Towards quantitative assessment of burn based on photoacoustic and optical coherence tomography

Dynamic optical coherence elastography for skin burn assessment: A preliminary study on mice model

Skin burns that include tissue coagulation necrosis imply variations in stiffness. Dynamic phase-sensitive optical coherence elastography (OCE) is used to evaluate the stiffness of burned skin nondestructively in this paper. The homemade dynamic OCE was initially verified through tissue-mimicking phantom experiments regarding Rayleigh wave speed. After being burned with a series of temperatures and durations, the corresponding structure and stiffness variations of mice skin were demonstrated by histological images, optical coherence tomography B-scans, and OCE elastic wave speed maps. The results clearly displayed the variation in elastic properties and stiffness of the scab edge extending in the lateral direction. Statistical analysis revealed that murine skin burned at temperatures exceeding 100°C typically exhibited greater stiffness than skin burned at temperatures below 100°C. The dynamic OCE technique shows potential application for incorporating elasticity properties as a biomechanical extension module to diagnose skin burn injuries.

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Non-invasive medical imaging technology for the diagnosis of burn depth

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Quantification method to objectively evaluate the fibrous structural status of tendons based on polarization-sensitive OCT

Histological analysis is widely used to evaluate injured tendons; however, it has the limitation of being semi-quantitative. Hence, we developed a quantification method to objectively evaluate the fibrous structure of tendons, exhibiting the optical property of birefringence, using polarization-sensitive optical coherence tomography (PS-OCT). We used a partial-rupture rat model in which the middle 0.75 cm of the Achilles tendon was cut with a blade. Rats were sacrificed at 2, 4 or 6 weeks after the injury, and PS-OCT and histological analyzes were performed. The PS-OCT phase retardation images and score well represented the structural changes of the injured tendon according to the wound healing state. Therefore, the proposed novel quantification method using PS-OCT can be used to evaluate the fibrous structural status of tendons.

Mixed scattering as a problem in laser speckle contrast analysis

Static scattering is detrimental to the accuracy of laser speckle contrast analysis (LASCA) measurements on skin when, instead of percentile change monitoring, absolute perfusion values are needed, e.g., for tissue injury examination. Perfusion values were calculated using two evaluation models, while changing the dynamic/static scattering ratio of monitored skin and tissue phantoms. Results were strongly affected by the significant increase of static contribution. Measurements on a modified tissue phantom showed that the changes in the measured perfusion values were mostly caused by the mixed scattering, which was omitted by the tested models. Dynamic ratio values obtained by multi-exposure LASCA could be used for perfusion data correction.