Publications

Klyuzhin, I. S., Cheng, J-C., Bevington, C., Sossi, V. (2019). Use of a Tracer-specific Deep Artificial Neural Net to Denoise Dynamic PET Images. IEEE Transactions on Medical Imaging, in press.
Keywords: Dynamic PET, Denoising, Neural network, Machine learning, Deep learning, Kinetic modeling.

Salmanpour, M. R., Shamsaei, M., Saberi, A., Setayeshi, S., Klyuzhin, I. S., Sossi, V., Rahmim. A. (2019). Optimized Machine Learning Methods for Prediction of Cognitive Outcome in Parkinson’s Disease. Computers in Biology and Medicine, in press.

Sossi, V., Cheng, J-C., Klyuzhin, I. S. (2019). Imaging in Neurodegeneration: Movement Disorders. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(3), 262-274. https://doi.org/10.1109/TRPMS.2018.2871760
Keywords: Brain function, Data quantification, Denoising, Hybrid positron emission tomography (PET), Magnetic resonance imaging (MRI), Image feature and connectivity analysis, Molecular imaging, Movement disorders, Neurodegeneration.

Fu, J. F., Klyuzhin, I. S., McKenzie, J., Neilson, N., Shahinfard, E., Dinelle, K., McKeown, M.J., Stoessl, A.J., Sossi, V. (2019). Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson’s disease induced presynaptic alterations. NeuroImage: Clinical, 23, 101856. https://doi.org/10.1016/j.nicl.2019.101856
Keywords: Data fusion, Dopamine, Parkinson’s disease, Pattern analysis, Positron emission tomography.

Tang, J., Yang, B., Shenkov, N. N., Klyuzhin, I. S., Fotouhi, S., Davoodi-Bojd, E., Lu, L., Soltanian-Zadeh, H., Sossi, V., Rahmim, A. (2019). Artificial neural network based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features. Molecular Imaging and Biology, in press.
Keywords: Machine learning, Predictive models, Artificial neural network, Feature selection, SPECT, Medical imaging, Radiomics, Parkinson, Disease.

Fu, J. F., Klyuzhin, I., Liu, S., Shahinfard, E., Vafai, N., McKenzie, J., … Sossi, V. (2018). Investigation of serotonergic Parkinson’s disease-related covariance pattern using [11C]-DASB/PET. NeuroImage: Clinical, 19, 652–660. https://doi.org/10.1016/j.nicl.2018.05.022
Keywords: Pattern recognition, Data mining, Covariance analysis, Principal component analysis, Machine learning, Positron emission tomography, Neuroscience, Serotonergic system, Parkinson, Disease.

Klyuzhin, I. S., Fu, J. F., Hong, A., Sacheli, M., Shenkov, N., Matarazzo, M., … Sossi, V. (2018). Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration. PloS One, 13(11), e0206607. https://doi.org/10.1371/journal.pone.0206607
Keywords: Principal component analysis, Regularized regression, Sparse representation, Dimensionality reduction, Pattern extraction, Covariance analysis, Machine learning, Brain imaging, Image analysis.

Klyuzhin, I. S., Fu, J. F., Shenkov, N., Rahmim, A., & Sossi, V. (2018). Use of Generative Disease Models for Analysis and Selection of Radiomic Features in PET. IEEE Transactions on Radiation and Plasma Medical Sciences, 1–1. https://doi.org/10.1109/TRPMS.2018.2844171
Keywords: Machine learning, Computational modeling, Image analysis, Feature selection, Biomedical imaging, Radiomics, Generative models, Positron emission tomography, Parkinson, Disease.

Klyuzhin, I. S., & Sossi, V. (2017). PET Image Reconstruction and Deformable Motion Correction Using Unorganized Point Clouds. IEEE Transactions on Medical Imaging, 36(6), 1263–1275. https://doi.org/10.1109/TMI.2017.2675989
Keywords: Image reconstruction, Deformation modeling, Point clouds, Meshfree methods, Algorithm design, Maximum likelihood, Expectation maximization, Motion correction, Positron emission tomography.

Klyuzhin, I. S., Gonzalez, M., Shahinfard, E., Vafai, N., & Sossi, V. (2016). Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease. Journal of Cerebral Blood Flow and Metabolism, 36(6), 1122–1134. https://doi.org/10.1177/0271678X15606718
Keywords: Image analysis, Feature selection, Data mining, Statistical analysis, Image fusion, Multimodality imaging, Region of interest definition, Positron emission tomography, MRI, Brain, Parkinson, Disease.

Rahmim, A., Salimpour, Y., Jain, S., Blinder, S. A. L., Klyuzhin, I. S., Smith, G. S., … Sossi, V. (2016). Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments. NeuroImage: Clinical, 12, e1–e9. https://doi.org/10.1016/j.nicl.2016.02.012
Keywords: Image analysis, Textural features, Heterogeneity analysis, Feature selection, Regression analysis, Disease progression, SPECT, Parkinson, Disease.

O’Rourke, C., Klyuzhin, I., Park, J. S., & Pollack, G. H. (2011). Unexpected water flow through Nafion-tube punctures. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 83(5). https://doi.org/10.1103/PhysRevE.83.056305
Keywords: Optical microscopy, Hydrodynamics, Flow characterization, Microfluidics, Bioengineering, Statistical analysis, Polymer chemistry.

Klyuzhin, I. S., Ienna, F., Roeder, B., Wexler, A., & Pollack, G. H. (2010). Persisting water droplets on water surfaces. Journal of Physical Chemistry B, 114(44), 14020–14027. https://doi.org/10.1021/jp106899k
Keywords: Instrumentation design, Optical imaging, Physics of interfaces, Liquid non-coalescence, Hydrodynamics, Interfacial water, Hydroplanning

Klyuzhin, I., Symonds, A., Magula, J., & Pollack, G. H. (2008). New method of water purification based on the particle-exclusion phenomenon. Environmental Science and Technology, 42(16), 6160–6166. https://doi.org/10.1021/es703159q
Keywords: Water purification, Solution characterization, Optical microscopy, Confocal microscopy, Bioengineering, UV-VIS Spectroscopy, Chemoanalysis, Particulate matter.

Shklyar, T. F., Safronov, A. P., Klyuzhin, I. S., Pollack, G., & Blyakhman, F. A. (2008). A correlation between mechanical and electrical properties of the synthetic hydrogel chosen as an experimental model of cytoskeleton. Biophysics, 53(6), 544–549. https://doi.org/10.1134/S0006350908060146
Keywords: Bioengineering, Biophysics, Modeling, Electrochemical potential, Phase transition, Cytoskeleton, Polymer gel.