Medical Community: Abstracts




Sarang Lakare, Dongqing Chen, Lihong Li, Arie Kaufman, Mark Wax, and
Zhengrong Liang

Purpose: Virtual colonoscopy aims to provide a safe and comfortable technique to screen the entire colon for detection of polyps (cancerous growth). An accurate diagnosis requires a clean, un-obstructed view of the colon lumen during thevirtual fly-through. One way to achieve a clean colon lumen is to perform physical bowel cleansing prior to the image scan. This method, although effective, is highly uncomfortable for the patient. Electronic cleansing provides an alternative solution by removing the residual material from the scanned dataset before the dataset is used for the virtual fly-through. This work aims at developing a fully automatic solution for electronic cleansing that is fast and reliable.

Prior to obtaining CT images for virtual colonoscopy, the patient undergoes bowel preparation of mild laxatives and a low residue diet.This also includes four 250 cc doses of 2.1% w/v barium sulfate
suspension taken with meals and two doses of 60 cc of gastroview (diatrizoate meglumine and diatrizoate sodium solution) taken the night prior to and the morning of the procedure. CT images are obtained after
the patient's colon is distended with CO2 (1 - 2 L) given through a rectal tube using standard virtual colonoscopy parameters and are reconstructed into a 3D dataset. The dataset is automatically classified into different regions using approximate thresholding. Special rays which are designed to detect intersection between distinct regions, known as segmentation rays, are then shot through the dataset. These rays detect intersections and mark the partial volume regions. In our case, the partial volume region between air and tagged residual fluid is undesirable. Hence, when a segmentation ray detects this type of partial volume region, we remove that region and mark it as air. The region between soft-tissue and tagged fluid is also a partial volume region. Because we need to remove the tagged fluid, we convert this region to a partial volume region between air and soft-tissue. The segmentation rays which detect a partial volume region between soft-tissue and tagged fluid, convert the fluid region into air region while maintaining the partial volume between the two. Finally, the remaining tagged fluid is converted to air. Results: We tested our fully automatic electronic cleansing algorithm on both volunteer and patient datasets. Figure 1(a) shows a CT slice and Figure 1(b) shows the same slice after applying our electronic cleansing algorithm. We also compare our results (Figure 2c) with thresholding (Figure 2a) and vector quantization (Figure 2b). The average time taken for electronic cleansing (for datasets with 300-400 slices of 512x512 resolution each) is 1 min on a Linux workstation (900MHz AMD).

New or breakthrough work: In our electronic cleansing approach, we use a new segmentation technique based on segmentation rays. These rays are specially designed to analyze the intensity profile as they traverse through the dataset. When this intensity profile matches any of the pre-defined profiles, the rays perform image reconstruction. We use these rays to detect the itersection between air and tagged fluid and between tagged fluid and soft-tissue. Since partial volume is always at the intersections, it is easily

Conclusion: We presented an electronic colon cleansing technology based on segmentation rays. The advantage of segmentation rays over other segmentation approaches is in the detection of partial volume regions. Segmentation rays can accurately detect partial volume regions and remove them if needed. Once partial volume is eliminated, removal of other unwanted regions (e.g., tagged fluid) is straight-forward (e.g., by using thresholding). This approach to electronic cleansing is extremely fast as it requires minimal computation.

electronic cleansing, segmentation rays, virtual colonoscopy, virtual endoscopy, massive polyp screening, early cancer detection, volumetric segmentation, interactive visualization.

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