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TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT

Publication ,  Conference
Solomon, J; Nelson, R; Samei, E
Published in: Medical Physics
January 1, 2013

Purpose: Realistic three‐dimensional mathematical models of subtle lesions are essential for many CT studies focused on performance evaluation and optimization. The purpose of this work was to develop and apply a generic framework for creating such models informed by clinical data. Methods: A contrast profile equation was developed to describe the attenuation of a modeled lesion as a function of distance from the center of the lesion in a given direction. This equation prescribes the overall size, shape, contrast, and edge profile characteristics of the lesion in 3D. By adjusting the parameters of the contrast profile equation, the characteristics of a simulated lesion could be manipulated to emulate a realistic lesion. The simulated lesions could further be voxelized at any arbitrary resolution for comparison with real CT data. Using this framework, a trust‐region iterative minimization fitting algorithm was developed to generate a library of simulated lesions based on clinical CT data with known lesions. We first identified and segmented liver lesions, lung nodules, and renal stones from clinical cases. The fitting algorithm was then applied to the segmented pathologies. The results were evaluated for realism using an observer study and ROC analysis. Results: Based on CT image data from 22 patients; 10 liver lesion, 13 lung nodule, and 20 renal stone models were created. These models were found to have size, shape, contrast, and edge profile characteristics similar to those of real lesions. The observers could not distinguish between real and modeled lesions (AUC = 0.49). Conclusions: It is possible to create realistic 3D mathematical models of anthropomorphic lesions in CT images. These models could be instrumental in performance evaluation and optimization of CT systems. GE Healthcare, A‐56:Optimization of Protocols Employing Advanced Reconstruction Algorithms. © 2013, American Association of Physicists in Medicine. All rights reserved.

Duke Scholars

Published In

Medical Physics

DOI

ISSN

0094-2405

Publication Date

January 1, 2013

Volume

40

Issue

6

Start / End Page

436

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences
 

Citation

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Solomon, J., Nelson, R., & Samei, E. (2013). TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT. In Medical Physics (Vol. 40, p. 436). https://doi.org/10.1118/1.4815390
Solomon, J., R. Nelson, and E. Samei. “TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT.” In Medical Physics, 40:436, 2013. https://doi.org/10.1118/1.4815390.
Solomon J, Nelson R, Samei E. TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT. In: Medical Physics. 2013. p. 436.
Solomon, J., et al. “TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT.” Medical Physics, vol. 40, no. 6, 2013, p. 436. Scopus, doi:10.1118/1.4815390.
Solomon J, Nelson R, Samei E. TU‐C‐103‐01: A Framework for 3D Modeling of Anthropomorphic Lesions in CT. Medical Physics. 2013. p. 436.

Published In

Medical Physics

DOI

ISSN

0094-2405

Publication Date

January 1, 2013

Volume

40

Issue

6

Start / End Page

436

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences