Gradient-based iterative image reconstruction scheme for time-resolved optical tomography

Currently available tomographic image reconstruction schemes for optical tomography (OT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. Furthermore, these algorithms usually require the inversion of large, f...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 1999-03, Vol.18 (3), p.262-271
Main Authors: Hielscher, A.H., Klose, A.D., Hanson, K.M.
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Cerebral Hemorrhage - diagnosis
Cerebral Ventricles - pathology
Finite difference method
Finite difference methods
Hemodynamics
Hemorrhaging
Humans
Image Processing, Computer-Assisted
Image reconstruction
Infant
Infrared imaging
Infrared Rays
Investigative techniques, diagnostic techniques (general aspects)
Iterative algorithms
Iterative methods
Jacobian matrices
Mathematical models
Matrix algebra
Medical sciences
Minimization methods
Models, Theoretical
Nervous system
Neurophysiology
Nonhomogeneous media
Optics and Photonics
Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques
Physiological models
Predictive models
Tomography
Tomography - methods
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Summary:Currently available tomographic image reconstruction schemes for optical tomography (OT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. Furthermore, these algorithms usually require the inversion of large, full, ill-conditioned Jacobian matrixes. In this work a gradient-based iterative image reconstruction (GIIR) method is presented that promises to overcome current limitations. The code consists of three major parts: (1) A finite-difference, time-resolved, diffusion forward model is used to predict detector readings based on the spatial distribution of optical properties; (2) An objective function that describes the difference between predicted and measured data; (3) An updating method that uses the gradient of the objective function in a line minimization scheme to provide subsequent guesses of the spatial distribution of the optical properties for the forward model. The reconstruction of these properties is completed, once a minimum of this objective function is found. After a presentation of the mathematical background, two- and three-dimensional reconstruction of simple heterogeneous media as well as the clinically relevant example of ventricular bleeding in the brain are discussed. Numerical studies suggest that intraventricular hemorrhages can be detected using the GIIR technique, even in the presence of a heterogeneous background.
ISSN:0278-0062
1558-254X
DOI:10.1109/42.764902