Parametric estimation of 3D tubular structures for diffuse optical tomography

We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction.

[1]  Marjolein van der Voort,et al.  A Novel Fluorescent Imaging Agent for Diffuse Optical Tomography of the Breast: First Clinical Experience in Patients , 2009, Molecular Imaging and Biology.

[2]  Eric L. Miller,et al.  Parametric Level Set Methods for Inverse Problems , 2010, SIAM J. Imaging Sci..

[3]  D Boas,et al.  A fundamental limitation of linearized algorithms for diffuse optical tomography. , 1997, Optics express.

[4]  B. Pogue,et al.  Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results. , 2003, Applied optics.

[5]  Jeffrey A. Fessler,et al.  A Bayesian Approach to Reconstruction from Incomplete Projections of a Multiple Object 3D Domain , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J P Culver,et al.  Optimization of optode arrangements for diffuse optical tomography: A singular-value analysis. , 2001, Optics letters.

[7]  Regine Choe,et al.  Comparison of diffuse optical tomography of human breast with whole-body and breast-only positron emission tomography. , 2008, Medical physics.

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

[9]  Sergio Fantini,et al.  NEAR-INFRARED, BROAD-BAND SPECTRAL IMAGING OF THE HUMAN BREAST FOR QUANTITATIVE OXIMETRY: APPLICATIONS TO HEALTHY AND CANCEROUS BREASTS , 2010 .

[10]  E. Miller,et al.  Parametric level set reconstruction methods for hyperspectral diffuse optical tomography , 2012, Biomedical optics express.

[11]  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.

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

[13]  Eric L. Miller,et al.  A Parametric Level-Set Approach to Simultaneous Object Identification and Background Reconstruction for Dual-Energy Computed Tomography , 2011, IEEE Transactions on Image Processing.

[14]  Fabrizio Martelli,et al.  Light Propagation Through Biological Tissue and Other Diffusive Media: Theory, Solutions, and Software , 2009 .

[15]  Alan J. Laub,et al.  Matrix analysis - for scientists and engineers , 2004 .

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

[17]  E. Miller,et al.  Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography. , 2004, Optics letters.

[18]  Kaj Madsen,et al.  Methods for Non-Linear Least Squares Problems , 1999 .

[19]  Sergio Fantini,et al.  Near-Infrared Optical Mammography for Breast Cancer Detection with Intrinsic Contrast , 2011, Annals of Biomedical Engineering.

[20]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[21]  David Boas,et al.  Three-dimensional shape-based imaging of absorption perturbation for diffuse optical tomography. , 2003, Applied optics.

[22]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[23]  C. Vogel.  Computational Methods for Inverse Problems , 1987 .

[24]  Sandra K. Soho,et al.  Characterization of hemoglobin, water, and NIR scattering in breast tissue: analysis of intersubject variability and menstrual cycle changes. , 2004, Journal of biomedical optics.

[25]  Simon R. Arridge,et al.  Corrections to linear methods for diffuse optical tomography using approximation error modelling , 2010, Biomedical optics express.

[26]  Robert A Jesinger,et al.  Vascular abnormalities of the breast: arterial and venous disorders, vascular masses, and mimic lesions with radiologic-pathologic correlation. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  P. Hansen.  Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .

[28]  A. Mandelis.  Diffusion-wave fields : mathematical methods and Green functions , 2001 .

[29]  E. Miller,et al.  Efficient determination of multiple regularization parameters in a generalized L-curve framework , 2002 .