Selected Recent Publications

Traumatic Brain Injury:

  1. W. Zhao, C. Kuo, L. Wu, D. B. Camarillo, and S. Ji. Performance evaluation of a pre-computed brain response atlas in dummy head impacts. Ann. Biomed. Eng. 45(10): 2437-2450, 2017. doi: 10.1007/s10439-017-1888-3 PMID: 28710533
  2. C. Kuo, L. Wu, W. Zhao, M. Fanton, S. Ji, D.B. Camarillo. (2017) Propagation of Errors from Head Kinematic Measurements to Finite Element Tissue Responses. Biomech. Model. Mechanobiol. (in press)
  3. W. Zhao, Y. Cai, Z. Li, S. Ji. Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter. Biomech Model Mechanobiol. 16, 1709–1727, 2017. doi:10.1007/s10237-017-0915-5 PMID: 28500358
  4. W. Zhao, S. Ji. (2017) Brain strain uncertainty due to shape variation in and simplification of head angular velocity profiles. Biomech. Model. Mechanobiol. 16(2): 449–461. doi:10.1007/s10237-016-0829-7
  5. W.W. Lytton, J. Arle, G. Bobashev, S. Ji, T.L. Kalssen, V.Z. Marmarelis, J. Schwaber, M. Sherif, T. Sanger. (2017) Multiscale modeling in the clinic: Diseases of the brain and nervous system. Brain Informatics. (in press) doi:10.1007/s40708-017-0067-5
  6. Y. Feng, S. Qiu, X. Xia, S. Ji, CH. Lee. (2017) A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter. Journal of Biomechanics. 57:146-151. doi: 10.1016/j.jbiomech.2017.03.025.
  7. Y. Cai, S. Ji (2016) Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome. In: Crimi A, Menze B, Maier O, et al. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing AG 2016, Athens, Greece, pp 259–270
  8. Y. Feng, C.H. Lee, L. Sun, S. Ji., X. Zhao. (2016) Characterizing white matter tissue in large strain via asymmetric indentation and inverse finite element modeling. Journal of the Mechanical Behavior of Biomedical Materials. 65:490–501.
  9. W. Zhao, S. Ji. (2016) Real-time, whole-brain, temporally resolved pressure responses in translational head impact. J. R. Soc. Interface Focus (Invited contribution to a focused topical issue) PMID: 26855762
  10. W. Zhao, J. C. Ford, L. A. Flashman, T. W. McAllister, and S. Ji. White matter injury susceptibility via fiber strain evaluation using whole-brain tractography. J. Neurotrauma 2016;33: 1834–1847. doi:10.1089/neu.2015.4239 PMID: 26782139 (front cover)
  11. S. Ji, W. Zhao. A pre-computed brain response atlas for instantaneous strain estimation in contact sports. Annals of Biomedical Engineering. 43(8):1877–1895, 2015. DOI: 10.1007/s10439-014-1193-3 PMID: 25449149 (front cover)
  12. S. Ji, W. Zhao, J.C. Ford, J.G. Beckwith, R.P. Bolander, R.M. Greenwald, L.A. Flashman, K.D. Paulsen, T.W. McAllister. Group-wise evaluation and comparison of white matter fiber strain and maximum principal strain in sports-related concussion. Journal of Neurotrauma. 32(7), 441-454, 2015. doi/abs/10.1089/neu.2013.3268 PMID: 24735430 (front cover)
  13. S. Ji, W. Zhao, Z. Li, T.W. McAllister. Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation. Biomechanics and Modeling in Mechanobiology. 13(5), 1121–36, 2014. doi:10.1007/s10237-014-0562-z
  14. S. Ji, H. Ghadyani, R.P. Bolander, J.G. Beckwith, J.C. Ford, T.W. McAllister, L.A. Flashman, K.D. Paulsen, K. Ernstrom, S. Jain, R. Raman, L. Zhang and R.M. Greenwald. Parametric Comparisons of Intracranial Mechanical Responses from Three Validated Finite Element Models of the Human Head. Annals of Biomedical Engineering, 42(1): 11–24, 2014. doi:10.1007/s10439-013-0907-2

Surgical Image-guidance:

