Spatially varying regularization based on retrieved support in diffuse optical tomography

Sohail Sabir; Sanghoon Cho; Seunryong Cho

doi:10.1117/12.2254340

13 March 2017 Spatially varying regularization based on retrieved support in diffuse optical tomography

Sohail Sabir, Sanghoon Cho, Seunryong Cho

Proceedings Volume 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging; 101371K (2017) https://doi.org/10.1117/12.2254340
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality capable of providing the functional characteristics (oxygen saturation and hemodynamic states) of thick biological tissue by quantifying the optical parameters. The parameter recovery problem in DOT is a nonlinear, ill-posed and ill conditioned inverse problem. The non-linear iterative methods are usually employed for image reconstruction in DOT by utilizing Tikhonov based regularization approach. These methods employ l2-norm based regularization where the constant regularization parameter is determined either empirically or generalized cross validation methods or L curve method. The reconstructed images look smoother or noisy depending on the chosen value of the regularization constant. Moreover the edges information of the inclusions appeared to be blurred in such constant regularization methods. In this study we proposed a method to retrieve and utilized a non-zero support (possible tumor location) to generate a spatially varying regularization map. The inclusions locations were determined by considering the imaging problem as a multiple measurements vector (MMV) problem. Based on the recovered inclusion positions spatially regularization map was generated to be used in non-linear image reconstruction framework. The results retrieved with such spatially varying priors shows slightly improved image reconstruction in terms of better contrast recovery, reduction in background noise and preservation of edge information of inclusions compared with the constant regularization approach.

Citation Download Citation

Sohail Sabir, Sanghoon Cho, and Seunryong Cho "Spatially varying regularization based on retrieved support in diffuse optical tomography", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101371K (13 March 2017); https://doi.org/10.1117/12.2254340

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available