Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography.

@article{Gupta2009SingularVD,
  title={Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography.},
  author={Saurabh Gupta and Phaneendra K. Yalavarthy and Debasish Roy and Daqing Piao and Ram Mohan Vasu},
  journal={Medical physics},
  year={2009},
  volume={36 12},
  pages={
          5559-67
        }
}
PURPOSE A computationally efficient algorithm (linear iterative type) based on singular value decomposition (SVD) of the Jacobian has been developed that can be used in rapid dynamic near-infrared (NIR) diffuse optical tomography. METHODS Numerical and experimental studies have been conducted to prove the computational efficacy of this SVD-based algorithm over conventional optical image reconstruction algorithms. RESULTS These studies indicate that the performance of linear iterative… 
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14 Citations
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Methods Citations
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14 Citations

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Citation Type

Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method.
TLDR
This new framework with the combination of ℓ(1)-norm-based regularization can provide better robustness to noise and provide better contrast recovery compared to conventional �¬(2)-based techniques.
Accelerating nonlinear reconstruction in laminar optical tomography by use of recursive SVD inversion.
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A practical nonlinear LOT approach to the absorption reconstruction that takes advantage of the numerical stability of the singular value decomposition (SVD) for the ill-posed linear inversion, and is accelerated by adopting an explicitly recursive strategy for the time-consuming repeated SVD inversion.
A pseudo-time EnKF incorporating shape based reconstruction for diffuse optical tomography.
TLDR
The PD-EnKF, which exhibits little sensitivity against variations in the fictitiously introduced noise processes, is shown to be accurate and robust in recovering a spatial map of the absorption coefficient from DOT data and is consistently superior to those through a deterministic and explicitly regularized Gauss-Newton algorithm.
Model-Resolution-Based Basis Pursuit Deconvolution Improves Diffuse Optical Tomographic Imaging
TLDR
It is shown, both in numerical and experimental gelatin phantom cases, that the proposed frame work to improve the reconstructed image characteristics using the basis pursuit deconvolution method yields better recovery of the target shapes compared to traditional method, without the loss of quantitativeness of the results.
Efficient method for near real-time diffuse optical tomography of the human brain
TLDR
Two methods of efficient sensitivity map generation based on Finite Element Models (FEM) are developed based on (1) reduced sensitivity matrix and (2) parallelisation process, and time and memory efficiency of these processes are evaluated and compared with conventional methods.
Pseudo-time particle filtering for diffuse optical tomography.
We recast the reconstruction problem of diffuse optical tomography (DOT) in a pseudo-dynamical framework and develop a method to recover the optical parameters using particle filters, i.e.,…
Investigation of effect of modulation frequency on high-density diffuse optical tomography image quality
TLDR
By incorporating multiple overlapping functional near-infrared spectroscopy measurements, high-density diffuse optical tomography images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI), pointing to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.
Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.
TLDR
The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.
Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain.
TLDR
A fast and efficient sensitivity map generation based on Finite Element Models (FEM) is developed which utilises a reduced sensitivitys matrix taking advantage of sparsity and parallelisation processes for near real-time image recovery.
Incoherence-based optimal selection of independent measurements in diffuse optical tomography
TLDR
The proposed method was compared with the recently established data-resolution matrix-based approach for optimal choice of independent measurements and shown to be superior as it does not require an optimal regularization parameter for providing the same information.
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