Dr. Rahul Bhotika Profile

Dr. Rahul Bhotika

Senior Scientist at GE Research

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Publications (8)

Proceedings Article | 20 March 2015 Paper

FIST: a fast interactive segmentation technique

Dirk Padfield, Rahul Bhotika, Alexander Natanzon

Proceedings Volume 9413, 941317 (2015) https://doi.org/10.1117/12.2082011

KEYWORDS: Image segmentation, Image processing algorithms and systems, Lung, Medical imaging, Computed tomography, Magnetic resonance imaging, 3D image processing, Image processing, 3D metrology, Brain

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Proceedings Article | 24 February 2012 Paper

Texture analysis using Minkowski functionals

Xiaoxing Li, Paulo R. Mendonça, Rahul Bhotika

Proceedings Volume 8314, 83144Y (2012) https://doi.org/10.1117/12.912033

KEYWORDS: Image segmentation, Lung, Medical imaging, Liver, Pathology, Computed tomography, Principal component analysis, 3D image processing, Emphysema, 3D metrology

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Proceedings Article | 31 March 2011 Paper

Liver fat quantification using fast kVp-switching dual energy CT

Andras Kriston, Paulo Mendonça, Alvin Silva, Robert Paden, William Pavlicek, Dushyant Sahani, Benedek Janos Kis, Laszlo Rusko, Darin Okerlund, Rahul Bhotika

Proceedings Volume 7962, 79623W (2011) https://doi.org/10.1117/12.878206

KEYWORDS: Liver, Tissues, Signal attenuation, Dual energy imaging, Monte Carlo methods, Biopsy, X-ray computed tomography, Imaging spectroscopy, Visualization, Medicine

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Proceedings Article | 12 March 2011 Paper

Initial evaluation of virtual un-enhanced imaging derived from fast kVp-switching dual energy contrast enhanced CT for the abdomen

M. Joshi, P. Mendonca, D. Okerlund, P. Lamb, N. Kulkarni, D. Pinho, D. Sahani, R. Bhotika

Proceedings Volume 7962, 79621G (2011) https://doi.org/10.1117/12.878565

KEYWORDS: Dual energy imaging, Computed tomography, Image quality, Abdomen, Tissues, Kidney, Fast packet switching, Liver, Data acquisition, X-ray computed tomography

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Proceedings Article | 23 March 2010 Paper

Effective atomic number accuracy for kidney stone characterization using spectral CT

M. Joshi, D. Langan, D. Sahani, A. Kambadakone, S. Aluri, K. Procknow, X. Wu, R. Bhotika, D. Okerlund, N. Kulkarni, D. Xu

Proceedings Volume 7622, 76223K (2010) https://doi.org/10.1117/12.844372

KEYWORDS: Renal calculi, Signal attenuation, Calcium, Dual energy imaging, Computed tomography, Kidney, X-ray computed tomography, Imaging spectroscopy, Spine, Medicine

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Proceedings Article | 22 March 2010 Paper

Initial use of fast switched dual energy CT for coronary artery disease

William Pavlicek, Prasad Panse, Amy Hara, Thomas Boltz, Robert Paden, Didem Yamak, Paul Licato, Naveen Chandra, Darin Okerlund, Sandeep Dutta, Rahul Bhotika, David Langan

Proceedings Volume 7622, 76221V (2010) https://doi.org/10.1117/12.844859

KEYWORDS: Dual energy imaging, Calcium, Iodine, Visualization, Arteries, Tissues, Heart, Signal attenuation, X-rays, X-ray imaging

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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.

Showing 5 of 8 publications

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