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Dynamic thermography has been clinically proven to be a valuable diagnostic technique for skin tumor detection as well as for other medical applications, and shows many advantages over static thermography. Numerical modelling of heat transfer phenomena in biological tissue during dynamic thermography can aid the technique by improving process parameters or by estimating unknown tissue parameters based on measurement data. This paper presents a new non-linear numerical model of multilayer skin tissue containing a skin tumour together with thermoregulation response of the tissue during the cooling-rewarming process of dynamic thermography. The thermoregulation response is modelled by temperature-dependent blood perfusion rate and metabolic heat generation. The aim is to describe bioheat transfer more realistically. The model is based on the Pennes bioheat equation and solved numerically using a subdomain BEM approach treating the problem as axisymmetrical. The paper includes computational tests for Clark II and Clark IV tumours, comparing the models using constant and temperature-dependent properties which showed noticeable differences and highlighted the importance of using a local thermoregulation model. Results also show the advantage of using dynamic thermography for skin tumour screening and detection at an early stage. One of the contributions of this paper is a complete sensitivity analysis of 56 model parameters based on the gradient of the surface temperature difference between tumour and healthy skin. The analysis shows that size of the tumour, blood perfusion rate, thermoregulation coefficient of the tumour, body core temperature and density and specific heat of the skin layers in which the tumour is embedded are important for modelling the problem, and so have to be determined more accurately to reflect realistic skin response of the investigated tissue, while metabolic heat generation and its thermoregulation are not.

Melanoma is one of the most fatal skin cancers; for this reason, there is a need for the development of new safe, non-invasive and efficient diagnostic techniques. Dynamic thermography is showing to be a promising technique for the early detection of skin cancers. Therefore, this paper investigates two different inverse bioheat problems using steady-state and transient skin temperature measurements. Both problems are investigated numerically to estimate how accurate blood perfusion rate, metabolic heat generation, diameter and thickness of the tumour can be estimated simultaneously under exact and noisy measurement data, based on a complex numerical model describing multilayer tissue. The inverse problems have been tested using different melanoma size, Clark II and Clark IV. The Design of Experiments (DOE) technique has been used to solve and analyse the inverse problems. A substantial number of numerical model evaluations, totalling 2, 306, 486 simulations, had to be undertaken as part of the full factorial DOE. The results show that it is always possible to obtain tumour parameters using exact static or dynamic measurement data. However, for noisy temperature data, the use of a dynamic approach showed an advantage over the steady-state one, which failed because of the very small temperature differences between the healthy skin and the tumour. The dynamic thermography can retrieve blood perfusion rate, thickness and diameter of the tumour as well as the metabolic heat generation despite the low sensitivity for low and high levels of measurement error; however, to detect melanoma lesions at an early stage, the measurement and model errors should be kept as low as possible.

The use of cationic biopolymer surfaces for high protein binding affinity matrices is described. As model proteins, fluorescently labeled bovine serum albumins (FITC-BSA, TRITC-BSA) have been employed. The amount of proteins on such cationically rendered surfaces was quantified by QCM-D. In addition, flexible, transparent, patterned COP slides have been prepared and loaded with proteins ranging from 15 pM to 15 μM TRITC-BSA.