Lower extremity ulcers are one of the most common complications that not only affect many people around the world but also have huge impact on economy since a large amount of resources are spent for treatment and prevention of the diseases. Clinical studies have shown that reduction in the wound size of 40% within 4 weeks is an acceptable progress in the healing process. Quantification of the wound size plays a crucial role in assessing the extent of healing and determining the treatment process. To date, wound healing is visually inspected and the wound size is measured from surface images. The extent of wound healing internally may vary from the surface. A near-infrared (NIR) optical imaging approach has been developed for non-contact imaging of wounds internally and differentiating healing from non-healing wounds. Herein, quantitative wound size measurements from NIR and white light images are estimated using a graph cuts and region growing image segmentation algorithms. The extent of the wound healing from NIR imaging of lower extremity ulcers in diabetic subjects are quantified and compared across NIR and white light images. NIR imaging and wound size measurements can play a significant role in potentially predicting the extent of internal healing, thus allowing better treatment plans when implemented for periodic imaging in future.
In current computed tomography (CT) examinations, the associated
X-ray radiation dose is of significant concern to
patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning
technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs)
parameter as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably
increase data noise and degrade CT perfusion maps greatly if no adequate noise control is applied during image
reconstruction. To capture the essential dynamics of CT perfusion, a simple spatial-temporal Bayesian method that uses
a piecewise parametric model of the residual function is used, and then the model parameters are estimated from a
Bayesian formulation of prior smoothness constraints on perfusion parameters. From the fitted residual function, reliable
CTP parameter maps are obtained from low dose CT data. The merit of this scheme exists in the combination of
analytical piecewise residual function with Bayesian framework using a simpler prior spatial constrain for CT perfusion
application. On a dataset of 22 patients, this dynamic
spatial-temporal Bayesian model yielded an increase in
signal-tonoise-ratio (SNR) of 78% and a decrease in
mean-square-error (MSE) of 40% at low dose radiation of 43mA.