Photon transport models for predictive assessment of imageability

Krishnan, Kasiraman; Lomnes, Stephen J.; Kollegal, Manohar; Joshi, Amey; Healey, Andrew

doi:10.1117/12.630796

Cited by 1 publication

(3 citation statements)

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“…The average mesh resolution should be higher than the mean free path close to the source in the region of interest ͑the average value of node-to-node distance Ͻmfp͒. 37 2. The distance from the source to the edge of the phantom should be at least Ͼ5 * penetration depth.…”

Section: Mesh Generationmentioning

confidence: 99%

“…Our earlier studies to map the validity of using an FE 3-D diffusion model as an engineering tool for the predictive assessment of imageability through a systematic comparison of depth-resolved fluence calculated by diffusion equation and the gold standard Monte Carlo model had shown that although, in theory, the diffusion model applies when s Јӷ a , in practice, its performance can vary sharply for the same s Ј/ a ratio. 37,38 We summarized the rules of thumb for applicability of the FE diffusion equation for excitation fluence as:…”

Section: Mesh Generationmentioning

confidence: 99%

“…37,38 We first create the boundary of the mouse phantom from MRI data using a series of image processing steps. We next discretize the geometry with an FE descriptor using FEMLAB.…”

Section: Introductionmentioning

confidence: 99%

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Digital mouse phantom for optical imaging

Dwivedi¹,

Krishnan²,

Sajja³

2007

J. Biomed. Opt.

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We present a method for design and use of a digital mouse phantom for small animal optical imaging. We map the boundary of a mouse model from magnetic resonance imaging (MRI) data through image processing algorithms and discretize the geometry by a finite element (FE) descriptor. We use a validated FE implementation of the three-dimensional (3-D) diffusion equation to model transport of near infrared (NIR) light in the phantom with a mesh resolution optimized for representative tissue optical properties on a computing system with 8-GB RAM. Our simulations demonstrate that a section of the mouse near the light source is adequate for optical system design and that the variation of intensity of light on the boundary is well within typical noise levels for up to 20% variation in optical properties and nodes used to model the boundary of the phantom. We illustrate the use of the phantom in setting goals for specific binding of targeted exogenous fluorescent contrasts based on anatomical location by simulating a nearly tenfold change in the detectability of a 2-mm-deep target depending on its placement. The methodology described is sufficiently general and may be extended to generate digital phantoms for designing clinical optical imaging systems.

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Section: Mesh Generationmentioning

confidence: 99%

Section: Mesh Generationmentioning

confidence: 99%