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Proceedings Paper

Quantitative characterization of liver tumor radiodensity in CT images: a phantom study between two scanners
Author(s): Benjamin Paul Berman; Qin Li; Sarah McKenney; Stanley Thomas Fricke; Yuan Fang; Marios A. Gavrielides; Nicholas Petrick
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Paper Abstract

Quantitative assessment of tumor radiodensity is important for the clinical evaluation of contrast enhancement and treatment response, as well as for the extraction of texture-related features for image analysis or radiomics. Radiodensity estimation, Hounsfield Units (HU) in CT images, can be affected by patient factors such as tumor size, and by system factors such as acquisition and reconstruction protocols. In this project, we quantified the measurability of liver tumor HU using a 3D-printed phantom, imaged with two CT systems: Siemens Somatom Force and GE Lightspeed VCT. The phantom was printed by dithering two materials to create spherical tumors (10, 14 mm) with uniform densities (90, 95, 100, 105 HU). Image datasets were acquired at 120 kVp including 15 repeats using two matching exposures across the CT systems, and reconstructed using comparable algorithms. The radiodensity of each tumor was measured using an automated matched-filter method. We assessed the performance of each protocol using the area under the ROC curve (AUC) as the metric for distinguishing between tumors with different radiodensities. The AUC ranged from 0.8 to 1.0 and was affected by tumor size, radiodensity, and scanner; the lowest AUC values corresponded to low dose measurements of 10 mm tumors with less than 5 HU difference. The two scanners exhibited similar performance >0.9 AUC for large lesions with contrast above 7 HU, though differences were observed for the smallest and lowest contrast tumors. These results show that HU estimation should be carefully examined, considering that uncertainty in the tumor radiodensity may propagate to quantification of other characteristics, such as size and texture.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753H (27 February 2018); doi: 10.1117/12.2293190
Show Author Affiliations
Benjamin Paul Berman, U.S. Food and Drug Administration (United States)
Qin Li, U.S. Food and Drug Administration (United States)
Sarah McKenney, Children's National Medical Ctr. (United States)
Stanley Thomas Fricke, Children's National Medical Ctr. (United States)
Yuan Fang, U.S. Food and Drug Administration (United States)
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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