Near-infrared (NIR) spectroscopic imaging of wounds has been performed by past researchers to obtain tissue oxygenation at discrete point locations. We had developed a near-infrared optical scanner (NIROS) that performs noncontact NIR spectroscopic (NIRS) imaging to provide 2D tissue oxygenation maps of the entire wounds. Regions of changed oxygenation have to be demarcated and registered with respect to visual white light images of the wound. Herein, a semi-automatic image segmentation and co-registration approach using machine learning has been developed to differentiate regions of changed tissue oxygenation. A registration technique was applied using a transformation matrix approach using specific markers across the white light image and the NIR images (or tissue oxygenation maps). This allowed for physiological changes observed from hemodynamic changes to be observed in the RGB white light image as well. Semi-automated segmentation techniques employing graph cuts algorithms was implemented to demarcate the 2D tissue oxygenation maps depicting regions of increased or decreased oxygenation and further coregistered onto the white light images. The developed registration technique was validated via phantom studies (both flat and curved phantoms) and in-vivo studies on controls, demonstrating an accuracy >97%. The technique was further implemented on wounds (here, diabetic foot ulcers) across weeks of treatment. Regions of decreased oxygenation were demarcated, and its area estimated and co-registered in comparison to the clinically demarcated wound area. Future work involves the development of automated machine learning approaches of image analysis for clinicians to obtain real-time co-registered clinical and subclinical assessments of the wound.
Diabetic Foot Ulcers (DFUs) are responsible for 20% of diabetic-related hospitalization and 85% of diabetes related amputations. In DFUs the primary factor affecting healing is an adequate oxygen supply to the wound. However, the gold standard approach for assessing DFUs is by evaluating the reduction of wound size over a four-week period. In this study, we investigate the potential of altered breathing patterns as a technique to assess localized oxygenated perfusion in DFUs as a measure of healing potential. A continuous wave (CW), non-contact, near infrared optical scanner (NIROS) was used to conduct NIR based spectroscopic imaging at dual discrete wavelengths (729nm and 799nm) on DFUs with 7mW of maximum optical power. Subjects were imaged at discrete time points and dynamically utilizing an altered breathing paradigm (i.e. breath-hold) to measure the relative oxy- (ΔHbO) and deoxyhemoglobin (ΔHbR) changes in normal and DFU scenarios. Results show that in normal individuals, ΔHbO/ΔHbR changes at all points of the foot because of altered breathing patterns are synchronous; whereas in the DFU scenario changes in hemodynamic parameters are asynchronous. This indicates that under normal circumstances, oxygenated perfusion changes are consistent and uniform at all points of the foot as opposed to the DFU scenario’s inconsistent oxygenated perfusion. Altered breathing paradigms may serve as a useful tool in assessing localized sub-surface oxygenated perfusion in regions around the wound, and help clinicians better cater the treatment process.
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