title: Model-resolution based regularization improves near infrared diffuse optical tomography.
abstract: Diffuse optical tomographic imaging is known to be an ill-posed problem, and a penalty/regularization term is used in image reconstruction (inverse problem) to overcome this limitation. Two schemes that are prevalent are spatially varying (exponential) and constant (standard) regularizations/penalties. A scheme that is also spatially varying but uses the model information is introduced based on the model-resolution matrix. This scheme, along with exponential and standard regularization schemes, is evaluated objectively based on model-resolution and data-resolution matrices. This objective analysis showed that resolution characteristics are better for spatially varying penalties compared to standard regularization; and among spatially varying regularization schemes, the model-resolution based regularization fares well in providing improved data-resolution and model-resolution characteristics. The verification of the same is achieved by performing numerical experiments in reconstructing 1% noisy data involving simple two- and three-dimensional imaging domains.
authors.affiliation: Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560 012, India.
authors.forename: Sree Harsha
authors.initials: SH
authors.lastname: Katamreddy
authors.forename: Phaneendra K
authors.initials: PK
authors.lastname: Yalavarthy
mesh_terms: D021621:Imaging, Three-Dimensional; D007259:Infrared Rays; D008962:Models, Theoretical; D041622:Tomography, Optical / Q000379:methods*