The overall hypothesis in this proposal is emerging hybrid imaging systems that bring spectroscopy into imaging require computational tools which currently do not exist. Specifically, a boundary element method (BEM) algorithm can be implemented to perform three dimensional (3D) image reconstruction for image-guided near infrared (IG-NIR) tomography of breast tissue and fluorescence tomography in small animals. This system intelligently utilizes anatomical structures from MRI to guide NIR spectroscopy (NIRS) to improve diagnosis of breast cancer;and CT tissue structure to guide NIRS allowing accurate recovery of fluorescence uptake. Both MR and CT approaches to IG-NIRS traditionally rely on numerical models to the diffusion equation requiring volume discretization (such as finite element). In this proposal, we will solve the diffusion equation using the BEM (requiring only surface discretization) and apply it for 3D image reconstruction, assuming that the underlying tissue boundaries can be obtained a priori from MRI or CT. In particular, a computational toolbox will be developed that seamlessly creates surface meshes for different tissue layers such as adipose, fibroglandular and tumor, using MRI images. The toolbox will use these grids along with NIR measurements to reconstruct 3D tissue vascular estimates of total hemoglobin, oxygen saturation and water;and cellular estimates of scatterer size and number density in each tissue layer. A fluorescence toolbox will also be developed that obtains surface grids from MicroCT images and solves a set of coupled diffusion equations simultaneously using BEM to recover 3D fluorescence values in different tissue organs of small animals. These toolboxes together with a graphical user interface allowing 3D image visualization and juxtaposition of NIR, MRI and MicroCT images, will provide easy-to-use boundary element software for different research groups utilizing hybrid imaging techniques. Leveraging the clinical data from an ongoing breast imaging trial, we propose to analyze the results from 3D boundary element tissue estimates of 50 patients to explore the sensitivity and specificity measures of this technique for tissue diagnosis as well as its potential to study cancer non-invasively. In-vivo measurements from small animals imaged in a CT-fluorescence setting will also be available through a separate funded project, for testing of BEM molecular imaging. This novel BEM toolbox with its strengths over volume discretization methods such as FEM and its computational efficiency in solving the image-guided reconstruction problem will set the standard for 3D optical imaging. This will further the use of MRI-NIR 3D imaging as an everyday diagnostic tool providing non-invasive high-resolution functional characterization of diseased tissue. Two versions of the toolbox will plan to be developed, one which is more advanced and can be translated into a commercial version through interaction with ART Inc, and at the same time, a open access version will be distributed which allows novice users in the field of NIRS to set up new and evolving tools which use the BEM toolbox.

Public Health Relevance

A hybrid MRI-near-infrared (NIR) system has the potential to reduce false-positives and the number of follow-up invasive procedures in breast cancer diagnosis using complementary information from optical signatures. The computational toolbox proposed here will provide a powerful and efficient method for viable and more accurate three-dimensional imaging of large clinical subject populations in this framework. Overall, this will further advance the study of high-resolution optical signatures of normal and diseased breast tissue in-vivo and fluorescence imaging for studying biochemical and cellular mechanisms in-vivo.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Zhang, Yantian
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Dartmouth College
Biomedical Engineering
Schools of Engineering
United States
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