Model-based image reconstruction from time-resolved diffusion data

Suhail S. Saquib; Kenneth M. Hanson; Gregory S. Cunningham

doi:10.1117/12.274123

25 April 1997 Model-based image reconstruction from time-resolved diffusion data

Suhail S. Saquib, Kenneth M. Hanson, Gregory S. Cunningham

Author Affiliations +

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274123
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

This paper addresses the issue of reconstructing the unknown field of absorption and scattering coefficients from time- resolved measurements of diffused light in a computationally efficient manner. The intended application is optical tomography, which has generated considerable interest in recent times. The inverse problem is posed in the Bayesian framework. The maximum a posteriori (MAP) estimate is used to compute the reconstruction. We use an edge-preserving generalized Gaussian Markov random field to model the unknown image. The diffusion model used for the measurements is solved forward in time using a finite-difference approach known as the alternating-directions implicit method. This method requires the inversion of a tridiagonal matrix at each time step and is therefore of O(N) complexity, where N is the dimensionality of the image. Adjoint differentiation is used to compute the sensitivity of the measurements with respect to the unknown image. The novelty of our method lies in the computation of the sensitivity since we can achieve it in O(N) time as opposed to O(N²) time required by the perturbation approach. We present results using simulated data to show that the proposed method yields superior quality reconstructions with substantial savings in computation.

Citation Download Citation

Suhail S. Saquib, Kenneth M. Hanson, and Gregory S. Cunningham "Model-based image reconstruction from time-resolved diffusion data", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274123

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