Robert Rohling

Robert Rohling

Robert Rohling

Professor and Director ICICS

P.Eng., B.A.Sc. (UBC), M.Eng. (McGill), Ph.D. (Cambridge)

phone: (604) 822-2045
fax: (604) 822-2403
email: rohling@ece.ubc.ca
website:  Robotics and Control Laboratory
office: KAIS 3059

Research Interests

  • Biomedical Engineering
  • Medical Imaging
  • Medical Information Systems
  • Robotics

Current Research Work

  • In medical imaging, I am developing new acquisition techniques for ultrasound with the goal of improving diagnostics. Two current research directions in this area are 3D ultrasound and spatial compounding to improve the visualization of anatomy and pathology.
  • In medical information systems, I am working with industry to improve the timely dissemination of digital medical images and associated data to health care providers. In particular I am currently working on new methods for radiologists to navigate large image sets. This is in response to the growing size of studies produced by modern scanners.
  • Finally, I have an interest in the calibration of robotic systems and their application in surgery.

All of these topics are multidisciplinary and I hold a joint appointment with the Department of Electrical and Computer Engineering and the Department of Mechanical Engineering to support this research.

Selected Publications

  • H. Vaseli, Z. Liao, A.H. Abdi, H. Girgis, D. Behnami, C. Luong, F.T. Dezaki, N. Dhungel, R. Rohling, K. Gin, and P. Abolmaesumi, “Designing lightweight deep learning models for echocardiography view classification,” In Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10951, p. 109510F). International Society for Optics and Photonics, 2019.
  • L.R. Porto, R. Tang, A. Sawka, V. Lessoway, E.M.A. Anas, D. Behnami, P. Abolmaesumi, and R. Rohling, “Comparison of Patient Position and Midline Lumbar Neuraxial Access Via Statistical Model Registration to Ultrasound,” Ultrasound in medicine & biology45(1), pp.255-263, 2019.
  • S. Honigmann, Y.C. Zhu, R. Singla, P. Abolmaesumi, A. Chau, and R. Rohling, “EpiGuide 2D: visibility assessment of a novel multi-channel out-of-plane needle guide for 2D point of care ultrasound,” In Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10951, p. 109510K). International Society for Optics and Photonics, 2019.
  • M.H. Jafari, H. Girgis, N. Van Woudenberg, Z. Liao, R. Rohling, K. Gin, P. Abolmaesumi, and T. Tsang, “Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training,” International journal of computer assisted radiology and surgery, pp.1-11, 2019.
  • F. Deeba, M. Ma, M. Pesteie, J. Terry, D. Pugash, J.A. Hutcheon, C. Mayer, S. Salcudean, and R. Rohling, “Attenuation Coefficient Estimation of Normal Placentas,” Ultrasound in medicine & biology, 2019.
  • M. Pesteie, V. Lessoway, P. Abolmaesumi, and R.N. Rohling, “Automatic localization of the needle target for ultrasound-guided epidural injections,” IEEE transactions on medical imaging37(1), pp.81-92, 2018.
  • I. Peterlík, H. Courtecuisse, R. Rohling, P. Abolmaesumi, C. Nguan, S. Cotin, and S. Salcudean, “Fast elastic registration of soft tissues under large deformations,” Medical image analysis45, pp.24-40, 2018.
  • M. Ai, T. Salcudean, R. Rohling, P. Abolmaesumi, and S. Tang, “Transurethral illumination probe design for deep photoacoustic imaging of prostate,” In Photons Plus Ultrasound: Imaging and Sensing 2018 (Vol. 10494, p. 104940C). International Society for Optics and Photonics, 2018.
  • C.D. Gerardo, E. Cretu, and R. Rohling, “Fabrication and testing of polymer-based capacitive micromachined ultrasound transducers for medical imaging,” Microsystems & Nanoengineering4(1), p.19, 2018.
