Model-based correction of finite aperture effect in photoacoustic tomography

Li, Menglin; Tseng, Yi-Chieh; Cheng, Chung-Chih

doi:10.1364/oe.18.026285

Cited by 34 publications

(36 citation statements)

References 16 publications

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“…This assertion, however, is only true when the inversion procedure used is based on the assumption of point detectors. It has been recently shown that if the detector’s aperture is accounted for in model-based inversion, the effect of the detector’s aperture on the reconstruction may be significantly mitigated, leading to enhanced resolution [74, 124]. Alternatively, post-reconstruction processing techniques such as spin deblurring may be used to undo the effect of the detector’s PSF [125].…”

Section: Detector’s Aperturementioning

confidence: 99%

Acoustic Inversion in Optoacoustic Tomography: A Review

Rosenthal¹,

Ntziachristos²,

Razansky

2014

CMIR

211

188

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Optoacoustic tomography enables volumetric imaging with optical contrast in biological tissue at depths beyond the optical mean free path by the use of optical excitation and acoustic detection. The hybrid nature of optoacoustic tomography gives rise to two distinct inverse problems: The optical inverse problem, related to the propagation of the excitation light in tissue, and the acoustic inverse problem, which deals with the propagation and detection of the generated acoustic waves. Since the two inverse problems have different physical underpinnings and are governed by different types of equations, they are often treated independently as unrelated problems. From an imaging standpoint, the acoustic inverse problem relates to forming an image from the measured acoustic data, whereas the optical inverse problem relates to quantifying the formed image. This review focuses on the acoustic aspects of optoacoustic tomography, specifically acoustic reconstruction algorithms and imaging-system practicalities. As these two aspects are intimately linked, and no silver bullet exists in the path towards high-performance imaging, we adopt a holistic approach in our review and discuss the many links between the two aspects. Four classes of reconstruction algorithms are reviewed: time-domain (so called back-projection) formulae, frequency-domain formulae, time-reversal algorithms, and model-based algorithms. These algorithms are discussed in the context of the various acoustic detectors and detection surfaces which are commonly used in experimental studies. We further discuss the effects of non-ideal imaging scenarios on the quality of reconstruction and review methods that can mitigate these effects. Namely, we consider the cases of finite detector aperture, limited-view tomography, spatial under-sampling of the acoustic signals, and acoustic heterogeneities and losses.

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Section: Detector’s Aperturementioning

confidence: 99%

Acoustic Inversion in Optoacoustic Tomography: A Review

Rosenthal¹,

Ntziachristos²,

Razansky

2014

CMIR

211

188

View full text Add to dashboard Cite

show abstract

“…To simultaneously reduce the imaging artifacts and grating lobes, this built model will be used for CS reconstruction. The detected signal produced by a linear array at a given channel Ct that arises from a point optical absorber at position r in PA linear array imaging can be expressed as [4]:…”

Section: A Linear and Discrete P A Linear Array Imaging Model For Csmentioning

confidence: 99%

“…Thus, we can add a new constraint directly to the inverse problem of Eq. (3) for PA linear array imaging [5][6]: (4) where e is the parameter that controls the fidelity of the reconstruction to y. By using the nonlinear recovery algorithm based on convex optimization, P A linear array imaging can be reconstructed.…”

Section: Bmentioning

confidence: 99%

Compressive-sensing like grating-lobe suppressed image reconstruction for photoacoustic linear array imaging

Chiu

2014

2014 IEEE International Ultrasonics Symposium

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To avoid large grating lobes, using a small element-to-element pitch of ultrasound array transducers for photoacoustic (PA) imaging is necessary. Such constraint introduces higher system cost and complexity, especially when greater than 20-MHz high-frequency arrays are used. As a result, to reduce fabrication difficulties and obtain better signal sensitivity in P A imaging, ultrasound linear array transducers are commonly used in practice instead of phased arrays. However, the field-of-view (FOV) is limited to the full aperture size because linear arrays do not have the ability to steer PA receive beams without the introduction of large grating lobes. In addition, strong P A signals are commonly generated in the near field in the back-ward mode where grating-lobe clutters can even hamper the image contrast seriously. In this study, we proposed a novel compressed-sensing-like grating-lobe suppressed image reconstruction method for P A linear array imaging. To overcome the tradeoff between FOV and grating lobe clutters introduced by using a linear array, compressive sensing (CS) concept is adopted here to reduce the grating lobes. The CS theory relies on an important principle: sparsity. Fortunately, unlike ultrasound imaging, absorption distribution in P A imaging intrinsically owns sparsity in the spatial domain. In consequence, a sparsity constraint minimizing the Ll norm of energy deposition can be introduced to the conventional reconstruction method. By adopting such a constraint and using the nonlinear recovery algorithm based on convex optimization, P A linear array imaging can be reconstructed with grating lobe clutters greatly suppressed.Simulation results demonstrated that the proposed method can reduce the grating lobes caused by using a linear array with large FOV. In the meantime, compared with the image reconstructed by the traditional back-projection method, the image reconstructed by the proposed method has fewer artifacts.

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“…Both the finite apertures and the finite bandwidths of the detectors can cause blurring of the reconstructed images 35,36 . How-ever, if the properties of the detectors are taken into account in the model-based inversion, the blurring may be significantly reduced leading to an enhanced image quality [37][38][39] . In this work, the finite sizes and bandwidths of the acoustic detectors are modeled and included into the solution of the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Image reconstruction with uncertainty quantification in photoacoustic tomography

Tick

Pulkkinen

Tarvainen

2016

The Journal of the Acoustical Society of America

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Photoacoustic tomography is a hybrid imaging method that combines optical contrast and ultrasound resolution. The goal of photoacoustic tomography is to resolve an initial pressure distribution from detected ultrasound waves generated within an object due to an illumination of a short light pulse. In this work, a Bayesian approach to photoacoustic tomography is described. The solution of the inverse problem is derived and computation of the point estimates for image reconstruction and uncertainty quantification is described. The approach is investigated with simulations in different detector geometries, including limited view setup, and with different detector properties such as ideal point-like detectors, finite size detectors, and detectors with a finite bandwidth. The results show that the Bayesian approach can be used to provide accurate estimates of the initial pressure distribution, as well as information about the uncertainty of the estimates.

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Model-based correction of finite aperture effect in photoacoustic tomography

Cited by 34 publications

References 16 publications

Acoustic Inversion in Optoacoustic Tomography: A Review

Acoustic Inversion in Optoacoustic Tomography: A Review

Compressive-sensing like grating-lobe suppressed image reconstruction for photoacoustic linear array imaging

Image reconstruction with uncertainty quantification in photoacoustic tomography

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