Sparsity‐based acoustic inversion in cross‐sectional multiscale optoacoustic imaging

Han, Yiyong; Tzoumas, Stratis; Nunes, António; Ntziachristos, Vasilis; Rosenthal, Amir

doi:10.1118/1.4928596

Cited by 32 publications

(22 citation statements)

References 40 publications

(52 reference statements)

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“…A sparsity-based acoustic inversion algorithm was developed in cross-sectional multiscale PAT systems using different types of sparsity regularizations. 20 The forward model was built based on the image grid and the measurement geometry in a PAT system with the assumption of a lossless dispersion-free homogeneous acoustic medium. L 1 , total variation (TV), combined TV-L 1 regularizations were compared in both simulations and experiments.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model

et al. 2018

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Photoacoustic (PA) imaging is an emerging imaging technique for many clinical applications. One of the challenges posed by clinical translation is that imaging systems often rely on a finite-aperture transducer rather than a full tomography system. This results in imaging artifacts arising from an underdetermined reconstruction of the initial pressure distribution (IPD). Furthermore, clinical applications often require deep imaging, resulting in a low-signal-to-noise ratio for the acquired signal because of strong light attenuation in tissue. Conventional approaches to reconstruct the IPD, such as back projection and time-reversal, do not adequately suppress the artifacts and noise. We propose a sparsity-based optimization approach that improves the reconstruction of IPD in PA imaging with a linear array ultrasound transducer. In simulation studies, the forward model matrix was measured from k-Wave simulations, and the approach was applied to reconstruct simulated point objects and the Shepp-Logan phantom. The results were compared with the conventional back projection, time-reversal approach, frequency-domain reconstruction, and the iterative least-squares approaches. In experimental studies, the forward model of our imaging system is directly measured by scanning a graphite point source through the imaging field of view. Experimental images of graphite inclusions in tissue-mimicking phantoms are reconstructed and compared with the back projection and iterative least-squares approaches. Overall these results show that our proposed optimization approach can leverage the sparsity of the PA images to improve the reconstruction of the IPD and outperform the existing popular reconstruction approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model

et al. 2018

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“…Therefore, the in vivo application of eMSOT was restricted so far to high intensity, high signal-to-noise ratio regions of high-quality optoacoustic images. With the advent of advanced image reconstruction techniques that yield less artefacts and noise [20], it is expected that elaborate spectral analysis methods will also be used in a more routine basis. An additional limitation of eMSOT is that it assumes haemoglobin as the sole tissue absorber, not accounting for the potential presence of other absorbing molecules (e.g.…”

Section: Discussion: Advantages and Limitationsmentioning

confidence: 99%

Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology

Tzoumas

Ntziachristos

2017

Phil. Trans. R. Soc. A.

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A key feature of optoacoustic imaging is the ability to illuminate tissue at multiple wavelengths and therefore record images with a spectral dimension. While optoacoustic images at single wavelengths reveal morphological features, in analogy to ultrasound imaging or X-ray imaging, spectral imaging concedes sensing of intrinsic chromophores and externally administered agents that can reveal physiological, cellular and subcellular functions. Nevertheless, identification of spectral moieties within images obtained at multiple wavelengths requires spectral unmixing techniques, which present a unique mathematical problem given the three-dimensional nature of the optoacoustic images. Herein we discuss progress with spectral unmixing techniques developed for multispectral optoacoustic tomography. We explain how different techniques are required for accurate sensing of intrinsic tissue chromophores such as oxygenated and deoxygenated haemoglobin versus extrinsically administered photo-absorbing agents and nanoparticles. Finally, we review recent developments that allow accurate quantification of blood oxygen saturation (sO) by transforming and solving the sO estimation problem from the spatial to the spectral domain.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

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“…A discrete wavelet packet decomposition can be used to further speed up the computations, since the inversion is decoupled into smaller subproblems [22], although memory requirements are not significantly reduced. Both the computational complexity and memory overhead can be reduced by decreasing the number of measurements (projections) and applying appropriate regularization for sparse recovery [23]. On the other hand, inherent symmetries of the acquisition setup can be exploited to reduce the necessary memory [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Model-Based Inversion in Cross-Sectional Optoacoustic Tomography

Ding

Deán‐Ben

Razansky

2016

IEEE Trans. Med. Imaging

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Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.

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Sparsity‐based acoustic inversion in cross‐sectional multiscale optoacoustic imaging

Cited by 32 publications

References 40 publications

Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model

Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model

Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology

Real-Time Model-Based Inversion in Cross-Sectional Optoacoustic Tomography

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