Tumor characterization in small animals using magnetic resonance-guided dynamic contrast enhanced diffuse optical tomography

Lin, Yuting; Thayer, David; Nalcioğlu, Orhan; Gülşen, Gültekin

doi:10.1117/1.3643342

Cited by 17 publications

(12 citation statements)

References 18 publications

(18 reference statements)

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“…Recently, it has been applied in breast cancer imaging or optical mammography [1][2][3] as well as in small animal imaging. [4][5][6] The 3-D DOT has been proposed by various groups [7][8][9][10][11][12][13][14] for imaging brain function.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

et al. 2014

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Functional near-infrared spectroscopy (fNIRS) is an optical method for noninvasively determining brain activation by estimating changes in the absorption of near-infrared light. Diffuse optical tomography (DOT) extends fNIRS by applying overlapping “high density” measurements, and thus providing a three-dimensional imaging with an improved spatial resolution. Reconstructing brain activation images with DOT requires solving an underdetermined inverse problem with far more unknowns in the volume than in the surface measurements. All methods of solving this type of inverse problem rely on regularization and the choice of corresponding regularization or convergence criteria. While several regularization methods are available, it is unclear how well suited they are for cerebral functional DOT in a semi-infinite geometry. Furthermore, the regularization parameter is often chosen without an independent evaluation, and it may be tempting to choose the solution that matches a hypothesis and rejects the other. In this simulation study, we start out by demonstrating how the quality of cerebral DOT reconstructions is altered with the choice of the regularization parameter for different methods. To independently select the regularization parameter, we propose a cross-validation procedure which achieves a reconstruction quality close to the optimum. Additionally, we compare the outcome of seven different image reconstruction methods for cerebral functional DOT. The methods selected include reconstruction procedures that are already widely used for cerebral DOT [minimum l2-norm estimate (l2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum l1- and a smooth minimum l0-norm estimate (l1MNE, l0MNE, respectively)] and a depth- and noise-weighted minimum norm (wMNE). Furthermore, we expand the range of algorithms for DOT by adapting two EEG-source localization algorithms [sparse basis field expansions and linearly constrained minimum variance (LCMV) beamforming]. Independent of the applied noise level, we find that the LCMV beamformer is best for single spot activations with perfect location and focality of the results, whereas the minimum l1-norm estimate succeeds with multiple targets.

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Section: Introductionmentioning

confidence: 99%

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

et al. 2014

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“…Previous studies have used multimodal FMT to report single-agent fluorophore kinetics in the blood pool (22) and tumor xenografts (23). The present study applies this technique to examine the quantification of specific receptor binding in vivo.…”

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confidence: 99%

Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo

Davis

Samkoe²,

Tichauer

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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The up-regulation of cell surface receptors has become a central focus in personalized cancer treatment; however, because of the complex nature of contrast agent pharmacokinetics in tumor tissue, methods to quantify receptor binding in vivo remain elusive. Here, we present a dual-tracer optical technique for noninvasive estimation of specific receptor binding in cancer. A multispectral MRI-coupled fluorescence molecular tomography system was used to image the uptake kinetics of two fluorescent tracers injected simultaneously, one tracer targeted to the receptor of interest and the other tracer a nontargeted reference. These dynamic tracer data were then fit to a dual-tracer compartmental model to estimate the density of receptors available for binding in the tissue. Applying this approach to mice with deep-seated gliomas that overexpress the EGF receptor produced an estimate of available receptor density of 2.3 ± 0.5 nM (n = 5), consistent with values estimated in comparative invasive imaging and ex vivo studies.

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“…A possible solution is to consider the multi-GPU parallelization. On the other hand, the current GPU solver will allow us to efficiently study a few RTE based inverse problems, [17][18][19][20] so that we can assess the potential gain in the imaging accuracy due to RTE. In addition, we will also spend time to write user documents and develop user-friendly interfaces to share this solver with the community.…”

Section: Discussionmentioning

confidence: 99%

Parallel multigrid solver of radiative transfer equation for photon transport via graphics processing unit

Gao

Phan²,

Lin

2012

J. Biomed. Opt

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Abstract. A graphics processing unit-based parallel multigrid solver for a radiative transfer equation with vacuum boundary condition or reflection boundary condition is presented for heterogeneous media with complex geometry based on two-dimensional triangular meshes or three-dimensional tetrahedral meshes. The computational complexity of this parallel solver is linearly proportional to the degrees of freedom in both angular and spatial variables, while the full multigrid method is utilized to minimize the number of iterations. The overall gain of speed is roughly 30 to 300 fold with respect to our prior multigrid solver, which depends on the underlying regime and the parallelization. The numerical validations are presented with the MATLAB codes at https://sites.google.com/site/ rtefastsolver/.

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Tumor characterization in small animals using magnetic resonance-guided dynamic contrast enhanced diffuse optical tomography

Cited by 17 publications

References 18 publications

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo

Parallel multigrid solver of radiative transfer equation for photon transport via graphics processing unit

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