A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography
CT Colonography (CTC) has emerged as a mainstream clinical practice of colonic cancer screening and diagnosis. One of the most critical problems is to increase compliance with CTC examinations via minimal bowel preparation (i.e., weak faecal-tagging), which nevertheless causes much lower signal-noise-ratio than conventional preparation.
In this paper, we present a new algorithm pipeline of electronically cleansing tagging materials in CTC under reduced oral contrast dose. Our method has the following steps: 1, robust structure parsing to generate a list of volume regions of interest (ROIs) of tagging material (avoiding bone erosion); 2, effectively locating local tagging-air (AT) transitional surface regions; 3, a novel discriminative-generative algorithm to learn the higher-order image appearance model in AT using 3D Markov Random Fields (MRF); 4, accurate probability density function based voxel labeling corresponding to semantic classes. Validated on 26 weak faecal-tagging CTC cases from 3 medical sites, our method yields better visualization clarity and readability compared with the previous approach . The whole system computes efficiently (e.g., < 40 seconds for CT images of 512×512×1000 +).
KeywordsGaussian Mixture Model Compute Tomography Colonography Markov Random Fields Compute Tomography Intensity Algorithm Pipeline
Unable to display preview. Download preview PDF.
- 3.Liedenbaum, M., Denters, M., Zijta, F., van Ravesteijn, V., Bipat, S., Vos, F., Dekker, E., Stoker, J.: Reducing the oral contrast dose in CT colonography: evaluation of faecal tagging quality and patient acceptance. Clin. Radiol., 30–37 (2010)Google Scholar
- 4.Lu, L., Wolf, M., Liang, J., Dundar, M., Bi, J., Salganicoff, M.: A Two-level Approach Towards Semantic Colon Segmentation: Removing Extra-colonic Findings. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 1009–1016. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 8.Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)Google Scholar
- 9.Cover, T.M., Thomas, J.A.: Elements of information theory (1991)Google Scholar
- 11.Lu, L., et al.: Hierarchical learning for tubular structure parsing in medical imaging: A study on coronary arteries using 3D CT Angiography. In: ICCV (2009)Google Scholar