Fluorescence optical diffusion tomography using multiple-frequency data

Milstein, Adam B.; Stott, Jonathan J.; Oh, Seungseok; Boas, David A.; Millane, Rick P.; Bouman, Charles A.; Webb, Kevin J.

doi:10.1364/josaa.21.001035

Cited by 85 publications

(68 citation statements)

References 51 publications

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“…Given N s sources and N d detectors, the measurement Γ i,j of the jth detector due to ith source is given as follows [1],…”

Section: Fdot Imaging Problemmentioning

confidence: 99%

“…The inverse problem of fluorescence diffusse optical tomography (FDOT) requires reconstruction of 2D or 3D fluorophore map inside the imaging domain using boundary measurements obtained at the emission and excitation wavelengths [1]. However, the number of measurements available is usually insufficient.…”

Section: Introductionmentioning

confidence: 99%

“…However, the number of measurements available is usually insufficient. Furthermore, the solution is not stable, i.e., a small perturbation in the measurement data can result in large deviations in the solution [1]. Thus, regularization techniques are usually necessary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined L<inf>2</inf>-L<inf>1</inf>-norm regularization in fluorescence diffuse optical tomography

Yazıcı

2011

2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC)

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The inverse problem of fluorescence diffusse optical tomography (FDOT) is often highly ill-posed, which needs regularization techniques. In this paper, we propose a combined l 1 -l 2 -norm regularization method to address the ill-posed FDOT inverse problem. Compared with the traditional Tikhonov regularization, the proposed method is able to effectively remove the noise in the reconstructed image without much over-smoothness. The performance of the proposed method is demonstrated in 3D numerical simulation.

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“…Given N s sources and N d detectors, the measurement Γ i,j of the jth detector due to ith source is given as follows [1],…”

Section: Fdot Imaging Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combined L<inf>2</inf>-L<inf>1</inf>-norm regularization in fluorescence diffuse optical tomography

Yazıcı

2011

2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC)

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“…This work uses an exponential curve model for compartmental modeling and a linear time-invariant fluorescence light propagation model derived based on authors' prior work in [26] and [27]. The reconstruction of pharmacokinetic-rate images is addressed based on the maximum a posteriori (MAP) estimation together with a parametric iterative coordinate descent optimization technique similar to the approach reported in [13].…”

Section: B Related Work and The Advantages Of Our Approachmentioning

confidence: 99%

“…This model is given as (27) where , are the indices of the voxels in the neighborhood of the th voxel; , are the spatial weighting coefficients, which may be different for each pharmacokinetic-rate or volume fraction image; and , is a zero-mean Gaussian process with , . The weighting coefficients may be spatially varying and can be chosen based on a variety of physiological information, i.e., tumor location, size, or shape.…”

Section: A a Priori Model For Pharmacokinetic Rates And Volume Fractmentioning

confidence: 99%

Direct Reconstruction of Pharmacokinetic-Rate Images of Optical Fluorophores From NIR Measurements

Alacam

Yazıcı

2009

IEEE Trans. Med. Imaging

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Abstract-In this paper, we present a new method to form pharmacokinetic-rate images of optical fluorophores directly from near infra-red (NIR) boundary measurements. We first derive a mapping from spatially resolved pharmacokinetic rates to NIR boundary measurements by combining compartmental modeling with a diffusion based NIR photon propagation model. We express this mapping as a state-space equation. Next, we introduce a spatio-temporal prior model for the pharmacokinetic-rate images and combine it with the state-space equation. We address the image formation problem using the extended Kalman filtering framework. We analyze the computational complexity of the resulting algorithms and evaluate their performance in numerical simulations. An important feature of our approach is that the reconstruction of fluorescence concentrations and compartmental modeling are combined into a single step 1) to take advantage of the inherent temporal correlations in dynamic NIR measurements, and 2) to incorporate spatio-temporal a priori information on pharmacokinetic-rate images. Simulation results show that the resulting algorithms are more robust and lead to higher signal-to-noise ratio as compared to existing approaches where the reconstruction of concentrations and compartmental modeling are treated separately. Additionally, we reconstructed pharmacokinetic-rate images using in vivo data obtained from three patients with breast tumors. The reconstruction results show that the pharmacokinetic rates of indocyanine green are higher inside the tumor region as compared to the surrounding tissue.

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A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging

Zhao

Pelegri

2015

Numer Methods Biomed Eng

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Biomechanical imaging techniques based on acoustic radiation force (ARF) have been developed to characterize the viscoelasticity of soft tissue by measuring the motion excited by ARF non-invasively. The unknown stress distribution in the region of excitation limits an accurate inverse characterization of soft tissue viscoelasticity, and single degree-of-freedom simplified models have been applied to solve the inverse problem approximately. In this study, the ARF-induced creep imaging is employed to estimate the time constant of a Voigt viscoelastic tissue model, and an inverse finite element (FE) characterization procedure based on a Bayesian formulation is presented. The Bayesian approach aims to estimate a reasonable quantification of the probability distributions of soft tissue mechanical properties in the presence of measurement noise and model parameter uncertainty. Gaussian process metamodeling is applied to provide a fast statistical approximation based on a small number of computationally expensive FE model runs. Numerical simulation results demonstrate that the Bayesian approach provides an efficient and practical estimation of the probability distributions of time constant in the ARF-induced creep imaging. In a comparison study with the single degree of freedom models, the Bayesian approach with FE models improves the estimation results even in the presence of large uncertainty levels of the model parameters.

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Fluorescence optical diffusion tomography using multiple-frequency data

Cited by 85 publications

References 51 publications

Combined L<inf>2</inf>-L<inf>1</inf>-norm regularization in fluorescence diffuse optical tomography

Combined L<inf>2</inf>-L<inf>1</inf>-norm regularization in fluorescence diffuse optical tomography

Direct Reconstruction of Pharmacokinetic-Rate Images of Optical Fluorophores From NIR Measurements

A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging

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