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

VL - 14

JO - Journal of Biomedical Optics

JF - Journal of Biomedical Optics

SN - 1083-3668

IS - 6

M1 - 064003

ER -