Higo, Kieran (2021) Time-optimal Controller Design for An Auto TCD Probe. [USQ Project]
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HIGO Kieran dissertation_redacted.pdf Download (1MB) | Preview |
Abstract
Transcranial Doppler (TCD) ultrasound (US) is a form of non-invasive medical imaging primarily used for measuring cerebral blood flow velocity (CBF-V). Velocity of the flow of blood in the intracranial arteries during dilation and constriction. By placing the TCD probe at the thin bone windows of temporal bone, the juncture of the frontal, parietal, temporal, and sphenoid (Naqvi et al. 2013), low frequency US waves in the order of 2 MHz or less, are used to produce a high-resolution imaging of CBF-V and vessel plasticity. Whilst relatively inexpensive the performance of manual probes is highly dependent on the operator, with auto probes scanning the entire brain. Both methods require considerable time resulting in discomfort for the patient and consuming resources of the user.
Proposed designs implement several algorithms to automatically scan and generate imagery. The initial algorithm is a global search of the Circle of Willis within the human brain, identifying targeted blood vessel sections (Huang et al. n.d.). Once the global search is complete, a local search is initiated to increase the accuracy of search outputs by examining the true or false signals from the global search, acquiring a continuous and stable signal spectrum.
A controller that could shorten the time required to perform ultrasound of the brain will reduce discomfort for the user and increase the availability of the resource, increasing the number of patients that can be serviced further improving the efficiency of the device (Huang et al. n.d.). This dissertation aims to simulate an auto TCD probe and reduce the time taken for these algorithms to perform searches and produce imagery of the CBF-V. The algorithms are the Decision Tree, Naive Bayes, Equal Interval Search, An Unknown Algorithm and the Golden Section Search.
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| Item Type: | USQ Project |
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| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021) |
| Supervisors: | Song, Bo |
| Qualification: | Bachelor of Engineering (Electrical) |
| Date Deposited: | 03 Jan 2023 02:11 |
| Last Modified: | 26 Jun 2023 01:24 |
| Uncontrolled Keywords: | transcranial doppler, ultrasound, cerebral blood flow, velocity, algorithm, search, decision tree, naive bayes, equal interval search, golden section search, an unknown algorithm |
| URI: | https://sear.unisq.edu.au/id/eprint/51819 |
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