Share Email Print
cover

Proceedings Paper

Gaussian kernel based anatomically-aided diffuse optical tomography reconstruction
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image reconstruction in diffuse optical tomography (DOT) is challenging because its inverse problem is nonlinear, ill-posed and ill-conditioned. Anatomical guidance from high spatial resolution imaging modalities can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we propose the kernel method to introduce anatomical information into the DOT image reconstruction algorithm. In this kernel method, optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT). The kernel based image model is directly incorporated into the forward model of DOT, which exploits the sparseness of the image in the feature space. Compared with Laplacian approaches to include structural priors, the proposed method does not require the image segmentation of distinct regions. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. We added 15% Gaussian noise onto both the numerical DOT measurements and the simulated CT image. We have also validated the proposed method by agar phantom experiment with anatomical guidance from a CT scan. We have studied the effects of voxel size and number of nearest neighborhood size in kernel method on the reconstructed DOT images. Our results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method.

Paper Details

Date Published: 17 February 2017
PDF: 10 pages
Proc. SPIE 10059, Optical Tomography and Spectroscopy of Tissue XII, 1005912 (17 February 2017); doi: 10.1117/12.2252786
Show Author Affiliations
Reheman Baikejiang, Univ. of California, Merced (United States)
Wei Zhang, Univ. of California, Merced (United States)
Changqing Li, Univ. of California, Merced (United States)


Published in SPIE Proceedings Vol. 10059:
Optical Tomography and Spectroscopy of Tissue XII
Bruce J. Tromberg; Arjun G. Yodh; Eva Marie Sevick-Muraca; Robert R. Alfano, Editor(s)

© SPIE. Terms of Use
Back to Top