Paper
22 March 2010 Differentiation of uric acid versus non-uric acid kidney stones in the presence of iodine using dual-energy CT
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Abstract
In this study, the feasibility of differentiating uric acid from non-uric acid kidney stones in the presence of iodinated contrast material was evaluated using dual-energy CT (DECT). Iodine subtraction was accomplished with a commercial three material decomposition algorithm to create a virtual non-contrast (VNC) image set. VNC images were then used to segment stone regions from tissue background. The DE ratio of each stone was calculated using the CT images acquired at two different energies with DECT using the stone map generated from the VNC images. The performance of DE ratio-based stone differentiation was evaluated at five different iodine concentrations (21, 42, 63, 84 and 105 mg/ml). The DE ratio of stones in iodine solution was found larger than those obtained in non-iodine cases. This is mainly caused by the partial volume effect around the boundary between the stone and iodine solution. The overestimation of the DE ratio leads to substantial overlap between different stone types. To address the partial volume effect, an expectation-maximization (EM) approach was implemented to estimate the contribution of iodine and stone within each image pixel in their mixture area. The DE ratio of each stone was corrected to maximally remove the influence of iodine solutions. The separation of uric-acid and non-uric-acid stone was improved in the presence of iodine solution.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Wang, M. Qu, S. Leng, and C. H. McCollough "Differentiation of uric acid versus non-uric acid kidney stones in the presence of iodine using dual-energy CT", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76223O (22 March 2010); https://doi.org/10.1117/12.845046
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Cited by 3 scholarly publications.
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KEYWORDS
Iodine

Renal calculi

Image segmentation

Expectation maximization algorithms

Computed tomography

Tissues

Photovoltaics

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