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Eddy Current Damping Stroke Sensor

Citation

Shahrestani, Shane Shayan (2021) Eddy Current Damping Stroke Sensor. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/zxdh-ej95. https://resolver.caltech.edu/CaltechTHESIS:01132021-224837966

Abstract

Existing paradigms for stroke diagnosis typically involve computed tomography (CT) or magnetic resonance (MR) imaging to classify ischemic versus hemorrhagic stroke variants, as treatment for these subtypes varies widely. Delays in diagnosis and issues related to transport of unstable patients may worsen neurological status. As such, translational medical devices that accelerate time to treatment in the field or hospital setting have the potential to lower morbidity and mortality in stroke patients. We demonstrated feasibility of rapid and accurate bedside stroke detection using a novel, handheld portable eddy current damping imaging device in laboratory benchtop as well as live human clinical ischemic and hemorrhagic stroke settings. We show that diagnosis of stroke may potentially be reduced from several hours to minutes, with additional spatial localization of intracranial hemorrhage, thereby rapidly guiding time-sensitive medical decisions for clinical intervention such as tissue plasminogen activator (tPA). The sensor additionally detects ischemic and hemorrhagic lesions located deep inside the brain, and its range can be selectively tuned during sensor design and fabrication.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Stroke, medical engineering, eddy current, sensor
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Medical Engineering
Awards:Caltech Three Minute Thesis (3MT) competition, 2021: 3rd Place.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Tai, Yu-Chong
Group:3MT Competition (Caltech)
Thesis Committee:
  • Gao, Wei (chair)
  • Tai, Yu-Chong
  • Gharib, Morteza
  • Zada, Gabriel
Defense Date:26 March 2021
Non-Caltech Author Email:shanesha (AT) usc.edu
Record Number:CaltechTHESIS:01132021-224837966
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:01132021-224837966
DOI:10.7907/zxdh-ej95
ORCID:
AuthorORCID
Shahrestani, Shane Shayan0000-0001-7561-4590
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14055
Collection:CaltechTHESIS
Deposited By: Shane Shahrestani
Deposited On:15 Apr 2021 17:01
Last Modified:26 Oct 2023 20:40

Thesis Files

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