IISc Logo    Title

etd AT Indian Institute of Science >
Centres under the Director (formely kown as Division of Information Sciences) >
Supercomputer Education and Research Centre (serc) >

Please use this identifier to cite or link to this item: http://etd.iisc.ernet.in/2005/3007

Title: Development of Sparse Recovery Based Optimized Diffuse Optical and Photoacoustic Image Reconstruction Methods
Authors: Shaw, Calvin B
Advisors: Yalavarthy, Phaneendra K
Keywords: Biomedical Optics
Diffuse Optical Tomography
Diffuse Optical Tomographic Image Reconstruction
Image Reconstruction
Photoacoustic Tomography
Medical Imaging
Biomedical Optical Imaging
Multi-modal Imaging
Inverse Problems
Sparse Reconstruction
Photoacoustic Image Reconstruction
Computer Tomography
Diffuse Optical Tomographic Imaging
Photoacoustic Tomographic Imaging
Photoacoustic Tomography Reconstruction
Submitted Date: 2014
Series/Report no.: G26754
Abstract: Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/3873/G26754-Abs.pdf
URI: http://hdl.handle.net/2005/3007
Appears in Collections:Supercomputer Education and Research Centre (serc)

Files in This Item:

File Description SizeFormat
G26754.pdf12.75 MBAdobe PDFView/Open

Items in etd@IISc are protected by copyright, with all rights reserved, unless otherwise indicated.


etd@IISc is a joint service of SERC & IISc Library ||
|| Powered by DSpace || Compliant to OAI-PMH V 2.0 and ETD-MS V 1.01