Registration of Prone and Supine CT Colonography Scans Using Correlation Optimized Warping and Canonical Correlation Analysis

Overview

Journal Med Phys

Specialty Biophysics

Date 2010 Jan 26

PMID 20095272

Citations 6

Authors

ShiJun Wang

Jianhua Yao

Jiamin Liu

Nicholas Petrick

Robert L Van Uitert

Senthil Periaswamy

Ronald M Summers

Affiliations

Soon will be listed here.

Abstract

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice-Once supine and once prone-to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans.

Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation.

Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27 +/- 52.97 to 14.98 mm +/- 11.41 mm, compared to the normalized distance along the colon centerline algorithm (p < 0.01).

Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.

Citing Articles

Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling.

Nadeem S, Marino J, Gu X, Kaufman A IEEE Trans Vis Comput Graph. 2016; 23(1):751-760.

PMID: 27875189 PMC: 7812443. DOI: 10.1109/TVCG.2016.2598791.

Endoluminal surface registration for CT colonography using haustral fold matching.

Hampshire T, Roth H, Helbren E, Plumb A, Boone D, Slabaugh G Med Image Anal. 2013; 17(8):946-58.

PMID: 23845949 PMC: 3807796. DOI: 10.1016/j.media.2013.04.006.

Haustral fold segmentation with curvature-guided level set evolution.

Zhu H, Barish M, Pickhardt P, Liang Z IEEE Trans Biomed Eng. 2012; 60(2):321-31.

PMID: 23193228 PMC: 3552127. DOI: 10.1109/TBME.2012.2226242.

Predicting polyp location on optical colonoscopy from CT colonography by minimal-energy curve modeling of the colonoscope path.

Liu J, Chang K, Yao J, Summers R IEEE Trans Biomed Eng. 2012; 59(12):3531-40.

PMID: 23033425 PMC: 4217649. DOI: 10.1109/TBME.2012.2217960.

Matching 3-D prone and supine CT colonography scans using graphs.

Wang S, Petrick N, Van Uitert R, Periaswamy S, Wei Z, Summers R IEEE Trans Inf Technol Biomed. 2012; 16(4):676-82.

PMID: 22552585 PMC: 3498489. DOI: 10.1109/TITB.2012.2194297.

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