TH-D-M100J-02: Quantitative Interstitial Multispectral Diffuse Optical Tomography of Human Prostate Using a Priori Structure Information

2007 ◽  
Vol 34 (6Part23) ◽  
pp. 2638-2638
Author(s):  
X Zhou ◽  
T Zhu ◽  
J Finlay ◽  
J Li ◽  
A Dimofte ◽  
...  
2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Michael Ghijsen ◽  
Yuting Lin ◽  
Mitchell Hsing ◽  
Orhan Nalcioglu ◽  
Gultekin Gulsen

Diffuse Optical Tomography (DOT) is an optical imaging modality that has various clinical applications. However, the spatial resolution and quantitative accuracy of DOT is poor due to strong photon scatting in biological tissue. Structurala prioriinformation from another high spatial resolution imaging modality such as Magnetic Resonance Imaging (MRI) has been demonstrated to significantly improve DOT accuracy. In addition, a contrast agent can be used to obtain differential absorption images of the lesion by using dynamic contrast enhanced DOT (DCE-DOT). This produces a relative absorption map that consists of subtracting a reconstructed baseline image from reconstructed images in which optical contrast is included. In this study, we investigated and compared different reconstruction methods and analysis approaches for regular endogenous DOT and DCE-DOT with and without MR anatomicala prioriinformation for arbitrarily-shaped objects. Our phantom and animal studies have shown that superior image quality and higher accuracy can be achieved using DCE-DOT together with MR structurala prioriinformation. Hence, implementation of a combined MRI-DOT system to image ICG enhancement can potentially be a promising tool for breast cancer imaging.


2005 ◽  
Vol 50 (12) ◽  
pp. 2837-2858 ◽  
Author(s):  
Murat Guven ◽  
Birsen Yazici ◽  
Xavier Intes ◽  
Britton Chance

2021 ◽  
Author(s):  
Isaac Harris

Abstract In this paper, we develop a new regularized version of the Factorization Method for positive operators mapping a complex Hilbert Space into it’s dual space. The Factorization Method uses Picard’s Criteria to define an indicator function to image an unknown region. In most applications the data operator is compact which gives that the singular values can tend to zero rapidly which can cause numerical instabilities. The regularization of the Factorization Method presented here seeks to avoid the numerical instabilities in applying Picard’s Criteria. This method allows one to image the interior structure of an object with little a priori information in a computationally simple and analytically rigorous way. Here we will focus on an application of this method to diffuse optical tomography where will prove that this method can be used to recover an unknown subregion from the Dirichlet-to-Neumann mapping. Numerical examples will be presented in two dimensions.


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