TY - JOUR
T1 - Parametric image reconstruction using the discrete cosine transform for optical tomography
AU - Gu, Xuejun
AU - Ren, Kui
AU - Masciotti, James
AU - Hielscher, Andreas H.
N1 - Funding Information:
This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering (NIBIB—Grant No. R01 EB001900) and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS—Grant No. 2R01-AR046255), both of which are part of the National Institutes of Health (NIH).
PY - 2009
Y1 - 2009
N2 - It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.
AB - It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.
KW - Discrete cosine transform
KW - Equation of radiative transfer
KW - Optical tomography
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U2 - 10.1117/1.3259360
DO - 10.1117/1.3259360
M3 - Article
C2 - 20059241
AN - SCOPUS:77950347108
SN - 1083-3668
VL - 14
JO - Journal of biomedical optics
JF - Journal of biomedical optics
IS - 6
M1 - 064003
ER -