Author:
Maheswari K. Uma,Thilak M.,SenthilKumar N.,Nagaprasad N.,Jule Leta Tesfaye,Seenivasan Venkatesh,Ramaswamy Krishnaraj
Abstract
AbstractThe forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box–Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R2 = > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively.
Publisher
Springer Science and Business Media LLC
Reference42 articles.
1. Joshua, V. D. & Dehghani, H. Signal regression in frequency-domain diffuse optical tomography to remove superficial signal contamination. Neurophotonics 8(1), 015013 (2021).
2. Gregg, N. M. et al. Brain specificity of diffuse optical imaging: Improvements from superficial signal regression and tomography. Front. Neuroenergetics. 2, 14 (2010).
3. Fantini, S. et al. Perspective: Prospects of non-invasive sensing of the human brain with diffuse optical imaging. APL Photon. 3(11), 110901 (2018).
4. Dehghani, H. et al. Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. Commun. Numer. Methods Eng. 25(6), 711–732 (2008).
5. Jagannath, R. P. K. & Yalavarthy, P. K. Approximation of internal refractive index variation improves image guided diffuse optical tomography of breast. IEEE. Trans Bio-med. Eng. 57(10), 2560–2563 (2010).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献