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Proceedings Paper

Performance evaluation of multi-material electronic cleansing for ultra-low-dose dual-energy CT colonography
Author(s): Rie Tachibana; Naja Kohlhase; Janne J. Näppi; Toru Hironaka; Junko Ota; Takayuki Ishida; Daniele Regge; Hiroyuki Yoshida
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Paper Abstract

Accurate electronic cleansing (EC) for CT colonography (CTC) enables the visualization of the entire colonic surface without residual materials. In this study, we evaluated the accuracy of a novel multi-material electronic cleansing (MUMA-EC) scheme for non-cathartic ultra-low-dose dual-energy CTC (DE-CTC). The MUMA-EC performs a wateriodine material decomposition of the DE-CTC images and calculates virtual monochromatic images at multiple energies, after which a random forest classifier is used to label the images into the regions of lumen air, soft tissue, fecal tagging, and two types of partial-volume boundaries based on image-based features. After the labeling, materials other than soft tissue are subtracted from the CTC images. For pilot evaluation, 384 volumes of interest (VOIs), which represented sources of subtraction artifacts observed in current EC schemes, were sampled from 32 ultra-low-dose DE-CTC scans. The voxels in the VOIs were labeled manually to serve as a reference standard. The metric for EC accuracy was the mean overlap ratio between the labels of the reference standard and the labels generated by the MUMA-EC, a dualenergy EC (DE-EC), and a single-energy EC (SE-EC) scheme. Statistically significant differences were observed between the performance of the MUMA/DE-EC and the SE-EC methods (p<0.001). Visual assessment confirmed that the MUMA-EC generated less subtraction artifacts than did DE-EC and SE-EC. Our MUMA-EC scheme yielded superior performance over conventional SE-EC scheme in identifying and minimizing subtraction artifacts on noncathartic ultra-low-dose DE-CTC images.

Paper Details

Date Published: 24 March 2016
PDF: 8 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978526 (24 March 2016); doi: 10.1117/12.2217140
Show Author Affiliations
Rie Tachibana, Institute of National Colleges of Technology (Japan)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Naja Kohlhase, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Janne J. Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Toru Hironaka, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Junko Ota, Osaka Univ. (Japan)
Takayuki Ishida, Osaka Univ. (Japan)
Daniele Regge, Institute for Cancer Research and Treatment (Italy)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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