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Non-invasive Detection of Peripheral Arterial Disease in the Lower Limb

Suciu, Diana-Madalina (2023) Non-invasive Detection of Peripheral Arterial Disease in the Lower Limb. Master of Science by Research (MScRes) thesis, University of Kent,. (doi:10.22024/UniKent/01.02.103779) (KAR id:103779)


Multispectral imaging (MSI) is a novel, non-invasive technique, which can detect tissue reflectance and deliver information regarding tissue oxygenation. The application of spectral imaging in medical care, can lead to development of early-stage diagnosis and monitoring of vascular diseases, such as Peripheral Arterial Disease. Although MSI is known to be widely applicable with a high functionality, many techniques have not been fully validated and require bulky, non-intuitive setup and analysis, hence more research and clinical trials are required. This thesis presents the first step of the development cycle in collaboration with the National Health Service, to create a low-cost, portable device for use in a clinical setting and that can also be transported to housebound patients. The system uses a four-lens multispectral camera and 16 attachable filters available in the visible range of 500 nm - 650 nm, with an LED medical lamp to collect images of a foot before and during arterial occlusion, using a blood pressure cuff inflated at 180 mmHg. A MATLAB program was created and used for calibration, processing of images and implementation of Beer-Lambert function; which delivered the oxygen saturation value. Four wavelengths were chosen as best fit: 520 nm, 540 nm, 610 nm, 640 nm, out of which 520 nm represents the isobestic wavelength, where no variation is seen between the image with and without cuff occlusion. Whereas, the other three wavelengths represent the opposite. The system created and the final results can provide proof of principle regarding the feasibility of the technology presented. Although, the results of the preliminary four filters were not highly conclusive, the methodologies developed provide a promising platform for future optimisation.

Item Type: Thesis (Master of Science by Research (MScRes))
Thesis advisor: Barker, Robert
DOI/Identification number: 10.22024/UniKent/01.02.103779
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Chemistry and Forensics
Funders: University of Kent (
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 08 Nov 2023 12:48 UTC
Last Modified: 09 Nov 2023 04:17 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Suciu, Diana-Madalina.

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