An iterative method of light fluence distribution estimation for quantitative photoacoustic imaging

Author(s):  
Fabian Guerra ◽  
Diego S. Dumani
2011 ◽  
Vol 16 (9) ◽  
pp. 096016 ◽  
Author(s):  
Adam Q. Bauer ◽  
Ralph E. Nothdurft ◽  
Todd N. Erpelding ◽  
Lihong V. Wang ◽  
Joseph P. Culver

Author(s):  
Altaf Hussain ◽  
Khalid Daoudi ◽  
Erwin Hondebrink ◽  
Wiendelt Steenbergen

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zong-Han Hsieh ◽  
Ching-Hsiang Fan ◽  
Yi-Ju Ho ◽  
Meng-Lin Li ◽  
Chih-Kuang Yeh

Abstract The major obstacles of optical imaging and photothermal therapy in biomedical applications is the strong scattering of light within biological tissues resulting in light defocusing and limited penetration. In this study, we propose high intensity focused ultrasound (HIFU)-induced heating tunnel to reduce the photon scattering. To verify our idea, Monte Carlo simulation and intralipid-phantom experiments were conducted. The results show that the thermal effect created by HIFU could improve the light fluence at the targeted region by 3% in both simulation and phantom experiments. Owing to the fluence increase, similar results can also be found in the photoacoustic experiments. In conclusion, our proposed method shows a noninvasive way to increase the light delivery efficiency in turbid medium. It is expected that our finding has a potential for improving the focal light delivery in photoacoustic imaging and photothermal therapy.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Janek Gröhl ◽  
Thomas Kirchner ◽  
Tim J. Adler ◽  
Lina Hacker ◽  
Niklas Holzwarth ◽  
...  

AbstractThe ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO$$_2$$ 2 , enables a wide variety of possible applications. sO$$_2$$ 2 can be computed from the ratio of oxyhemoglobin HbO$$_2$$ 2 and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO$$_2$$ 2 yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal. In this work, we propose learned spectral decoloring to enable blood oxygenation measurements to be inferred from multispectral photoacoustic imaging. The method computes sO$$_2$$ 2 pixel-wise, directly from initial pressure spectra $$S_{\text {p}_0}(\lambda , \mathbf {x})$$ S p 0 ( λ , x ) , which represent initial pressure values at a fixed spatial location $$\mathbf {x}$$ x over all recorded wavelengths $$\lambda$$ λ . The method is compared to linear unmixing approaches, as well as pO$$_2$$ 2 and blood gas analysis reference measurements. Experimental results suggest that the proposed method is able to obtain sO$$_2$$ 2 estimates from multispectral photoacoustic measurements in silico, in vitro, and in vivo.


2010 ◽  
Vol 15 (4) ◽  
pp. 046003 ◽  
Author(s):  
Behnoosh Tavakoli ◽  
Patrick D. Kumavor ◽  
Andres Aguirre ◽  
Quing Zhu

Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.


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