  • B. Zhuang, R. Rohling, and P. Abolmaesumi, “Accumulated angle factor-based beamforming to improve the visualization of spinal structures in ultrasound images,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control65(2), pp.210-222, 2018.
  • M. Pesteie, P. Abolmaesumi, and R. Rohling, “Deep neural maps,” arXiv preprint arXiv:1810.07291, 2018.
  • G. Samei, O. Goksel, J. Lobo, O. Mohareri, P. Black, R. Rohling, and S. Salcudean, “Real-time FEM-based registration of 3-D to 2.5-D transrectal ultrasound images,” IEEE transactions on medical imaging37(8), pp.1877-1886, 2018.
  • P. Edgcumbe, R. Singla, P. Pratt, C. Schneider, C. Nguan, and R. Rohling, “Follow the light: projector-based augmented reality intracorporeal system for laparoscopic surgery,” Journal of Medical Imaging5(2), p.021216, 2018.
  • M.H. Jafari, H. Girgis, Z. Liao, D. Behnami, A. Abdi, H. Vaseli, C. Luong, R. Rohling, K. Gin, T. Tsang, and P. Abolmaesumi, “A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data,” In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 29-37). Springer, Cham, 2018.
  • A.H. Abdi, C. Luong, T. Tsang, G. Allan, S. Nouranian, J. Jue, D. Hawley, S. Fleming, K. Gin, J. Swift, and R. Rohling, “Automatic quality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view,” IEEE transactions on medical imaging36(6), pp.1221-1230, 2017.
  • A. Mehrtash, M. Pesteie, J. Hetherington, P.A. Behringer, T. Kapur, W.M. Wells, R. Rohling, A. Fedorov, and P. Abolmaesumi, “Deepinfer: open-source deep learning deployment toolkit for image-guided therapy,” In Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10135, p. 101351K). International Society for Optics and Photonics, 2017.
  • A.H. Abdi, C. Luong, T. Tsang, J. Jue, K. Gin, D. Yeung, D. Hawley, R. Rohling, and P. Abolmaesumi, “Quality assessment of echocardiographic cine using recurrent neural networks: Feasibility on five standard view planes,” In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 302-310). Springer, Cham, 2017.
  • A.H. Abdi, C. Luong, T. Tsang, J. Jue, K. Gin, D. Yeung, D. Hawley, R. Rohling, and P. Abolmaesumi, “Quality assessment of echocardiographic cine using recurrent neural networks: Feasibility on five standard view planes,” In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 302-310). Springer, Cham, 2017.
  • P. Beigi, P. Malenfant, A. Rasoulian, R. Rohling, A. Dube, and V. Gunka, “Three-dimensional ultrasound-guided real-time midline epidural needle placement with epiguide: a prospective feasibility study,” Ultrasound in medicine & biology43(1), pp.375-379, 2017.
  • M. Honarvar, R.S. Sahebjavaher, R. Rohling, and S.E. Salcudean, “A comparison of finite element-based inversion algorithms, local frequency estimation, and direct inversion approach used in MRE,” IEEE transactions on medical imaging36(8), pp.1686-1698, 2017.
  • P. Beigi, R. Rohling, T. Salcudean, V.A. Lessoway, and G.C. Ng, “Detection of an invisible needle in ultrasound using a probabilistic SVM and time-domain features,” Ultrasonics78, pp.18-22, 2017.
  • R. Singla, P. Edgcumbe, P. Pratt, C. Nguan, and R. Rohling, “Intra-operative ultrasound-based augmented reality guidance for laparoscopic surgery,” Healthcare technology letters4(5), pp.204-209, 2017.
  • R. Singla, P. Edgcumbe, P. Pratt, C. Nguan, and R. Rohling, “Intra-operative ultrasound-based augmented reality guidance for laparoscopic surgery,” Healthcare technology letters4(5), pp.204-209, 2017.
  • F.T. Dezaki, N. Dhungel, A.H. Abdi, C. Luong, T. Tsang, J. Jue, K. Gin, D. Hawley, R. Rohling, and P. Abolmaesumi, “Deep residual recurrent neural networks for characterisation of cardiac cycle phase from echocardiograms,” In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 100-108). Springer, Cham, 2017.
