| Application of texture analysis to DAT SPECT imaging: relationship to clinical assessments A Rahmim, Y Salimpour, S Jain, SAL Blinder, IS Klyuzhin, GS Smith, ... NeuroImage: Clinical 12, e1-e9, 2016 | 70 | 2016 |
| Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease MR Salmanpour, M Shamsaei, A Saberi, S Setayeshi, IS Klyuzhin, ... Computers in biology and medicine 111, 103347, 2019 | 50 | 2019 |
| Radiomics in PET imaging: a practical guide for newcomers F Orlhac, C Nioche, I Klyuzhin, A Rahmim, I Buvat PET clinics 16 (4), 597-612, 2021 | 49 | 2021 |
| Artificial neural network–based prediction of outcome in Parkinson’s disease patients using DaTscan SPECT imaging features J Tang, B Yang, MP Adams, NN Shenkov, IS Klyuzhin, S Fotouhi, ... Molecular imaging and biology 21, 1165-1173, 2019 | 39 | 2019 |
| Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease MR Salmanpour, M Shamsaei, A Saberi, IS Klyuzhin, J Tang, V Sossi, ... Physica Medica 69, 233-240, 2020 | 38 | 2020 |
| Persisting water droplets on water surfaces IS Klyuzhin, F Ienna, B Roeder, A Wexler, GH Pollack The Journal of Physical Chemistry B 114 (44), 14020-14027, 2010 | 37 | 2010 |
| New method of water purification based on the particle-exclusion phenomenon I Klyuzhin, A Symonds, J Magula, GH Pollack Environmental science & technology 42 (16), 6160-6166, 2008 | 30 | 2008 |
| Using deep-learning to predict outcome of patients with Parkinson’s disease KH Leung, MR Salmanpour, A Saberi, IS Klyuzhin, V Sossi, AK Jha, ... 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference …, 2018 | 27 | 2018 |
| Use of a tracer-specific deep artificial neural net to denoise dynamic PET images IS Klyuzhin, JC Cheng, C Bevington, V Sossi IEEE transactions on medical imaging 39 (2), 366-376, 2019 | 26 | 2019 |
| A correlation between mechanical and electrical properties of the synthetic hydrogel chosen as an experimental model of cytoskeleton TF Shklyar, AP Safronov, IS Klyuzhin, G Pollack, FA Blyakhman Biophysics 53, 544-549, 2008 | 22 | 2008 |
| Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations JF Fu, I Klyuzhin, J McKenzie, N Neilson, E Shahinfard, K Dinelle, ... NeuroImage: Clinical 23, 101856, 2019 | 21 | 2019 |
| Investigation of serotonergic Parkinson's disease-related covariance pattern using [11C]-DASB/PET JF Fu, I Klyuzhin, S Liu, E Shahinfard, N Vafai, J McKenzie, N Neilson, ... NeuroImage: Clinical 19, 652-660, 2018 | 21 | 2018 |
| Dynamic PET image reconstruction utilizing intrinsic data‐driven HYPR4D denoising kernel JC Cheng, C Bevington, A Rahmim, I Klyuzhin, J Matthews, R Boellaard, ... Medical physics 48 (5), 2230-2244, 2021 | 18 | 2021 |
| Exploring the use of shape and texture descriptors of positron emission tomography tracer distribution in imaging studies of neurodegenerative disease IS Klyuzhin, M Gonzalez, E Shahinfard, N Vafai, V Sossi Journal of Cerebral Blood Flow & Metabolism 36 (6), 1122-1134, 2016 | 18 | 2016 |
| Testing the ability of convolutional neural networks to learn radiomic features IS Klyuzhin, Y Xu, A Ortiz, JL Ferres, G Hamarneh, A Rahmim Computer Methods and Programs in Biomedicine 219, 106750, 2022 | 16 | 2022 |
| Becoming good at ai for good M Kshirsagar, C Robinson, S Yang, S Gholami, I Klyuzhin, S Mukherjee, ... Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 664-673, 2021 | 16 | 2021 |
| Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration IS Klyuzhin, JF Fu, A Hong, M Sacheli, N Shenkov, M Matarazzo, ... PloS one 13 (11), e0206607, 2018 | 15 | 2018 |
| Use of generative disease models for analysis and selection of radiomic features in PET IS Klyuzhin, JF Fu, N Shenkov, A Rahmim, V Sossi IEEE Transactions on Radiation and Plasma Medical Sciences 3 (2), 178-191, 2018 | 15 | 2018 |
| Unexpected water flow through Nafion-tube punctures C O’Rourke, I Klyuzhin, JS Park, GH Pollack Physical Review E 83 (5), 056305, 2011 | 15 | 2011 |
| Machine learning methods for optimal prediction of outcome in Parkinson’s disease MR Salmanpour, M Shamsaei, A Saberi, S Setayeshi, E Taherinezhad, ... 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference …, 2018 | 12 | 2018 |
