Optimal wavelengths for optoacoustic measurements of blood oxygen saturation in biological tissues

Perekatova, Valeriya; Subochev, Pavel; Kleshnin, Mikhail; Turchin, Ilya V.

doi:10.1364/boe.7.003979

Cited by 25 publications

(24 citation statements)

References 44 publications

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“…Most methods model the distribution of optical absorption coefficients by iteratively updating the distribution after computing the solution of a forward model (cf., e.g., [7][8][9][10][11][12][13][14]) with inclusion of the acoustic inverse problem [15,16]. Alternatively, in multispectral photoacoustic imaging applications, the functional parameters are approximated directly by using a variety of spectral unmixing techniques (cf., e.g., [17][18][19]). Recently, machine learning-based methods for quantitative PAI (qPAI) have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

et al. 2018

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In medical applications, the accuracy and robustness of imaging methods are of crucial importance to ensure optimal patient care. While photoacoustic imaging (PAI) is an emerging modality with promising clinical applicability, state-of-the-art approaches to quantitative photoacoustic imaging (qPAI), which aim to solve the ill-posed inverse problem of recovering optical absorption from the measurements obtained, currently cannot comply with these high standards. This can be attributed to the fact that existing methods often rely on several simplifying a priori assumptions of the underlying physical tissue properties or cannot deal with realistic noise levels. In this manuscript, we address this issue with a new method for estimating an indicator of the uncertainty of an estimated optical property. Specifically, our method uses a deep learning model to compute error estimates for optical parameter estimations of a qPAI algorithm. Functional tissue parameters, such as blood oxygen saturation, are usually derived by averaging over entire signal intensity-based regions of interest (ROIs). Therefore, we propose to reduce the systematic error of the ROI samples by additionally discarding those pixels for which our method estimates a high error and thus a low confidence. In silico experiments show an improvement in the accuracy of optical absorption quantification when applying our method to refine the ROI, and it might thus become a valuable tool for increasing the robustness of qPAI methods.

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Section: Introductionmentioning

confidence: 99%

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

et al. 2018

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“…Applying the inaccuracies obtained in our phantom experiments, which correspond to

{\overset{false~}{R}}_{OA}

values of

1.1 \times R_{μ_{a}}

1.2 \times R_{μ_{a}}

, an absolute error of around 5% to 9% for arterial SO 2 levels (

{SO}_{2}^{ref}

~98%), and an absolute error of around 6% to 12% for venous blood (

{SO}_{2}^{ref}

~75%) can be expected. Moreover, the error in the estimation of SO 2 levels depends on the selection of wavelengths . In this study, the wavelength pair ( λ 1 = 760 nm, λ 2 = 830 nm) was given by the commercial NIRS/NIROT apparatus we worked with.…”

Section: Interpretation Of the Spectral Correction Resultsmentioning

confidence: 99%

Spectral correction for handheld optoacoustic imaging by means of near‐infrared optical tomography in reflection mode

Ulrich

Ahnen²,

Akarçay

et al. 2018

Journal of Biophotonics

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In vivo imaging of tissue/vasculature oxygen saturation levels is of prime interest in many clinical applications. To this end, the feasibility of combining two distinct and complementary imaging modalities is investigated: optoacoustics (OA) and near-infrared optical tomography (NIROT), both operating noninvasively in reflection mode. Experiments were conducted on two optically heterogeneous phantoms mimicking tissue before and after the occurrence of a perturbation. OA imaging was used to resolve submillimetric vessel-like optical absorbers at depths up to 25 mm, but with a spectral distortion in the OA signals. NIROT measurements were utilized to image perturbations in the background and to estimate the light fluence inside the phantoms at the wavelength pair (760 nm, 830 nm). This enabled the spectral correction of the vessel-like absorbers' OA signals: the error in the ratio of the absorption coefficient at 830 nm to that at 760 nm was reduced from 60%-150% to 10%-20%. The results suggest that oxygen saturation (SO ) levels in arteries can be determined with<10% error and furthermore, that relative changes in vessels' SO can be monitored with even better accuracy. The outcome relies on a proper identification of the OA signals emanating from the studied vessels.

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“…Similar to the previous work [ 12 ] we used the images obtained using a large set of 131 closely spaced wavelengths as a reference image. We did not consider the aspect of wavelength optimization from the standpoint of the optimal optical fluence evaluation within the blood or lymphatic vessels [ 18 ], which will be addressed in our future work.…”

Section: Discussionmentioning

confidence: 99%

Wavelength optimization in the multispectral photoacoustic tomography of the lymphatic drainage in mice

2018

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Multispectral photoacoustic tomography provides mapping of the tissue chromophore distributions using sets of tunable laser wavelengths. With the overall goal of studying the dynamics of cerebrospinal fluid in mice in vivo, our work aims to minimize the number of wavelengths to reduce scanning time, improve the temporal resolution, reduce the energy deposition and avoid the tracer photobleaching while maintaining high image quality. To select small sets of wavelengths we directly searched for the combinations of wavelengths providing the best and worst image quality in comparison with a reference image obtained using 131 closely spaced wavelengths between 680 and 940 nm in terms of the peak signal-to-noise ratio (PSNR). We have shown that using the PSNR optimization method, additional improvements could be achieved over the wavelength set selected using the method of the minimization of the extinction matrix condition number.

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Optimal wavelengths for optoacoustic measurements of blood oxygen saturation in biological tissues

Cited by 25 publications

References 44 publications

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

Spectral correction for handheld optoacoustic imaging by means of near‐infrared optical tomography in reflection mode

Wavelength optimization in the multispectral photoacoustic tomography of the lymphatic drainage in mice

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