Swept-source optical coherence tomography of lower limb wound healing with histopathological correlation

Abstract

Direct noninvasive visualization of wound bed with depth information is important to understand the tissue repair. We correlate skin swept-source-optical coherence tomography (OCT) with histopathological and immunohistochemical evaluation on traumatic lower limb wounds under honey dressing to compare and assess the tissue repair features acquired noninvasively and invasively. Analysis of optical biopsy identifies an uppermost brighter band for stratum corneum with region specific thickness (p < 0.0001) and gray-level intensity (p < 0.0001) variation. Below the stratum corneum, variation in optical intensities is remarkable in different regions of the wound bed. Correlation between OCT and microscopic observations are explored especially in respect to progressive growth and maturation of the epithelial and subepithelial components. Characteristic transition of uniform hypolucid band in OCT image for depigmented zone to wavy highly lucid band in the pigmented zone could be directly correlated with the microscopic findings. The transformation of prematured epithelium of depigmented area, with low expression of E-cadherin, to matured epithelium with higher E-cadherin expression in pigmented zone, implicated plausible change in their optical properties as depicted in OCT. This correlated evaluation of multimodal images demonstrates applicability of swept-source-OCT in wound research and importance of integrated approach in validation of new technology.

Optical, Spectroscopic, and Doppler Evaluation of “Normal” and “Abnormal” Reflexology Areas in Lumbar Vertebral Pathology: A Case Study

“…In this system, the axial scans (A-scans) of the laser were performed at a sweeping frequency of 16 kHz to construct the depth profile (~2 mm). The details of the instrumentation are described elsewhere [29]. OCT imaging (with a 3 mm imaging width) was performed at LEFT L-region (“abnormal” RA) and RIGHT L-region (“normal” RA).…”

Noninvasive Characterisation of Foot Reflexology Areas by Swept Source-Optical Coherence Tomography in Patients with Low Back Pain

“…The skin SS-OCT system produces cross-sectional images for different subsurface layers. The details of the instrumentation were previously described [31]. …”

“…The IIT Kharagpur researchers were involved in SS-OCT imaging within their respective domains of knowledge [31]. Just before capturing data, a drop of glycerol was applied with a very light touch uniformly over the skin to minimise optical mismatch without affecting stratum corneum integrity [32].…”

“…The SS-OCT images were categorised into 4 grades depending on the characteristics of the pathological conditions of the same. Based on the previous domain knowledge in interpreting skin SS-OCT images and skin histology, this grading was proposed by the related experts [31]. The grading was mainly based on the optical intensity parameters of the individual skin layers and the extent of the distinctly separable layers of the targeted skin areas.…”

Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

“…Corresponding percentiles are equally distributed across the intensity values depending on the number of regions, since the tissue discrimination capability will be precisely evaluated as a function of the number regions in this preliminary step. The employment of such a segmentation strategy avoids the necessity of previously known information about the tissue structure as in [19] and, therefore, makes the approach more extensible for tissue classification in urology or gynecology for example, where OCT is expected to gain popularity. To study the intensity distribution of the segmented regions, the first four statistical moments of the intensity values per region are computed as follows: $\begin{array}{c}{1}^{\mathit{st}}\hspace{0.17em}\mathit{statistical}\hspace{0.17em}\mathit{moment}\hspace{0.17em}(\mathit{mean})\hspace{0.17em}\overline{I}=\frac{1}{N}{\sum }_{i=1}^N{I}_i,\\ 2^{\mathit{nd}}\hspace{0.17em}\mathit{statistical}\hspace{0.17em}\mathit{moment}\hspace{0.17em}(\mathit{standard}\hspace{0.17em}\mathit{deviation})\hspace{0.17em}{\sigma }_I={\left(\frac{1}{N}{\sum }_{i=1}^N{(I_i-\overline{I})}^2\right)}^{\frac{1}{2}},\\ 3^{\mathit{rd}}\hspace{0.17em}\mathit{statistical}\hspace{0.17em}\mathit{moment}\hspace{0.17em}(\mathit{skewness})\hspace{0.17em}{S}_I=\frac{\frac{1}{N}{\sum }_{i=1}^N{(I_i-\overline{I})}^3}{{\sigma }_I^3},\\ 4^{\mathit{th}}\hspace{0.17em}\mathit{statistical}\hspace{0.17em}\mathit{moment}\hspace{0.17em}(\mathit{kurtosis})\hspace{0.17em}{K}_I=\frac{\frac{1}{N}{\sum }_{i=1}^N{false(I_i-\overline{I}false)}^4}{{\sigma }_I^4},\end{array}$where I i is the intensity value of a pixel within the region and N the number of pixels of the region.…”