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|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
Biomedical Optical Imaging
Photoacoustic Image Reconstruction
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|
|Appears in Collections:||Supercomputer Education and Research Centre (serc)|
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