Abstract
Near-infrared diffuse optical tomography imaging (DOT) suffers from a poor depth resolution due to the depth sensitivity decreases markedly in tissues. In this paper, an intelligent method, which is called layered maximum-singular-values adjustment (LMA), is proposed to compensate the decrease of sensitivity in depth dimension, and hence obtain improved depth resolution of DOT imaging. Simulations are performed with a semi-infinite model, and the simulated results for objects located in different depths demonstrate that the LMA technique can improve significantly the depth resolution of reconstructed objects. The positional errors of less than 3 mm can be obtained in the depth dimension for all depths from -1 cm to -3 cm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pogue, B.W., Testorf, M., Mcbride, T., et al.: Instrumentation and Design of a Frequency Domain Diffuse Optical Tomography Imager for Breast Cancer Detection. Opt. Exp. 13, 391–403 (1997)
Hebden, J.C., Gibson, A., Yusof, R.M., Everdell, N., et al.: Three-dimensional Optical Tomography of the Premature Infant Brain. Phys. Med. Biol. 47, 4155–4166 (2002)
Bluestone, A.Y., Abdoulaev, G., et al.: Three Dimensional Optical Tomography of Hemodynamics in the Human Head. Opt. Exp. 9, 272–286 (2001)
Peters, V.G., Wyman, D.R., et al.: Optical Properties of Normal and Diseased Human Breast Tissues in the Visible and Near Infrared. Phys. Med. Biol. 35, 1317–1334 (1990)
Douiri, A., Schweiger, R.M.J., Arridge, S.: Local Diffusion Regularization Method for Optical Tomography Reconstruction by Using Robust Statistics. Opt. Lett. 30, 2439–3441 (2005)
Joseph, D.K., Huppert, T.J., et al.: Diffuse Optical Tomography System to Image Brain Activation with Improved Spatial Resolution and Validation with Functional Magnetic Resonance Imaging. Appl. Opt. 45, 8142–8151 (2006)
Niu, H.J., Guo, P., Ji, L., Jiang, T.: Improving Diffuse Optical Tomography Imaging with Adaptive Regularization Method. In: Proc. SPIE, vol. 6789 K, pp. 1–7 (2007)
Pogue, B.W., McBride, T.O., et al.: Spatially Variant Regularization Improves Diffuse Optical Tomography. App. Opt. 38, 2950–2961 (1999)
Zhao, Q., Ji, L., Jiang, T.: Improving Depth Resolution of Diffuse Optical Tomography with a Layer-based Sigmoid Adjustment Method. Opt. Exp. 15, 4018–4029 (2007)
Paulsen, K.D., Jiang, H.: Spatially Varying Optical Property Reconstruction Using a Finite Element Diffusion Equation Approximation. Med. Phys. 22, 691–701 (1995)
Ishimaru, A., Schweiger, M., Arridge, S.R.: The Finite-element Method for the Propagation of Light in Scattering Media: Frequency Domain Case. Med. Phys. 24, 895–902 (1997)
Delpy, D.T., Cope, M., et al.: Estimation of Optical Pathlength Through Tissue From Direct Time of Flight Measurement. Phys. Med. Biol. 33, 1433–1442 (1988)
Zhao, Q., Ji, L., Jiang, T.: Improving Performance of Reflectance Diffuse Optical Imaging Using a Multiple Centered Mode. J. Biomed. Opt. 11, 064019, 1–8 (2006)
Arridge, S.R.: Optical Tomography in Medical Imaging. Inverse Probl. Eng. 15, R41-R93 (1999)
Boas, D.A., Dale, A.M.: Simulation Study of Magnetic Resonance Imaging-guided Cortically Constrained Diffuse Optical Tomography of Human Brain Function. Appl. Opt. 44, 1957–1968 (2005)
Song, X., Pogue, B.W., et al.: Automated Region Detection Based on the Contrast-to-noise Ratio in Near-infrared Tomography. Appl. Opt. 43, 1053–1062 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niu, HJ., Guo, P., Jiang, TZ. (2008). Improving Depth Resolution of Diffuse Optical Tomography with Intelligent Method. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-87442-3_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87440-9
Online ISBN: 978-3-540-87442-3
eBook Packages: Computer ScienceComputer Science (R0)