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Self-calibrated algorithms for diffuse optical tomography and bioluminescence tomography using relative transmission
Mohamed A Naser1, Michael S Patterson, John W Wong
1Department of Medical Physics and Applied Radiation Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S4K1, Canada.
Biomedical Optics Express
|November 20, 2012
Summary
Developed reconstruction algorithms for diffuse optical tomography (DOT) and bioluminescence tomography (BLT) accurately reconstruct imaging data. These methods, utilizing diffusion theory and finite element analysis, show good agreement with actual values, even with relative data.
Area of Science:
- Biomedical Imaging
- Optical Physics
- Computational Modeling
Background:
- Diffuse optical tomography (DOT) and bioluminescence tomography (BLT) are powerful imaging techniques.
- Accurate reconstruction algorithms are crucial for reliable imaging results.
- Existing methods often face challenges with calibration and geometric factors.
Purpose of the Study:
- To develop and validate novel reconstruction algorithms for DOT and BLT.
- To improve the accuracy and robustness of tomographic imaging.
- To enable self-calibration for both DOT and BLT.
Main Methods:
- Numerical solution of the diffusion equation using the finite element method.
- Direct measurement of uncalibrated light fluence rates.
- Self-calibration strategies for DOT using transmission images and Jacobian calculations.
- Wavelength-specific calibration of BLT using DOT transmission measurements.
- Application to a 3D mouse model (MOBY) with segmented tissue regions.
Main Results:
- Reconstruction algorithms demonstrated good agreement with actual values for both DOT and BLT.
- Self-calibration methods effectively canceled geometrical and optical factors.
- Reconstructions using relative data were robust.
- Absolute data reconstructions were sensitive to small calibration errors.
Conclusions:
- The developed DOT and BLT algorithms provide accurate and self-calibrated reconstructions.
- Relative data is more robust to calibration errors than absolute data.
- These algorithms represent a significant advancement in optical tomography imaging.
