This project aims to improve the estimation of functional properties of breast tissue in near infrared (NIR) imaging. This imaging technique (also known as diffuse optical tomography (DOT)) is noninvasive and non-ionizing, and can be routinely used to characterize the breast tissue. In this technique, fibers placed on the boundary of the breast deliver NIR light (600 nm to 950 nm) and collect the propagated diffused light (the patient fiber-optic setup is shown in the photo). The attenuation and scattering of light through breast tissue volume provide an estimation of functional properties using a model-based approach.

The Fiber Optic Setup consists of fibers placed on the boundary of the breast, delivering NIR light, and collecting the propagated diffused light.
The image resolution and contrast in NIR tomographic image reconstruction are affected by parameters such as the number of boundary measurements, the mesh resolution in the forward calculation, and the reconstruction basis. The magnitude of the total sensitivity was analyzed to find the spatial variation for a given problem, and the field response of the domain becomes more uniform by increasing the sensitivity to deeper regions while suppressing the hypersensitivity near the external boundaries. This is achieved by increasing the number of measurements.

This project is part of a continuing effort to develop methods and algorithms for three-dimensional alternative breast imaging modalities. One important milestone in the project was completing the work on optimizing the NIR data-collection strategies in 3D. A framework to incorporate the spatial-priors into the NIR image reconstruction procedure was developed and also proven to be effective, even in case of imperfect spatial priors. Moreover, a new algorithm that takes into account noise characteristics of the instruments was developed and tested extensively in the simulation studies. Preliminary 3D reconstruction results using this new algorithm show improved quantitative accuracy compared to the traditional image reconstruction techniques.

This work was done by Phaneendra K. Yalavarthy of Dartmouth College for the U.S. Army Medical Research and Materiel Command. ARL-0062

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Diagnostics Education and training Fibers Imaging and visualization Mathematical models Medical Noise Optics Optimization People and personalities Research and development Simulation and modeling
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This article first appeared in the September, 2009 issue of Medical Design Briefs Magazine (Vol. 33 No. 9).

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