Numerical modeling for image reconstruction
1 / 25

Numerical Modeling for Image Reconstruction - PowerPoint PPT Presentation

  • Uploaded on

Numerical Modeling for Image Reconstruction. Subha Srinivasan 11/2/2009. Definition of Inverse Problem. Definition: Given a distribution of sources and a distribution of measurements at the boundary dΩ, finding the tissue parameter distribution within domain Ω. Expressed as x = F -1 (y).

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Numerical Modeling for Image Reconstruction' - axel-mclaughlin

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Numerical modeling for image reconstruction

Numerical Modeling for Image Reconstruction



Definition of inverse problem
Definition of Inverse Problem..

  • Definition: Given a distribution of sources and a distribution of measurements at the boundary dΩ, finding the tissue parameter distribution within domain Ω.

  • Expressed as x = F-1(y)

Ways of solving inverse problems
Ways of Solving Inverse Problems

  • Back-projection methods

  • Perturbation methods

  • Non-linear optimization methods

Back projection methods
Back-projection methods

  • Assumes that each projection provides a nearly independent measurement of the domain.

  • Assumes that light travels in a straight line: not true with tissue unless scattering is isolated

fan beam




Filtered Back-projection method:

[measurements] = [attenuation op.] [object ]

[image] = [attenuation op.]T [filter] [measurements]

Linear reconstruction for change in optical properties
Linear reconstruction for Change in Optical Properties

  • x = F-1(y) is a non-linear problem: can be linearized using Taylor’s series expansion if initial estimate is close to actual values:

Jacobian matrix

Reconstructing for changes rather than absolute values

Structure of jacobian
Structure of Jacobian

Ф = I e-i(ωt+θ)

I = signal amplitude

θ = signal phase

m = [ma, D]

Absorption coeff.

Diffusion Coeff.

Shape of jacobian
Shape of Jacobian

Calculated by:



2)Direct Analytic


3) Adjoint method


  • Linearizing change in intensity: born approximation

  • Linearizing change in log intensity: Rytov approximation

  • Inverting J: large, under-determined and ill-posed: some standard methods can be used

  • Truncated SVD, Tikhonov regularization, Algebriac reconstruction techniques (ART) & Conjugate Gradient methods are commonly used

Terminology inverse problem
Terminology: Inverse Problem

  • Ill-posed–Small changes in the data can cause large changes in the parameters.

  • Ill-conditioned–The condition number (ratio of largest singular value to smallest singular value) is large, which implies the inverse solution would not be unique.

  • Ill-determined–(or under-determined) The number of independent equations are smaller than number of unknowns.

Deriving update equation using least squares minimization
Deriving Update Equation using Least Squares Minimization

  • Minimizing error functional:

  • Setting derivative to zero:

  • Taylor’s approximation

  • Rewriting:

  • Substituting:

  • Update equation:

Assumptions of levenberg marquardt minimization
Assumptions of Levenberg-Marquardt Minimization:

  • JTJ is positive-definite

  • Initial guess must be close to actual solution

  • Update equation does not solve first-order conditions unless α = 0

*Yalavarthy et. al., Medical Physics, 2007

Tikhonov Minimization:

Key idea is to introduce apriori assumptions about size and smoothness of desired solution:

L is dimensionless

common choice: L = I

(the identity matrix)

*Tikhonov et. al, 1977; Tarantola SIAM 2004.

*Yalavarthy et. al., Medical Physics, 2007

Tikhonov Minimization


  • parameters within the minimization scheme => stability


  • it requires a prior opinion about the noise characteristics of the parameter and data spaces (for λ)

Choosing regularization l curve criterion
Choosing Regularization: L-curve criterion

  • Convenient graphical tool for displaying trade-off between size of solution and its fit to the given data as λ varies.

  • λ can also be chosen empirically or based on parameter/data values.

Hansen, ‘L-curve and its use in numerical treatment of inverse problems’

Reconstruction results
Reconstruction Results

  • Simulated Measurements, 5% Noise

Spectral image reconstruction
Spectral Image Reconstruction

Data from Boulnois et al, Hale & Quarry,

figure from thesis Srinivasan et al

Spectral image reconstruction1
Spectral Image Reconstruction

Relationships between Jsp & J can be obtained

Details, refer to Srinivasan et al, AO, 2005

Simulations show reduced cross talk in spectral images
Simulations show Reduced Cross-talk in spectral images



Water (%)

Scatt Ampl.

Scatt Power


  • Data generated from a tumor-simulating phantom using FEM forward model, with 1% random-Gaussian noise added.



  • Spectral Method: Smoother Images; 15.3 % mean error compared to 43% (conv. Method).

  • Reduced Cross-talk between HbO2 and water: from30% (conv.) to 7% (spectral).

  • Accuracy in StO2 accurate (<1% error)

Srinivasan et al, PhD thesis, 2005

Results from image reconstruction experimental data
Results from Image Reconstruction:Experimental Data

Brooksby, Srinivasan et al, Opt Lett, 2005


  • Gibson et al, Phy Med Bio: 50 : 2005: A review paper

  • Paulsen et al, Med Phy: 22(6): 1995: first results from image-reconstruction in DOT

  • Yalavarthy et al, Med Phy: 34(6): 2007: good explanation of math

  • Brooksby et al, IEEE Journal of selected topics in quantum electronics: 9(2): 2003: good reference for spatial priors

  • Hansen: ‘Rank deficient and discrete ill-posed problems’: SIAM: 1998: good reference for tikhonov/l-curve

  • Srinivasan et al, Appl Optics: 44(10): 2005: reference for spectral priors

  • Press et al: ‘Numerical Recipes in Fortran 77’: II edition: 1992: great book for numerical folks!