Noncontact fluorescence diffuse optical tomography of heterogeneous media

Hervé, Lionel; Koenig, Anne; Silva, A. Da; Berger, Michel; Boutet, J.; Dinten, Jean‐Marc; Peltié, Philippe; Rizo, P.

doi:10.1364/ao.46.004896

Cited by 72 publications

(63 citation statements)

References 20 publications

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“…However, background optical heterogeneity correction using recovered optical property map from DOT is demonstrated to provide much better accuracy than the Born normalization method. 38 Our study also confirms that better quantitative accuracy is achieved when the absorption and scattering maps of the medium are reconstructed from DOT and used in FT reconstruction. Perhaps in most small animal imaging applications, the Born normalization method is sufficient to correct background optical heterogeneity in FT reconstruction.…”

Section: Resultssupporting

confidence: 79%

In vivovalidation of quantitative frequency domain fluorescence tomography

et al. 2012

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Section: Resultssupporting

confidence: 79%

In vivovalidation of quantitative frequency domain fluorescence tomography

et al. 2012

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“…This coefficient is then used to reconstruct the fluorophore distribution. 12,13 Another approach involves combining previously published values for tissue-specific optical parameters with high-resolution anatomical information provided by CT or MRI; however, there is no consensus in the literature on the actual values of tissue-specific optical parameters, and considerable inter-subject variability is reported. 14 An alternative approach 15 simplifies the problem by assuming homogeneous optical properties and normalizing fluorescence photon density to excitation photon density following the so-called normalized Born approximation.…”

Section: Introductionmentioning

confidence: 99%

Influence of absorption and scattering on the quantification of fluorescence diffuse optical tomography using normalized data

et al. 2012

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Abstract. Reconstruction algorithms for imaging fluorescence in near infrared ranges usually normalize fluorescence light with respect to excitation light. Using this approach, we investigated the influence of absorption and scattering heterogeneities on quantification accuracy when assuming a homogeneous model and explored possible reconstruction improvements by using a heterogeneous model. To do so, we created several computer-simulated phantoms: a homogeneous slab phantom (P1), slab phantoms including a region with a two-to six-fold increase in scattering (P2) and in absorption (P3), and an atlas-based mouse phantom that modeled different liver and lung scattering (P4). For P1, reconstruction with the wrong optical properties yielded quantification errors that increased almost linearly with the scattering coefficient while they were mostly negligible regarding the absorption coefficient. This observation agreed with the theoretical results. Taking the quantification of a homogeneous phantom as a reference, relative quantification errors obtained when wrongly assuming homogeneous media were in the range þ41 to þ94% (P2), 0.1 to −7% (P3), and −39 to þ44% (P4). Using a heterogeneous model, the overall error ranged from −7 to 7%. In conclusion, this work demonstrates that assuming homogeneous media leads to noticeable quantification errors that can be improved by adopting heterogeneous models.

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“…This results in a linear forward model [21], which is the context of the present paper. A number of methods that include regularization have been studied for linear reconstruction in optical tomography; the Kaczmarz method (ART) with an appropriate stopping criterion, the filtered singular value decomposition and Tikhonov regularization [22]- [25]. In this contribution, we propose general regularization as a new regularization technique for the reconstruction of fluorescence data, together with an optimization method developed specifically for regularization.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Numerical Method for General $L_{p}$ Regularization in Fluorescence Molecular Tomography

Baritaux

Hassler

Unser

2010

IEEE Trans. Med. Imaging

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Abstract-Reconstruction algorithms for fluorescence tomography have to address two crucial issues: 1) the ill-posedness of the reconstruction problem, 2) the large scale of numerical problems arising from imaging of 3-D samples. Our contribution is the design and implementation of a reconstruction algorithm that incorporates general regularization . The originality of this work lies in the application of general constraints to fluorescence tomography, combined with an efficient matrix-free strategy that enables the algorithm to deal with large reconstruction problems at reduced memory and computational costs. In the experimental part, we specialize the application of the algorithm to the case of sparsity promoting constraints . We validate the adequacy of regularization for the investigation of phenomena that are well described by a sparse model, using data acquired during phantom experiments.

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Noncontact fluorescence diffuse optical tomography of heterogeneous media

Cited by 72 publications

References 20 publications

In vivovalidation of quantitative frequency domain fluorescence tomography

In vivovalidation of quantitative frequency domain fluorescence tomography

Influence of absorption and scattering on the quantification of fluorescence diffuse optical tomography using normalized data

An Efficient Numerical Method for General $L_{p}$ Regularization in Fluorescence Molecular Tomography

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