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Nonlinear regression‐based method for pseudoenhancement correction in CT colonography
Author(s) -
Tsagaan Baigalmaa,
Näppi Janne,
Yoshida Hiroyuki
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3147201
Subject(s) - imaging phantom , nuclear medicine , computed tomography , biomedical engineering , soft tissue , partial volume , computer science , medicine , radiology
In CT colonography (CTC), orally administered positive‐contrast tagging agents are often used for differentiating residual bowel contents from native colonic structures. However, tagged materials can sometimes hyperattenuate observed CT numbers of their adjacent untagged materials. Such pseudoenhancement complicates the differentiation of colonic soft‐tissue structures from tagged materials, because pseudoenhanced colonic structures may have CT numbers that are similar to those of tagged materials. The authors developed a nonlinear regression‐based (NLRB) method for performing a local image‐based pseudoenhancement correction of CTC data. To calibrate the correction parameters, the CT data of an anthropomorphic reference phantom were correlated with those of partially tagged phantoms. The CTC data were registered spatially by use of an adaptive multiresolution method, and untagged and tagged partial‐volume soft‐tissue surfaces were correlated by use of a virtual tagging scheme. The NLRB method was then optimized to minimize the difference in the CT numbers of soft‐tissue regions between the untagged and tagged phantom CTC data by use of the Nelder‐Mead downhill simplex method. To validate the method, the CT numbers of untagged regions were compared with those of registered pseudoenhanced phantom regions before and after the correction. The CT numbers were significantly different before performing the correction( p < 0.01 ) , whereas, after the correction, the difference between the CT numbers was not significant. The effect of the correction was also tested on the size measurement of polyps that were covered by tagging in phantoms and in clinical cases. In phantom cases, before the correction, the diameters of 12 simulated polyps submerged in tagged fluids that were measured in a soft‐tissue CT display were significantly different from those measured in an untagged phantom( p < 0.01 ) , whereas after the correction the difference was not significant. In clinical cases, before the correction, the diameters of 29 colonoscopy‐confirmed 3–14 mm polyps affected by tagging that were measured in a soft‐tissue CT display were significantly different from those measured in a lung CT display( p < 0.0001 )or in colonoscopy( p < 0.05 ) , whereas after the correction the difference was not significant. Finally, the effect of the correction was tested on automated detection of 25 polyps ≥ 6 mm affected by tagging in 56 clinical CTC cases. The application of the correction increased the detection accuracy from 60% with 5.0 FP detections per patient without correction to 96% with 2.9 FP detections with correction. This improvement in detection accuracy was statistically significant( p < 0.05 ) . The results indicate that the proposed NLRB method can yield an accurate pseudoenhancement correction with potentially significant benefits in clinical CTC examinations.