of CT colonography for detection of large adenomas and cancers. , 2008, The New England journal of medicine.

[8]  David J. Hawkes,et al.  Endoluminal surface registration for CT colonography using haustral fold matching☆ , 2013, Medical Image Anal..

[9]  R. Truyen,et al.  Feasibility of automated matching of supine and prone CT-colonography examinations. , 2006, The British journal of radiology.

[10]  J. Wardle,et al.  Computed tomographic colonography versus colonoscopy for investigation of patients with symptoms suggestive of colorectal cancer (SIGGAR): a multicentre randomised trial , 2013, The Lancet.

[11]  David H. Kim,et al.  Clinical management of small (6- to 9-mm) polyps detected at screening CT colonography: a cost-effectiveness analysis. , 2008, AJR. American journal of roentgenology.

[12]  Marc Modat,et al.  Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography. , 2011, Medical physics.

[13]  Ronald M Summers,et al.  Normalized distance along the colon centerline: a method for correlating polyp location on CT colonography and optical colonoscopy. , 2009, AJR. American journal of roentgenology.

[14]  P. Pickhardt,et al.  Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. , 2003, The New England journal of medicine.

[15]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[16]  M. Mascalchi,et al.  Computed tomographic colonography in subjects with positive faecal occult blood test refusing optical colonoscopy. , 2013, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.