Mr. Brian W. Thomsen Profile

Brian W. Thomsen

Clinical Research Scientist at GE Healthcare

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Publications (2)

Proceedings Article | 22 March 2010 Paper

Multi-material decomposition of spectral CT images

Paulo R. Mendonça, Rahul Bhotika, Mahnaz Maddah, Brian Thomsen, Sandeep Dutta, Paul Licato, Mukta Joshi

Proceedings Volume 7622, 76221W (2010) https://doi.org/10.1117/12.844531

KEYWORDS: Computed tomography, Signal attenuation, Tissues, Iodine, Chemical elements, Blood, Bone, Switching, X-ray computed tomography, X-rays

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Proceedings Article | 9 March 2010 Paper

Automated liver lesion characterization using fast kVp switching dual energy computed tomography imaging

Alberto Santamaria-Pang, Sandeep Dutta, Sokratis Makrogiannis, Amy Hara, William Pavlicek, Alvin Silva, Brian Thomsen, Scott Robertson, Darin Okerlund, David Langan, Rahul Bhotika

Proceedings Volume 7624, 76240V (2010) https://doi.org/10.1117/12.844059

KEYWORDS: Computed tomography, Dual energy imaging, Liver, Image classification, Switching, Signal attenuation, Tumors, Imaging spectroscopy, Medicine, X-ray computed tomography

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Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.

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