Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Mozumder, Meghdoot; Tarvainen, Tanja; Arridge, Simon; Kaipio, Jari P.; Kolehmainen, Ville

doi:10.1364/boe.4.002015

Cited by 18 publications

(17 citation statements)

References 28 publications

(62 reference statements)

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“…Furthermore, it has recently been proposed for marginalization of the speed of sound in PAT [18]. In other optical and ultrasonic imaging applications it has been applied, for example, in compensating of errors due to discretization [24], reduction of the physical model [25], domain truncation [26], [27], uncertainties related to geometry and shape [28], [29], and uncertainties in model parameters [30], [31]. Outside biomedical imaging, it has been utilized in other acoustical modeling and inverse problems such as model reduction in aquifer dimension estimation from seismic signals [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography

Sahlström

Pulkkinen

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et al. 2020

IEEE Trans. Med. Imaging

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Photoacoustic tomography is an imaging modality based on the photoacoustic effect caused by the absorption of an externally introduced light pulse. In the inverse problem of photoacoustic tomography, the initial pressure generated through the photoacoustic effect is estimated from a measured photoacoustic time-series utilizing a forward model for ultrasound propagation. Due to the ill-posedness of the inverse problem, errors in the forward model or measurements can result in significant errors in the solution of the inverse problem. In this work, we study modeling of errors caused by uncertainties in ultrasound sensor locations in photoacoustic tomography using a Bayesian framework. The approach is evaluated with simulated and experimental data. The results indicate that the inverse problem of photoacoustic tomography is sensitive even to small uncertainties in sensor locations. Furthermore, these uncertainties can lead to significant errors in the estimates and reduction of the quality of the photoacoustic images. In this work, we show that the errors due to uncertainties in ultrasound sensor locations can be modeled and compensated using Bayesian approximation error modeling. Index Terms Photoacoustic tomography (PAT), inverse problems, Bayesian methods, error modeling.

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Section: Introductionmentioning

confidence: 99%

Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography

Sahlström

Pulkkinen

Tick

et al. 2020

IEEE Trans. Med. Imaging

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“…Previously, BAE modeling has been utilized in QPAT in reduction of modeling errors caused by marginalization of scattering coefficient [17] and compensation of inaccuracies due to the numerical approximation of an acoustic solver [40]. In other optical and acoustic imaging modalities, BAE approach has been utilized, for example in diffuse optical tomography in model reduction [41][42][43][44], and compensating uncertainties in optode positions and boundary shape [45][46][47] and in full-waveform ultrasound tomography in compensating errors due to reduced discretization and approximate boundary models [48].…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction with Reliability Assessment in Quantitative Photoacoustic Tomography

2018

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Quantitative photoacoustic tomography is a novel imaging method which aims to reconstruct optical parameters of an imaged target based on initial pressure distribution, which can be obtained from ultrasound measurements. In this paper, a method for reconstructing the optical parameters in a Bayesian framework is presented. In addition, evaluating the credibility of the estimates is studied. Furthermore, a Bayesian approximation error method is utilized to compensate the modeling errors caused by coarse discretization of the forward model. The reconstruction method and the reliability of the credibility estimates are investigated with two-dimensional numerical simulations. The results suggest that the Bayesian approach can be used to obtain accurate estimates of the optical parameters and the credibility estimates of these parameters. Furthermore, the Bayesian approximation error method can be used to compensate for the modeling errors caused by a coarse discretization, which can be used to reduce the computational costs of the reconstruction procedure. In addition, taking the modeling errors into account can increase the reliability of the credibility estimates.

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“…These statistics are then used in the image reconstruction process to compensate for the modeling errors. [23][24][25][26][27][28] Nevertheless, the most popular technique for in vivo DOT imaging has been difference imaging where the objective is to reconstruct change in the optical properties using measurements before and after the change. Conventionally, the image reconstruction is carried out using the difference of the measurements and a linearized approximation of the observation model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear approach to difference imaging in diffuse optical tomography

et al. 2015

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Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors.

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Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Cited by 18 publications

References 28 publications

Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography

Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography

Image Reconstruction with Reliability Assessment in Quantitative Photoacoustic Tomography

Nonlinear approach to difference imaging in diffuse optical tomography

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