Model-resolution based regularization improves near infrared diffuse optical tomography.

Diffuse optical tomographic imaging is known to be an ill-posed problem, and a penalty/regularization term is used in image reconstruction (inverse problem) to overcome this limitation. Two schemes that are prevalent are spatially varying (exponential) and constant (standard) regularizations/penalties. A scheme that is also spatially varying but uses the model information is introduced based on the model-resolution matrix. This scheme, along with exponential and standard regularization schemes, is evaluated objectively based on model-resolution and data-resolution matrices. This objective analysis showed that resolution characteristics are better for spatially varying penalties compared to standard regularization; and among spatially varying regularization schemes, the model-resolution based regularization fares well in providing improved data-resolution and model-resolution characteristics. The verification of the same is achieved by performing numerical experiments in reconstructing 1% noisy data involving simple two- and three-dimensional imaging domains.

[1]  B. Pogue,et al.  Weight-matrix structured regularization provides optimal generalized least-squares estimate in diffuse optical tomography. , 2007, Medical physics.

[2]  M. Schweiger,et al.  Photon-measurement density functions. Part 2: Finite-element-method calculations. , 1995, Applied optics.

[3]  David Isaacson,et al.  Electrical Impedance Tomography , 1999, SIAM Rev..

[4]  H. Dehghani,et al.  Diffuse optical imaging , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  R. Leahy,et al.  Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography , 2008, Physics in medicine and biology.

[6]  B. Pogue,et al.  Tutorial on diffuse light transport. , 2008, Journal of biomedical optics.

[7]  Phaneendra K. Yalavarthy,et al.  Helmholtz-Type Regularization Method for Permittivity Reconstruction Using Experimental Phantom Data of Electrical Capacitance Tomography , 2010, IEEE Transactions on Instrumentation and Measurement.

[8]  Eric L. Miller,et al.  Imaging the body with diffuse optical tomography , 2001, IEEE Signal Process. Mag..

[9]  Tianzi Jiang,et al.  Improving image quality of diffuse optical tomography with a projection-error-based adaptive regularization method. , 2008, Optics express.

[10]  D. Delpy,et al.  Optical Imaging in Medicine , 1998, CLEO/Europe Conference on Lasers and Electro-Optics.

[11]  Hamid Dehghani,et al.  Wavelength dependence of sensitivity in spectral diffuse optical imaging: effect of normalization on image reconstruction. , 2008, Optics express.

[12]  Hamid Dehghani,et al.  Structural information within regularization matrices improves near infrared diffuse optical tomography. , 2007, Optics express.

[13]  M. Schweiger,et al.  Gauss–Newton method for image reconstruction in diffuse optical tomography , 2005, Physics in medicine and biology.

[14]  S R Arridge,et al.  Comparison of two- and three-dimensional reconstruction methods in optical tomography. , 1998, Applied optics.

[15]  Hamid Dehghani,et al.  Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. , 2009, Communications in numerical methods in engineering.

[16]  S R Arridge,et al.  Recent advances in diffuse optical imaging , 2005, Physics in medicine and biology.

[17]  B. Pogue,et al.  A parallel-detection frequency-domain near-infrared tomography system for hemoglobin imaging of the , 2001 .

[18]  Hamid Dehghani,et al.  Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis. , 2006, Optics express.

[19]  Hamid Dehghani,et al.  Numerical modelling and image reconstruction in diffuse optical tomography , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  B. Pogue,et al.  Image-guided diffuse optical fluorescence tomography implemented with Laplacian-type regularization. , 2007, Optics express.

[21]  Eric L. Miller,et al.  Hyperspectral image reconstruction for diffuse optical tomography , 2011, Biomedical optics express.

[22]  R. Cubeddu,et al.  Optical Tomography , 1998, Technical Digest. 1998 EQEC. European Quantum Electronics Conference (Cat. No.98TH8326).

[23]  S. Arridge,et al.  Optical imaging in medicine: II. Modelling and reconstruction , 1997, Physics in medicine and biology.

[24]  Britton Chance,et al.  Diffuse optical tomography with a priori anatomical information , 2003, SPIE BiOS.

[25]  B. Pogue,et al.  Spatially variant regularization improves diffuse optical tomography. , 1999, Applied optics.

[26]  Britton Chance,et al.  Diffuse optical tomography with physiological and spatial a priori constraints , 2004, Physics in medicine and biology.

[27]  S. Arridge.  Optical tomography in medical imaging , 1999 .

[28]  Arye Nehorai,et al.  Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm. , 2007, Optics express.

[29]  M. Schweiger,et al.  The finite element method for the propagation of light in scattering media: boundary and source conditions. , 1995, Medical physics.