Research

Research

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1.0 Current Research

1.1 Medical Robotics

Robotic Transcranial Magnetic Stimulation - Inside Out Tracking and Projection Mapping

Intro: "Transcranial Magnetic Stimulation (TMS) is a neurostimulation technique based on the principle of electromagnetic induction of an electric field in the brain with both research and clinical applications. To produce an optimal neuro-modulatory effect, TMS coil must be placed on the head and oriented accurately with respect to the region of interest within the brain. A robotic method can enhance the accuracy and facilitate the procedure for TMS coil placement. This work presents two system improvements for robot-assisted TMS (RA-TMS) application."

Development: C++, Python, Matlab, ROS, Unity, Vtk, 3D Slicer
  1. Liu, Y., Liu, S. J., Sefati, S., Jing, T., Kheradmand, A., & Armand, M. (2022, March). Inside-out tracking and projection mapping for robot-assisted transcranial magnetic stimulation. In Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) III (Vol. 11931, pp. 57-70). SPIE.

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Figure 1.1 The workflow of proposed RA-TMS system [1]

1.2 Medical Augmented Reality

Augmented Reality Assisted Orbital Floor Reconstrution Surgery

Intro: "Conventionally, the accuracy of implant placement relies on the surgeon’s expertise. Intraoperative imaging and navigation are rarely used due to their cost and setup times, so erroneous implant positioning is often unrecognized until postoperative imaging. This confers risk to the patient’s eyeball, orbital vasculature, optic nerves, and stereotactic vision. In this work, we develop the workflow and user interface of an Augmented Reality (AR) system to aid surgeons with intraoperative placement of an orbital floor implant and ultimately reduce rates of implant malposition."

Development: C#, Python, Unity
  1. Liu, Y., Azimi, E., Davé, N., Qiu, C., Yang, R., & Kazanzides, P. (2021, May). Augmented Reality Assisted Orbital Floor Reconstruction. In 2021 IEEE International Conference on Intelligent Reality (ICIR) (pp. 25-30). IEEE.

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Figure 1.2 Transformation map [1]

1.3 Medical Imaging

SAM Integration on 3D Slicer

Intro: "The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest segmentation dataset at this time. The model has demonstrated that it can create high-quality masks for image segmentation with good promptability and generalizability. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and utilization of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer, a widely-used open-source image processing and visualization software that has been extensively used in the medical imaging community. This open-source extension to 3D Slicer and its demonstrations are posted on GitHub (this https URL). SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time."

Development: Python, 3D Slicer
  1. Liu, Y., Zhang, J., She, Z., Kheradmand, A., & Armand, M. (2023). SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM. arXiv preprint arXiv:2304.05622.

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Figure 1.3 SAMM architecture and latency validation [1]

1.4 Neurology / Neuroscience

Autokinetic Effect of Human Subjects

Intro

2.0 Past Research

2.1 Machine Learning, Non-destructive Testing, and Next Generation Batteries

Pulsed Eddy Current Data Analysis for the Characterization of the Second-Layer Discontinuities

Intro: "Pulsed eddy current (PEC) technique has been applied as a viable method to detect hidden discontinuities in metallic structures. Conventionally, selected time-domain features are employed to characterize the PEC data, such as peak value, lift-off point of intersection, rising point, crossing time, and differential time to peak. The research in this work continues the effort in a previous study on detecting the radial cracks starting from the fastener hole in second layer of a two-layer mock-up aircraft structure."

Development: Python, Keras
  1. Liu, Y., Liu, S., Liu, H., Mandache, C., & Liu, Z. (2019). Pulsed eddy current data analysis for the characterization of the second-layer discontinuities. Journal of Nondestructive Evaluation, 38(1), 1-8.

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Figure 2.1 Discontinuities captured by sensors [1]

Lithium Batteries with Bio-waste Derived Activated Carbon

Intro: "Lithium-selenium (LieSe) batteries represent a promising energy storage system due to the relatively high electronic conductivity and high volumetric energy density of Se as a cathode. The design of porous carbon with tunable structure and low cost is a key to enabling Se cathodes for high-performance Li-Se batteries. In this study, hierarchically microporous activated carbon (AC) was fabricated from waste coffee grounds through a carbonization and KOH-activation process."
  1. Zhao, P., Shiraz, M. H. A., Zhu, H., Liu, Y., Tao, L., & Liu, J. (2019). Hierarchically porous carbon from waste coffee grounds for high-performance Li–Se batteries. Electrochimica Acta, 325, 134931.

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Figure 2.2 SEM images of waste coffee grounds and the electrolyte [1]