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Published on: July 17, 2012
Optimal Illumination Patterns for Fluorescence Tomography
Joyita Dutta1, Sangtae Ahn, Anand A Joshi
1Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089, USA.
Abstract:
Fluorescence tomography has become increasingly popular for detecting molecular targets for imaging gene expression and other cellular processes in vivo in small animal studies. In this imaging modality, multiple sets of data are acquired by illuminating the animal surface with different excitation patterns, each of which produces a distinct spatial pattern of fluorescence. This work addresses one of the most intriguing, yet unsolved, problems of fluorescence tomography, which is to determine how to optimally illuminate the animal surface so as to maximize the information content in the acquired data. The key idea of this work is to parameterize the illumination pattern and to maximize the information content in the data by improving the conditioning of the Fisher information matrix. We formulate our problem as a constrained optimization problem. We compare the performance of different geometric illumination schemes with those generated by this optimization approach using the Digimouse atlas.
Insights
This study optimizes illumination patterns for fluorescence tomography, enhancing in vivo molecular imaging in small animals. The new method maximizes data information content for clearer cellular process visualization.
Area of Science:
- Biomedical Imaging
- Optical Imaging
- Molecular Imaging
Background:
- Fluorescence tomography is vital for in vivo molecular target detection and imaging cellular processes in small animals.
- Acquiring data involves illuminating the animal with various excitation patterns to capture distinct fluorescence spatial patterns.
Purpose of the Study:
- To solve the problem of optimally illuminating animal surfaces in fluorescence tomography.
- To maximize information content in acquired data by optimizing illumination patterns.
Main Methods:
- Parameterizing illumination patterns for fluorescence tomography.
- Formulating the problem as a constrained optimization task.
- Improving the conditioning of the Fisher information matrix to maximize data information content.
Main Results:
- Developed an optimization approach for illumination patterns in fluorescence tomography.
- Compared optimized geometric illumination schemes against standard methods using the Digimouse atlas.
- Demonstrated improved information content through optimized illumination.
Conclusions:
- The proposed optimization method effectively determines optimal illumination patterns for fluorescence tomography.
- This approach enhances the quality and information content of in vivo molecular imaging data.
- Further application of this method can advance small animal imaging studies.
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