A new level-set algorithm for the diffuse optical imaging of the brain

We propose a new image reconstruction algorithm for functional near infrared spectroscopic imaging of the brain. Our approach considers the functional changes in the optical properties to be support limited. We simultaneously estimate the values of changes as well as the support from the available measurements. Since this scheme exploits the structure inherent to functional imaging, it provides reconstructions with better spatial resolution and is more robust to noise.

[1]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[2]  J. Zolésio,et al.  Introduction to shape optimization : shape sensitivity analysis , 1992 .

[3]  Yoram Bresler,et al.  Further results on spectrum blind sampling of 2D signals , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[4]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[5]  R. J. Gaudette,et al.  A comparison study of linear reconstruction techniques for diffuse optical tomographic imaging of absorption coefficient. , 2000, Physics in medicine and biology.

[6]  A. Dale,et al.  Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging. , 2003, Applied optics.

[7]  E. Miller,et al.  Optimal linear inverse solution with multiple priors in diffuse optical tomography. , 2005, Applied optics.

[8]  A Villringer,et al.  Cerebral blood oxygenation changes induced by visual stimulation in humans. , 1996, Journal of biomedical optics.

[9]  Yao Wang,et al.  A wavelet-based multiresolution regularized least squares reconstruction approach for optical tomography , 1997, IEEE Transactions on Medical Imaging.

[10]  Vladislav Toronov,et al.  Simultaneous integrated diffuse optical tomography and functional magnetic resonance imaging of the human brain. , 2005, Optics express.

[11]  Y. Bresler,et al.  Spectrum-blind minimum-rate sampling and reconstruction of 2-D multiband signals , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[12]  C L Matson,et al.  Three-dimensional tumor localization in thick tissue with the use of diffuse photon-density waves. , 1997, Applied optics.

[13]  D. Donoho.  For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[14]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[15]  G. Sapiro,et al.  Geometric partial differential equations and image analysis [Book Reviews] , 2001, IEEE Transactions on Medical Imaging.

[16]  Pierre Moulin,et al.  A Self-Referencing Level-Set Method for Image Reconstruction from Sparse Fourier Samples , 2004, International Journal of Computer Vision.

[17]  D. Hood,et al.  Fast and Localized Event-Related Optical Signals (EROS) in the Human Occipital Cortex: Comparisons with the Visual Evoked Potential and fMRI , 1997, NeuroImage.

[18]  Charles L. Matson,et al.  Backpropagation in turbid media , 1999 .

[19]  Olivier Faugeras,et al.  Reconciling Distance Functions and Level Sets , 2000, J. Vis. Commun. Image Represent..

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

[21]  Anders M. Dale,et al.  Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy , 2004, NeuroImage.

[22]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[23]  Thierry Blu,et al.  Efficient energies and algorithms for parametric snakes , 2004, IEEE Transactions on Image Processing.

[24]  D. Boas,et al.  Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography. , 1995, Optics letters.

[25]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[26]  E Gratton,et al.  Propagation of photon-density waves in strongly scattering media containing an absorbing semi-infinite plane bounded by a straight edge. , 1993, Journal of the Optical Society of America. A, Optics and image science.

[27]  David A Boas,et al.  Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function. , 2005, Applied optics.

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

[29]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  C H Schmitz,et al.  Optical tomographic imaging of dynamic features of dense-scattering media. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[32]  F. Santosa.  A Level-set Approach Inverse Problems Involving Obstacles , 1995 .

[33]  Alexander D. Klose,et al.  Gradient-based iterative image reconstruction scheme for time-resolved optical tomography , 1999, IEEE Transactions on Medical Imaging.