Diffuse correlation spectroscopy (DCS) is a powerful tool for investigating microvascular dynamics in deep tissues. It has been used for non-invasive blood flow assessment at the bedside. This study first provides a thorough literature review on system setups (continuous-wave, frequency-domain, and time-domain) and derive corresponding theoretical models. I then present an innovative deep learning algorithm, DCS-NET, which is easy and robust to train, fast, and insensitive to measurement noise for data processing. The absolute blood flow index (BFi) at different depths with/without measurement noise was calculated, followed by a relative blood flow calculation. I then calculated the intrinsic sensitivity, and calculated BFi with varied optical properties and scalp/skull thicknesses finally. Compared with the semi-infinite, and three-layer fitting methods, I show that the DCS-NET is approximately 17,000 times faster than the traditional three-layer model and 32 times faster than the semi-infinite model, respectively. It provides increased inherent sensitivity to deep tissues compared to fitting methods. DCS-NET demonstrates remarkable noise resilience and is minimally affected by variations in μa and μs'. Additionally, we have shown that DCS-NET can extract relative blood flow index (rBFi) with a substantially lower error of 8.35%. In comparison, the semi-infinite and three-layer fitting models produce considerable errors in rBFi, amounting to 43.76% and 19.66%, respectively. Additionally, a DCS prototype is developed by integrating an advanced CMOS single-photon avalanche diode array, which employing a parallel light detection scheme, exhibits exceptional photon-counting throughput. The system tested on a milk phantom, showing SNR gain with the entire sensor is improved nearly 160-fold compared with a single pixel. An in vivo blood occlusion test was also performed. In conclusion, our system works well, and this research can offer peers effective guidance to embark on DCS research.
| Date of Award | 15 May 2024 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde |
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| Supervisor | David Li (Supervisor) & Erfu Yang (Supervisor) |
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