Quantitative characterization of liver tumor radiodensity in CT images: a phantom study between two scanners

27 February 2018 Quantitative characterization of liver tumor radiodensity in CT images: a phantom study between two scanners

Benjamin Paul Berman, Qin Li, Sarah McKenney, Stanley Thomas Fricke, Yuan Fang, Marios A. Gavrielides, Nicholas Petrick

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Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105753H (2018) https://doi.org/10.1117/12.2293190
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Benjamin Paul Berman, Qin Li, Sarah McKenney, Stanley Thomas Fricke, Yuan Fang, Marios A. Gavrielides, and Nicholas Petrick "Quantitative characterization of liver tumor radiodensity in CT images: a phantom study between two scanners", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753H (27 February 2018); https://doi.org/10.1117/12.2293190

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KEYWORDS

Tumors

Scanners

Computed tomography

Liver

Imaging systems

3D printing

Diagnostics

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