Research Papers

Parametric image reconstruction using the discrete cosine transform for optical tomography

[+] Author Affiliations
Xuejun Gu

Columbia University, Department of Biomedical Engineering, 351 Engineering Terrace, MC8904, 1210 Amsterdam Avenue, New York, New York 10027

Kui Ren

University of Texas at Austin, Department of Mathematics, 1 University Station, C1200, Austin, Texas 78712

James Masciotti, Andreas H. Hielscher

Columbia University, Department of Biomedical Engineering, 351 Engineering Terrace, MC8904, 1210 Amsterdam Avenue, New York, New York 10027 and Columbia University, Department of Radiology, College of Physicians & Surgeons, MC28, 630 W 168th Street, New York, New York 10027

J. Biomed. Opt. 14(6), 064003 (December 03, 2009). doi:10.1117/1.3259360
History: Received February 11, 2009; Revised July 17, 2009; Accepted September 08, 2009; Published December 03, 2009; Online December 03, 2009
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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.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Xuejun Gu ; Kui Ren ; James Masciotti and Andreas H. Hielscher
"Parametric image reconstruction using the discrete cosine transform for optical tomography", J. Biomed. Opt. 14(6), 064003 (December 03, 2009). ; http://dx.doi.org/10.1117/1.3259360


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