Ye et al., 1999 - Google Patents

Optical diffusion tomography by iterative-coordinate-descent optimization in a Bayesian framework

Ye et al., 1999

View HTML
Document ID
5025666047519020336
Author
Ye J
Webb K
Bouman C
Millane R
Publication year
Publication venue
JOSA A

External Links

Snippet

Frequency-domain diffusion imaging uses the magnitude and phase of modulated light propagating through a highly scattering medium to reconstruct an image of the spatially dependent scattering or absorption coefficients in the medium. An inversion algorithm is …
Continue reading at opg.optica.org (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/4795—Scattering, i.e. diffuse reflection spatially resolved investigating of object in scattering medium
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]

Similar Documents

Publication Publication Date Title
Ye et al. Optical diffusion tomography by iterative-coordinate-descent optimization in a Bayesian framework
Song et al. Reconstruction for free-space fluorescence tomography using a novel hybrid adaptive finite element algorithm
Cox et al. Two-dimensional quantitative photoacoustic image reconstruction of absorption distributions in scattering media by use of a simple iterative method
Yao et al. Frequency-domain optical imaging of absorption and scattering distributions by a Born iterative method
Jin et al. A reconstruction algorithm for electrical impedance tomography based on sparsity regularization
Yalavarthy et al. Weight‐matrix structured regularization provides optimal generalized least‐squares estimate in diffuse optical tomography
Barbour A perturbation approach for optical diffusion tomography using continuous-wave and time-resolved data
Prakash et al. Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography
Haltmeier et al. Single-stage reconstruction algorithm for quantitative photoacoustic tomography
Gao et al. Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization
Ye et al. Modified distorted Born iterative method with an approximate Fréchet derivative for optical diffusion tomography
Eppstein et al. Biomedical optical tomography using dynamic parameterization and Bayesian conditioning on photon migration measurements
Tick et al. Image reconstruction with uncertainty quantification in photoacoustic tomography
Zhu et al. Iterative total least-squares image reconstruction algorithm for optical tomography by the conjugate gradient method
Prakash et al. A LSQR‐type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography
Kilmer et al. Three-dimensional shape-based imaging of absorption perturbation for diffuse optical tomography
Pulkkinen et al. Quantitative photoacoustic tomography using illuminations from a single direction
Oh et al. Source–detector calibration in three-dimensional Bayesian optical diffusion tomography
Marks A family of approximations spanning the Born and Rytov scattering series
Testorf et al. Sampling of time-and frequency-domain signals in Monte Carlo simulations of photon migration
Chen et al. Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography
Roy et al. Active constrained truncated Newton method for simple-bound optical tomography
Mozumder et al. Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach
Panagiotou et al. Information theoretic regularization in diffuse optical tomography
Zhao et al. Unrolled-DOT: an interpretable deep network for diffuse optical tomography