Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: pilot study

• Published in: Medical physics (Lancaster) Vol. 31; no. 10; p. 2912
• Main Authors: Li, Ping, Napel, Sandy, Acar, Burak, Paik, David S, Jeffrey, Jr, R Brooke, Beaulieu, Christopher F
• Format: Journal Article
• Language: English
• Published: United States 01.10.2004
• Subjects: Algorithms; Artificial Intelligence; Cluster Analysis; Colonic Polyps - diagnostic imaging; Colonography, Computed Tomographic - methods; Female; Humans; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Male; Middle Aged; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; Pilot Projects; Posture; Prone Position; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Supine Position
• ISSN: 0094-2405
• DOI: 10.1118/1.1796171

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.