Chapter title |
Contrast Ratio Quantification During Visualization of Microvasculature
|
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Chapter number | 59 |
Book title |
Oxygen Transport to Tissue XL
|
Published in |
Advances in experimental medicine and biology, August 2018
|
DOI | 10.1007/978-3-319-91287-5_59 |
Pubmed ID | |
Book ISBNs |
978-3-31-991285-1, 978-3-31-991287-5
|
Authors |
G. Saiko, A. Douplik, Saiko, G., Douplik, A. |
Abstract |
Visualization and monitoring of capillary loops in dermis and mucosa are of interest for a number of clinical applications, such as capillaroscopy, early cancer, or shock detection. For historical reasons, an unaided eye is still a primary aide to diagnostics in visual examinations for many medical specializations. However, the ability to make an early diagnosis using the unaided eye has remained poor. New optical modalities can significantly improve the accuracy of anomaly detection. To compare the image quality of various optical schemes, a systematic way to quantify it is required. The goal of this work is to develop an analytical approach for assessment of a contrast ratio as a single number quantitative metric image quality during optical imaging of capillary network. Based on skin layers geometry, we developed a two-layer optical tissue model. Then, we extended a two-layer Kubelka-Munk model to calculate the contrast ratio of a subsurface defect (absorption or scattering) imaging. We have obtained an explicit expression for the contrast ratio in the two-layer model. Then, we investigated how the contrast ratio is affected by the tissue optical parameters and depth of the inhomogeneity. Based on this analysis we identified two important cases: (a) the top layer with negligible absorption, and (b) the 'optically thick' top layer. The contrast ratio deteriorates differently with the inhomogeneity depth in these two cases. The contrast ratio can be used for quantification of image quality of subsurface inhomogeneities in the skin. The developed approach can be employed for estimation of interrogating depth of various tissue inhomogeneities and optimization of imaging techniques. |
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