I received my Ph.D. in Biomedical Engineering from Rutgers, The State University of New Jersey under the supervision of Dr. Anant Madabhushi in 2014 where I developed novel algorithms for prostate cancer analysis and registration of prostate MRI and transrectal ultrasound. I am currently a post doctoral research associate at CMIC where I am developing algorithms for real time surgical planning and guidance for the Epilepsy-Navigator (EpiNavTM) software platform.
I am currently leading the development of the Epilepsy-Navigator (EpiNavTM) software platform. EpiNavTM provides tools for pre-operative analysis and interactive surgical planning in the treatment of epileptic patients. Pre-operative image analysis tools allow users to perform image registration (NiftyReg), brain parcellation (NiftySeg), and vessel extraction to allow for mutli-modal visualization and assessment of brain anatomy. Computer-assisted planning of multiple stereo-electroencephalography (SEEG) electrodes simultaneously enables efficient planning of intracranial electrode implantations. Planning SEEG implantations involves optimizing several criteria simultaneously including: (1) each electrode must sample a target area within the cortex, (2) each electrode must avoid hitting critical structures (e.g. blood vessels, cerebrospinal tracts), and (3) electrodes must not interfere with each other. By optimizing for all electrodes and criteria simultaneously a SEEG implantation plan can be automatically created, and then refined by the neurosurgeon. The benefits are reduced planning time for the neurosurgeon, reduced complications for the patient, and an increased success rate for SEEG implantations.
 R. Sparks, G. Zombori, R. Rodionov, M. Nowell, S. N. Vos, M. A. Zuluaga, B. Diehl, T. Wehner, A. Miserocchi, A. W. McEvoy, J. S. Duncan, S. Ourselin. Automated Multiple Trajectory Planning Algorithm for the Placement of Stero-Electroencephalography (SEEG) electrodes in epilpesy treatment. International Journal Of Computer Assisted Radiology and Surgery 12(1):123-136, 2017. doi:10.1007/s11548-016-1452-x.
 V. N. Vakharia, R. Sparks, A. G. O’Keeffe, R. Rodionov, A. Miserocchi, A. W. McEvoy, S. Ourselin, J. S. Duncan. Accuracy of intracranial electrode placement for stereoencephalography: A systematic review and meta-analysis. Epilepsia, in press. doi: 10.1111/epi.13713.
 R. Sparks, G. Zombori, R. Rodionov, M. A. Zuluaga, B. Diehl, T. Wehner, A. Miserocchi, A. W. McEvoy, J. S. Duncan, S. Ourselin. Efficient Anatomy Driven Automated Multiple Trajectory Planning for Intracranial Electrode Implantation. In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI): 544-550, 2016. doi:10.1007/978-3-319-46720-7_63
 R. Sparks and A. Madabhushi. Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images. Scientific Reporting 6:27306, 2016. doi: 10.1038/srep27306.
 M. Nowell, R. Sparks, G. Zombori, A. Miserocchi, R. Rodionov, B. Diehl, T. Wehner, G. Baio, G. Trevisi, M. Tisdall, S. Ourselin, A. W. McEvoy, J. S. Duncan. Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy. Journal of Neurosurgery 124(6): 1820-1828, 2016. doi: 10.3171/2015.6.JNS15487
 M. Nowell, R. Rodionov, G. Zombri, R. Sparks, M. Rizzi, S. Ourselin, A. Miserocchi, A. McEvoy, J. Duncan. A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery. Journal of Visualized Experiments 111:53450, 2016. doi:10.3791/53450
 M. Nowell, R. Sparks, G. Zombori, A. Miserocchi, R. Rodionov, B. Diehl, T. Wehner, M. White, S. Ourselin, A. W. McEvoy, J. S. Duncan.Resection planning in extratemporal epilepsy surgery using 3D multimodality imaging and intraoperative MRI. British Journal of Neurosurgery, in press 2016.
 A. Singanamalli, M. Rusu, R. Sparks, N. N. C. Shih, A. Ziober, L. P. Wang, J. Tomaszewski, M. Rosen, M. Feldman, A. Madabhushi. Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer. Journal of Magnetic Resonance Imaging 43(1): 149-158, 2016.
 R. Sparks, N. B. Bloch, E. Feleppa, D. Barratt, D. Moses, L. Ponsky, A Madabhushi. Multiattribute probabilistic prostate elastic registration (MAPPER): Application to fusion of ultrasound and magnetic resonance imaging. Medical Physics, 42, 1153-1163, 2015 2015. doi:10.1118/1.4905104.
 M. Nowell, R. Rodionov, G. Zombori, R. Sparks, G. Winston, J. Kinghorn, B. Diehl, T. Wehner, A. Miserocchi, A. W. McEvoy, S. Ourselin, J. S. Duncan. Utility of 3D multimodality imaging in the implantation of intracranial electrodes in epilepsy. Epilepsia 56(3): 403-413, 2015. doi: 10.1111/epi.12924.
 G. Lee, R. Sparks, S. Ali, N. N. C. Shih, M. D. Feldman, E. Spangler, T. Rebbeck, J. E. Tomaszewski, A. Madabhushi. Co-Occuring Gland Angularity in Localized Subgraphs: Predicting Biochemical Recurrence in Intermediate-Risk Prostate Cancer Patients. PLoS ONE 2014, 9(5): e97954. doi: 10.1371/journal.pone.0097954.
