Multiple projection optical diffusion tomography with plane wave illumination

Markel, Vadim A.; Schotland, John C.

doi:10.1088/0031-9155/50/10/012

Cited by 8 publications

(10 citation statements)

References 28 publications

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“…Inversion of this forward model results in a reconstructed image, but is difficult since the problem is ill-posed and underdetermined. The problem is, therefore, often approached using very large detection arrays and computationally complex reconstruction methods, which have recently demonstrated improvements in resolution (Arridge 1999, Markel and Schotland 2005, Schweiger et al 2003. An important factor in the definition of a forward model is the incorporation of a priori optical properties of the tissue to be studied.…”

Section: Optical Breast Imagingmentioning

confidence: 99%

Refractive index of carcinogen-induced rat mammary tumours

2006

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Near-infrared optical techniques for clinical breast cancer screening in humans are rapidly advancing. Based on the computational inversion of the photon diffusion process through the breast, these techniques rely on optical tissue models for accurate image reconstruction. Recent interest has surfaced regarding the effect of refractive index variations on these reconstructions. Although many data exist regarding the scattering and absorption properties of normal and diseased tissue, no measurements of refractive index appear in the literature. In this paper, we present near-infrared refractive index data acquired from N-methyl-N-nitrosourea-induced rat mammary tumours, which are similar in pathology and disease progression to human ductal carcinoma. Eight animals, including one control, were employed in this study, yielding data from 32 tumours as well as adjacent adipose and connective tissues.

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Section: Optical Breast Imagingmentioning

confidence: 99%

Refractive index of carcinogen-induced rat mammary tumours

2006

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“…Toward this end, we point to recent research using linear methods for DOT and related areas where the results presented here may prove relevant [4,9,14,16,18,19,30]. Moreover, we emphasize that the methods we present in this paper are really only loosely dependent upon the specific form of the Green's functions.…”

Section: Introductionmentioning

confidence: 73%

Analysis and Exploitation of Matrix Structure Arising in Linearized Optical Tomographic Imaging

Hyde¹,

Kilmer²,

Brooks³

et al. 2008

SIAM J. Matrix Anal. & Appl.

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We present a novel method by which the large dense forward matrix A involved in a linear inverse diffusion problem can be decomposed into a number of sparse easily computed matrices. We begin by introducing an errorless decomposition which is applicable to a wide array of such imaging problems. Next, we incorporate interpolation into the construction of the matrices to reduce the computational complexity involved in the matrix-vector multiplications necessary to obtain an inverse solution. Error and computational complexity analysis are provided to support these developments. We then present numerical results that illustrate the gain in computational efficiency when the approximation is used in the Tikhonov regularized inverse problem, and show that the use of the approximation has virtually no negative effect on the quality of the reconstructed images. Finally, we discuss applicability to other imaging problems.

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“…Experiments in OT are usually performed with point sources (plane-wave [12] or structured [13] illumination have also been proposed). A point source can be written as q(r) = q 0 δ(r − r s ).…”

Section: Derivation Of the Born Seriesmentioning

confidence: 99%