  • J.M. Abeysekera, M. Ma, M. Pesteie, J. Terry, D. Pugash, J.A. Hutcheon, C. Mayer, L. Lampe, S. Salcudean, and R. Rohling, “SWAVE imaging of placental elasticity and viscosity: proof of concept,” Ultrasound in medicine & biology43(6), pp.1112-1124, 2017.
  • P. Beigi, R. Rohling, S.E. Salcudean, and G.C. Ng, “Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling,” International journal of computer assisted radiology and surgery11(6), pp.1183-1192, 2016.
  • C. Schneider, C. Nguan, R. Rohling, and S. Salcudean, “Tracked “pick-up” ultrasound for robot-assisted minimally invasive surgery,” IEEE Transactions on Biomedical Engineering63(2), pp.260-268, 2016.
  • W. Shu, M. Ai, T. Salcudean, R. Rohling, P. Abolmaesumi, and S. Tang, “Broadening the detection view of 2D photoacoustic tomography using two linear array transducers,” Optics express24(12), pp.12755-12768, 2016.
  • N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R. Rohling, S.E. Salcudean, and M. Moradi, “Ultrasound RF time series for classification of breast lesions,” IEEE transactions on medical imaging34(2), pp.652-661, 2015.
  • R.S. Sahebjavaher, G. Nir, M. Honarvar, L.O. Gagnon, J. Ischia, E.C. Jones, S.D. Chang, L. Fazli, S.L. Goldenberg, R. Rohling, and P. Kozlowski, “MR elastography of prostate cancer: quantitative comparison with histopathology and repeatability of methods,” NMR in Biomedicine28(1), pp.124-139, 2015.
  • S. Nagpal, I. Hacihaliloglu, T. Ungi, A. Rasoulian, J. Osborn, V. A. Lessoway, R. N. Rohling, D. P. Borschneck, P. Abolmaesumi, and P. Mousavi, “A global CT to US registration of the lumbar spine,” in SPIE Medical Imaging, p. 90362O–90362O, 2014.
  • A. Rasoulian, J. Osborn, S. Sojoudi, S. Nouranian, V. A. Lessoway, R. N. Rohling, and P. Abolmaesumi, “A System for Ultrasound-Guided Spinal Injections: A Feasibility Study,” in Information Processing in Computer-Assisted Interventions, Springer, pp. 90–99, 2014.
  • A. Rasoulian, R. N. Rohling, and P. Abolmaesumi, “Automatic labeling and segmentation of vertebrae in CT images,” in SPIE Medical Imaging, 2014, pp. 903623–903623.
  • H. Eskandari, A. Baghani, S. E. Salcudean, and R. N. Rohling, Bandpass sampling for elastography. Google Patents, 2014.
  • S. Nagpal, P. Abolmaesumi, A. Rasoulian, T. Ungi, I. Hacihaliloglu, J. Osborn, D. P. Borschneck, V. A. Lessoway, R. N. Rohling, and P. Mousavi, “CT to US Registration of the Lumbar Spine: A Clinical Feasibility Study,” in Information Processing in Computer-Assisted Interventions, Springer, 2014, pp. 108–117, 2014.
  • A. Suzani, A. Rasoulian, S. Fels, R. N. Rohling, and P. Abolmaesumi, “Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+ pose model,” in SPIE Medical Imaging, p. 90360P–90360P, 2014.
  • N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R. N. Rohling, S. E. Salcudean, and M. Moradi, “Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada,” in Ultrasonics Symposium (IUS), 2013 IEEE International, 2013, pp. 96–99.
  • H. Eskandari, S. E. Salcudean, and R. N. Rohling, Method and apparatus for determining viscoelastic parameters in tissue. Google Patents, 2013.
  • M. C. Yip, D. G. Lowe, S. E. Salcudean, R. N. Rohling, and C. Y. Nguan, “Tissue tracking and registration for image-guided surgery,” Medical Imaging, IEEE Transactions on, vol. 31, no. 11, pp. 2169–2182, 2012.
  • I. Hacihaliloglu, R. Abugharbieh, A. J. Hodgson, R. N. Rohling, and P. Guy, “Automatic bone localization and fracture detection from volumetric ultrasound images using 3-d local phase features,” Ultrasound in medicine & biology, vol. 38, no. 1, pp. 128–144, 2012.

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