Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

Gutta, Sreedevi; Kalva, Sandeep Kumar; Pramanik, Manojit; Yalavarthy, Phaneendra K.

doi:10.1002/mp.13023

Cited by 18 publications

(14 citation statements)

References 40 publications

(126 reference statements)

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“…The popular regularization in medical imaging reconstruction includes the Tikhonov regularization [56] and total variation regularization [54] . Among them, total variation is widespread use for recovering noise-contaminated images under the restricted imaging configuration.…”

Section: Methodsmentioning

confidence: 99%

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

2023

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Section: Methodsmentioning

confidence: 99%

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

2023

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“…Experimental data from in‐vivo rat brain was also used to compare the performance of different reconstruction methods explained here. The details about the experimental setup and the data collection strategy from in‐vivo rat brain can be found in Reference [19]. The collected experimental OA data were pre‐processed with a bandpass filter having a bandwidth of 0.1–8 MHz.…”

Section: Methodsmentioning

confidence: 99%

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

Yalavarthy

Kalva

Pramanik

et al. 2020

Journal of Biophotonics

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Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the modelbased schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.

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“…When experimental f (ν) function contains noises which often oscillate with large amplitude and frequency, the filter algorithms are needed to provide a clear information about the features of the observed system. Tikhonov method can be used as a strategy to solve this problem, with success in many cases [10,11,12,13,14]. The Tikhonov method consists of finding the solution which minimizes the function Φ λ (f ), where Φ λ (f ) is given by [16] Φ…”

Section: Generalized Tikhonov Functionalmentioning

confidence: 99%

“…Although widely used in image processing and inverse problems, its use in chemometrics is not often found in literature, some examples are [10,11,12,13,14]. As it will be shown, the Tikhonov method (TM) is easy to implement and efficient.…”

Section: Introductionmentioning

confidence: 99%

Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye

Lemes¹,

Santos²,

Tavares³

et al. 2022

Preprint

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All signals obtained as instrumental response of analytical apparatus are affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an inherently weak process, the noise background can lead to misinterpretations. Although surface amplification of the Raman signal using metallic nanoparticles has been a strategy employed to partially solve the signal-tonoise problem, the pre-processing of Raman spectral data through the use of mathematical filters has become an integral part of Raman spectroscopy analysis. In this paper, a Tikhonov modified method to remove random noise in experimental data is presented. In order to refine and improve the Tikhonov method as filter, the proposed method includes Euclidean norm of the fractional-order derivative of the solution as an additional criterion in Tikhonov function. In the strategy used here, the solution depends on the regularization parameter, λ, and on the fractional derivative order, α.As will be demonstrated, with the algorithm presented here, it is possible to obtain a noise free spectrum without affecting the fidelity of the molecular signal. In this alternative, the fractional derivative works as a fine control parameter for the usual Tikhonov method. The proposed method was applied to simulated data and to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye in Ag nanoparticles colloidal dispersion.

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Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

Cited by 18 publications

References 40 publications

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye

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