Publications

  • Klyuzhin, I. S., Bevington, C., Cheng, J-C., Sossi, V. (2020). Detection of transient neurotransmitter response using personalized neural networks. Physics in Medicine and Biology, early access
    https://doi.org/10.1088/1361-6560/abc230

  • Cheng, J.-C., Bevington, C., Rahmim, A., Klyuzhin, I., Matthews, J., Boellaard, R., Sossi, V. (2020). Dynamic PET image reconstruction utilizing intrinsic data-driven 4D de-noising kernel. Medical Physics, in press.

  • Bevington, C., Cheng, J.-C., Klyuzhin, I., Cherkasova, M., Winstanley, C., Sossi, V. (2020). A Monte Carlo approach for improving transient dopamine release detection sensitivity. Journal of Cerebral Blood Flow and Metabolism, early access.
    https://doi.org/10.1177/0271678x20905613

  • Fu, J. F., Klyuzhin, I. S., McKeown, M. J., Stoessl, A. J., Sossi, V. (2020) Novel data-driven, equation-free method captures spatio-temporal patterns of neurodegeneration in Parkinson’s disease: application of dynamic mode decomposition to PET. NeuroImage: Clinical, 25, 102150.
    https://doi.org/10.1016/j.nicl.2019.102150

  • Salmanpour, M. R., Shamsaei, M., Saberi, A., Klyuzhin, I. S., Tang, J., Sossi, V., Rahmim, A. (2020). Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease. Physica Medica, 69, 233-240.
    https://doi.org/10.1016/j.ejmp.2019.12.022

  • Klyuzhin, I. S., Cheng, J-C., Bevington, C., Sossi, V. (2020). Use of a Tracer-specific Deep Artificial Neural Net to Denoise Dynamic PET Images. IEEE Transactions on Medical Imaging, 39(2), 366-376.
    https://doi.org/10.1109/TMI.2019.2927199

  • 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, 111, 103347.
    https://doi.org/10.1016/j.compbiomed.2019.103347

  • 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

  • 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

  • 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, early access.
    https://doi.org/10.1007/s11307-019-01334-5

  • Klyuzhin, I. S., Fu, J. F., Shenkov, N., Rahmim, A., Sossi, V. (2019). Use of Generative Disease Models for Analysis and Selection of Radiomic Features in PET. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 178-191.
    https://doi.org/10.1109/TRPMS.2018.2844171

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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