Advancements in medical laser technology have paved the way for its widespread acceptance in a variety of treatments and procedures. Selectively targeting particular tissue structures with minimally invasive procedures limits the damage to surrounding tissue and allows for reduced post-procedural downtime. In many treatments that are hyperthermia-based, the efficiency depends on the achieved temperature within the targeted tissues. Current approaches for monitoring subdermal temperature distributions are either invasive, complex, or offer inadequate spatial resolution. Numerical studies are often therapy-tailored and source tissue parameters from the literature, lacking versatility and a tissue-specific approach. Here, we show a protocol that estimates the temperature distribution within the tissue based on a thermographic recording of its surface temperature evolution. It couples a time-dependent matching algorithm and thermal-diffusion-based model, while recognizing tissue-specific characteristics yielded by a fast calibration process. The protocol was employed during hyperthermic laser treatment performed ex-vivo on a heterogeneous porcine tissue, and in-vivo on a human subject. In both cases the calibrated thermal parameters correlate with the range of values reported by other studies. The matching algorithm sufficiently reproduced the temperature dynamics of heterogeneous tissue. The estimated temperature distributions within ex-vivo tissue were validated by simultaneous reference measurements, and the ones estimated in-vivo reveal a distribution trend that correlates well with similar studies. The presented method is versatile, supported by the protocol for tissue-specific tailoring, and can readily be implemented for temperature monitoring of various hyperthermia-based procedures by means of recording the surface temperature evolution with a miniature thermal camera implemented within a handheld laser scanner or similar.

Medical treatments such as high-intensity focused ultrasound, hyperthermic laser lipolysis or radiofrequency are employed as a minimally invasive alternatives for targeted tissue therapies. The increased temperature of the tissue triggers various thermal effects and leads to an unavoidable damage. As targeted tissues are generally located below the surface, various approaches are utilized to prevent skin layers from overheating and irreparable thermal damages. These procedures are often accompanied by cooling systems and protective layers accounting for a non-trivial detection of the subsurface temperature peak. Here, we show a temperature peak estimation method based on infrared thermography recording of the surface temperature evolution coupled with a thermal-diffusion-based model and a time-dependent data matching algorithm. The performance of the newly developed method was further showcased by employing hyperthermic laser lipolysis on an ex-vivo porcine fat tissue. Deviations of the estimated peak temperature remained below 1 °C, as validated by simultaneous measurement of depth temperature field within the tissue. Reconstruction of the depth profile shows a good reproducibility of the real temperature distribution with a small deviation of the peak temperature position. A thermal camera in combination with the time-dependent matching bears the scope for non-contact monitoring of the depth temperature profile as fast as 30 s. The latest demand for miniaturization of thermal cameras provides the possibility to embed the model in portable thermal scanners or medical laser technologies for improving safety and efficiency.

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Rheumatoid arthritis is a disease, which significantly impairs patient's quality of life and ability to work. It has been proven that early diagnosis is of paramount importance, since early treatment has a higher likelihood for improving the course of the disease. Thus, the onset of the disease should be detected as early as possible. Changes in tissue oxygenation, blood concentration, light scattering and joint shape indicate joint inflammation, which could be detected and quantified using optical techniques. That is why a proposed system combines the hyperspectral imaging system and 3D profilometer. It enables measuring a spectrum of the reflected light from small joints with about 1 nm resolution, and a shape of the surface with a precision of about 0.02 mm × 0.13 mm × 0.02 mm. In this study a RA diagnostics prototype comprising of a hyperspectral imaging system and a 3D scanning system is used to aid the rheumatoid arthritis diagnostics.