 A. McClintic, J. B. Garcia, M. Gofeld, M. Kliot, J. C. Kucewicz, J. D. Loeser, K . D. Pederson, R. Sparks, G. W. Terman, R. E. Tych, P. D. Mourad. Intense focused ultrasound stimulation can safely stimulate inflamed subcutaneous tissue and assess allodynia. Journal of Therapeutic Ultrasound 2014, 2:8. doi:10.1186/2050-5736-2-8.
 M. Orooji, R. Sparks, B. N. Bloch, E. Feleppa, D. Barratt, A. Madabhushi. Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation. In Proc of SPIE 9034, pp90343Y, 2014. doi: 10.1117/12.2043981.
 E. Hwuang, M. Rusu, S. Karthigeyan, S. C. Agner, R. Sparks, N. Shih, J. E. Tomaszewski, M. A. Rosen M.D., M. D. Feldman, A. Madabhushi, "Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI", in Proc SPIE 9034, pp90343P, 2014. doi: 10.1117/12.2044317.
 R. Sparks and A. Madabhushi. Explicit shape descriptors: novel morphologic features for histopathology classification. Medical Image Analysis 2013, 17(8):997-1009. doi:10.1016/j.media.2013.06.002.
 R. Sparks and A. Madabhushi. Statistical Shape Model for Manifold Regularization: Gleason grading of prostate histology. Computer Vision & Image Understanding 2013, 117(9):1138-1146. . doi: 10.1016/j.cviu.2012.11.011.
 R. Sparks, B.N. Bloch, E. Feleppa, D. Barratt, A. Madabhushi. Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric. In Proc SPIE 8671, pp86710A, 2013. doi:10.1117/12.2007610.
 A. Singanamalli, R. Sparks, M. Rusu, N. Shih, A. Ziober, J. E. Tomaszewski, M. Rosen, M. D. Feldman, A. Madabhushi, "Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach", in Proc of SPIE Vol. 8676, pp 867604, 2013. doi: 10.1117/12.2008136.
 E. Hwang, S. Danish, M. Rusu, R. Sparks, R. Toth, and A. Madabhushi. Anisotropic Smoothing Regularization (AnSR) in Thirion’s Demons Registration Evaluated Brain Tissue Changes Post-Laser Ablation. IEEE Engineering in Medicine and Biology Conference (EMBC) 2013. doi: 10.1109/EMBC.2013.6610423.
 G. Lee, R. Sparks, S. Ali, M. D. Feldman, N. Shih, J. E. Tomaszeweski, and A. Madabhushi. Co-Occurring Gland Tensors in Localized Cluster Graphs: Quantitative Histomorphometry for Predicting Biochemical Recurrence for Intermediate Grade Prostate Cancer. IEEE International Society of Biomedical Imaging (ISBI) 2013, 113-116. doi: 10.1109/ISBI.2013.6556425.
 R. Sparks and A. Madabhushi. Out-of-Sample Extrapolation for Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval of Prostate Histology. IEEE International Society of Biomedical Imaging (ISBI) 2011, pp 734-738. doi: 10.1109/ISBI.2011.5872510.
 R. Sparks and A. Madabhushi. Content-based image retrieval utilizing Explicit Shape Descriptors: applications to breast MRI and prostate histopathology. In Proc. SPIE 2011, Vol. 7692 pp. 79621I-79621I-13. doi:10.1117/12.878428.
 R. Toth, R. Sparks and A. Madabhushi. Creating a Medial Axis Based Statistical Shape Model (MASSM) and Its Applications to Prostate Segmentation. IEEE International Society of Biomedical Imaging (ISBI) 2011, pp 1463-1467. doi: 10.1109/ISBI.2011.5872676.
 L. Tchikindas, R. Sparks, J. Baccon, D. Ellison, A. R. Judkins, A. Madabhushi. Segmentation of nodular medulloblastoma using Random Walker and Hierarchical Normalized Cuts. IEEE Annual Northeast Bioenginering Conference (NEBEC) 2011, 1-2. doi: 10.1109/NEBC.2011.5778640.
 R. Sparks and A. Madabhushi. Computerized Classification of Benign and Malignant Breast Lesions on DCE-MRI Utilizing Novel Shape Descriptors. ISMRM 2011.
 R. Sparks and A. Madabhushi. Quantifying Gland Morphology for Computerized Prostate Cancer Detection and Gleason Grading. USCAP 2011.
 R. Sparks and A. Madabhushi. Novel Morphometric Based Classific1+6+ation via Diffeomorphic Based Shape Representation using Manifold Learning. In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI) 2010 Part III, 659-666. doi: 10.1007/978-3-642-15711-0_82.
 R. Sparks, R. Toth, J. Chappelow, G. Xiao, and A. Madabhushi. An Integrated Framework for Analyzing Three-Dimensional Shape Differences: Evaluating Prostate Morphometry. In Proc. of IEEE International Society of Biomedical Imaging (ISBI) 2010, 1081-1084. doi: 10.1109/ISBI.2010.5490180.
 J. Xu, R. Sparks, A. Janowcyzk, J.E. Tomaszewski, M.D. Feldman, and A. Madabhushi. High-throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Needle Core Biopsies. in Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI) 2010, LNCS 6367, pp. 77-88. doi: 10.1007/978-3-642-15989-3_10.