Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions

• Published in: British journal of radiology Vol. 90; no. 1070; p. 20160733
• Main Authors: Chen, Lih-Shyang, Hsu, Ta-Wen, Chen, Shao-Jer, Chang, Shu-Han, Lin, Chih-Wen, Chen, Yu-Ruei, Hsieh, Chin-Chiang, Han, Shu-Chen, Chang, Ku-Yaw, Hou, Chun-Ju
• Format: Journal Article
• Language: English
• Published: England The British Institute of Radiology 01.02.2017
• Subjects: Adult; Aged; Aged, 80 and over; Colon - diagnostic imaging; Colonic Polyps - diagnostic imaging; Colonography, Computed Tomographic - methods; Diagnosis, Differential; Female; Gastrointestinal/Abdominal; Humans; Imaging, Three-Dimensional - methods; Intestinal Neoplasms - diagnostic imaging; Male; Middle Aged; Physics and Technology; Pilot Projects; Retrospective Studies; Sensitivity and Specificity; Technical/Instrumental
• ISSN: 0007-1285; 1748-880X
• DOI: 10.1259/bjr.20160733

In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions. There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The marching cube algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis. Clinical evaluation confirms GM is significantly superior to SR in terms of gray-level pattern and spatial shape presentation of endoluminal cases (p < 0.01) and improves the confidence of identification and clinical classification of endoluminal lesions significantly (p < 0.01). The specificity and diagnostic accuracy of GM is significantly better than those of SR in diagnostic performance evaluation (p < 0.01). GM can reduce confusion in three-dimensional CTC and well correlate CTC with sectional images by the location as well as gray-level value. Hence, GM increases identification and differentiation of endoluminal lesions, and facilitates diagnostic process. Advances in knowledge: GM significantly improves the traditional SR method by providing reliable gray-level information for the surface points and is helpful in identification and differentiation of endoluminal lesions according to their shape and density.