Work

Structural information within regularization matrices improves near infrared diffuse optical tomography

Year: 2007

Type: article

Abstract: Near-Infrared (NIR) tomographic image reconstruction is a non-linear, ill-posed and ill-conditioned problem, and so in this study, different ways of penalizing the objective function with structural information were investigated. A simple framework to incorporate structural priors is presented, using simple weight matrices that have either Laplacian or Helmholtz-type structures. Using both MRI-derived breast geometry and phantom data, a systematic and quantitative comparison was performed with and without spatial priors. The Helmholtz-type structure can be seen as a more generalized approach for incorporating spatial priors into the reconstruction scheme. Moreover, parameter reduction (i.e. hard prior information) in the imaging field through the enforcement of spatially explicit regions may lead to erroneous results with imperfect spatial priors.

Source: Optics Express

DOI: 10.1364/oe.15.008043

Authors: Phaneendra K. Yalavarthy, Brian W. Pogue, Hamid Dehghani, Colin M. Carpenter, Shudong Jiang, Keith D. Paulsen

Institutions: Dartmouth College, University of Exeter

Language: English

Topic: Optical Imaging and Spectroscopy Techniques

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: U.S. Department of Defense, National Cancer Institute