Hemoglobin is one of the most important chromophores in the human body, since oxygen is carried to the tissue by binding with the hemoglobin. Therefore measuring the concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) is very important in both clinical settings and academic fields. Frequency domain near infrared spectroscopy (fdNIR spectroscopy) is a technique that can be used to measure the absolute concentrations of HbO and HbR non-invasively and locally. The fdNIR spectrometer utilizes the attenuation and the phase shift (with respect to the source) that an intensity modulated NIR light experiences in order to calculate the absorption (μa) and reduced scattering (μ′s) coefficient of the tissue. In this work, a miniaturized dual-wavelength fdNIR spectrometry instrument is presented with both tissue-like phantom and in vivo occlusion measurements. Systematic tests were performed on tissue phantoms to quantify the accuracy and stability of the instrument. The absolute errors for μaand μ′s were below 15% respectively. The amplitude and phase uncertainty were below 0.25% and 0.35°. In vivo measurements were also conducted to further validate the system.
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Determination of material optical properties from diffusive reflection light intensity profiles at multiple distances
Abstract Optical absorption and scattering properties are often estimated from the diffusive reflection light intensity at only one distance from the material surface, which often encounters accuracy and convergence issues. In this work, a method was proposed to determine optical properties by using diffusive reflection light intensity profiles at multiple distances, which enhanced data richness as a result of the intensity profiles are linearly independent. In this method, five features of light intensity profiles (contrast, correlation, energy, homogeneity, and second moment) were used to reduce the data dimensions. To demonstrate the effectiveness of the proposed method, Monte Carlo (MC) simulations were used to generate diffusive reflection light intensity profiles with noise at different distances for various combinations of four optical properties (absorption coefficientμa, scattering coefficientμs, isotropic coefficientg, and refractive indexn). The five profile feature vectors were used as inputs and the four optical parameters were used as outputs to train and test a backpropagation (BP) neural network. The influences of noise levels and the number of diffusive light intensity profiles on parameter estimation accuracy were investigated. The four optical parameters estimated by the BP network were compared with the results estimated by the traditional least squares method, which shows that the proposed method can estimate the optical properties with higher accuracy and better convergence.
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- Award ID(s):
- 1903716
- PAR ID:
- 10583267
- Publisher / Repository:
- IOP Science
- Date Published:
- Journal Name:
- Materials Research Express
- Volume:
- 7
- Issue:
- 2
- ISSN:
- 2053-1591
- Page Range / eLocation ID:
- 025403
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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