  1. S. Lollis, X. Fan, L. Evans, J.D. Olson, K.D. Paulsen, D.W. Roberts, S.K. Mirza, and S. Ji. (2017) Use of stereovision for intraoperative coregistration of a spinal surgical field: a human feasibility study. Operative Neurosurgery (in press) DOI:
  2. X. Fan, D.W. Roberts, T.J. Schaewe, S. Ji, L.H. Holton, D.A. Simon, K.D. Paulsen. Intraoperative image updating for brain shift following dural opening. J. Neurosurgery. 1–10 (2016). DOI: 10.3171/2016.6.JNS152953
  3. G.S. Kassab, G. An, E.A. Sander, M.I. Miga, J. Guccione, S. Ji, Y. Vodovotz. Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine. Annals of Biomedical Engineering. 2016 (Invited contribution to a white paper) DOI:10.1007/s10439-016-1596-4 PubMed PMID: 27015816
  4. S. Ji, X. Fan, J.D. Olson, L.T. Evans, K.D. Paulsen, D.W. Roberts, S.K. Mirza, S.S. Lollis. (2016) Patient Registration via Topologically Encoded Depth Projection Images in Spine Surgery. In T. Vrtovec, J. Yao, B. Glocker, T. Klinder, A. Frangi, G. Zheng, & S. Li (Eds.), Computational Methods and Clinical Applications for Spine Imaging, CSI 2015, LNCS 9402 (pp. 27–37). Springer International Publishing Switzerland.
  5. S. Ji, X. Fan, K.D. Paulsen, D.W. Roberts, S.K. Mirza, S.S. Lollis. (2015) Patient registration using intraoperative stereovision in image-guided open spinal surgery. IEEE Trans. Biomed. Eng. 62(9) 2177–2186. DOI: 10.1109/TBME.2015.2415731 PMID: 25826802
  6. S. Ji, X. Fan, K.D. Paulsen, D.W. Roberts, S.K. Mirza, S.S. Lollis. (2015) Intraoperative CT as a registration benchmark for intervertebral motion compensation in image-guided open spinal surgery. International Journal of Computer Assisted Radiology and Surgery (IJCARS). DOI: 10.1007/s11548-015-1255-5 PubMed PMID: 26194485
  7. X. Fan, D.W. Roberts, S. Ji, A. Hartov, K.D. Paulsen. (2015) “Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases”. J. of Neurosurgery. DOI: 10.3171/2014.12. JNS141321.
  8. S. Ji, X. Fan, A. Hartov, D.W. Roberts, K.D. Paulsen. Cortical Surface Shift Estimation Using Optical Flow Motion Tracking and Stereovision via Projection Image Registration, Medical Image Analysis, 18:1169–1183, 2014. DOI: 10.1016/
  9. X. Fan, S. Ji, A. Hartov, D.W. Roberts, K.D. Paulsen. “Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery”. Medical Physics. 2014 Oct;41(10):102302. doi: 10.1118/1.4894705.
  10. S. Ji, X. Fan, D.W. Roberts, K.D. Paulsen. Efficient stereo image geometrical reconstruction at arbitrary camera settings from a single calibration. Med Image Comput Comput Assist Interv. (MICCAI) 2014;17(Pt 1):440-7. PubMed PMID: 25333148
  11. Ji, S., Fan, X., Roberts, D. W., Hartov, A., Schaewe, T. J., Simon, D. A., & Paulsen, K. D. (2014). Brain Shift Compensation via Intraoperative Imaging and Data Assimilation. In C. Neu & G. Genin (Eds.), CRC Handbook of Imaging in Biological Mechanics (pp. 229–240). CRC Press and Taylor & Francis.
  12. Y. Chen, Z. Fan, S. Ji, J. Muenzer, H. An, W. Lin, Patient-specific biomechanical modeling of ventricular enlargement in hydrocephalus from longitudinal magnetic resonance imaging. K. Mori et al. (Eds.): MICCAI 2013, Part III, LNCS 8151, pp. 291–298, 2013.
  13. S. Ji, X. Fan, D.W. Roberts, A. Hartov, K.D. Paulsen, “Flow-based Correspondence Matching in Stereovision”, G. Wu et al. (Eds.): Machine Learning in Medical Imaging, LNCS 8184, pp. 106–113, 2013.
  14. Ji, S., Fan, X., Roberts, D. W., Hartov, A., Paulsen, K. D., Tracking Cortical Surface Deformation Using Stereovision. Mechanics of Biological Systems and Materials, Volume 5. The Society for Experimental Mechanics Series 2013, pp 169–176.