Molecular imaging in nano MRI

Molecular imaging in nano MRI /

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Bibliographic Details
Main Author: Ting, Michael (Author)
Corporate Author: Ebooks Corporation
Format: Electronic eBook
Language:English
Published: London : Hoboken, NJ : ISTE ; Wiley, 2014.
Series:Focus series in waves.
Subjects:
Magnetic resonance imaging.
Molecular diagnosis.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: ch. 1 Nano MRI
  • ch. 2 Sparse Image Reconstruction
  • 2.1. Introduction
  • 2.2. Problem formulation
  • 2.3. Validity of the observation model in MRFM
  • 2.4. Literature review
  • 2.4.1. Sparse denoising
  • 2.4.2. Variable selection
  • 2.4.3. Compressed sensing
  • 2.5. Reconstruction performance criteria
  • ch. 3 Iterative Thresholding Methods
  • 3.1. Introduction
  • 3.2. Separation of deconvolution and denoising
  • 3.2.1. Gaussian noise statistics
  • 3.2.2. Poisson noise statistics
  • 3.3. Choice of sparse denoising operator in the case of Gaussian noise statistics
  • 3.3.1. Comparison to the projected gradient method
  • 3.4. Hyperparameter selection
  • 3.5. MAP estimators using the LAZE image prior
  • 3.5.1. MAP1
  • 3.5.2. MAP2
  • 3.5.3. Comparison of MAP1 versus MAP2
  • 3.6. Simulation example
  • 3.7. Future directions
  • ch. 4 Hyperparameter Selection Using the SURE Criterion
  • 4.1. Introduction
  • 4.2. SURE for the lasso estimator
  • 4.3. SURE for the hybrid estimator
  • 4.4. Computational considerations
  • 4.5. Comparison with other criteria
  • 4.6. Simulation example
  • ch. 5 Monte Carlo Approach: Gibbs Sampling
  • 5.1. Introduction
  • 5.2. Casting the sparse image reconstruction problem in the Bayesian framework
  • 5.3. MAP estimate using the Gibbs sampler
  • 5.3.1. Conditional density of w
  • 5.3.2. Conditional density of a
  • 5.3.3. Conditional density of sigma2
  • 5.3.4. Conditional density of σ2
  • 5.4. Uncertainty in the blur point spread function
  • 5.5. Simulation example
  • ch. 6 Simulation Study
  • 6.1. Introduction
  • 6.2. Reconstruction simulation study
  • 6.2.1. Binary-valued x
  • 6.2.2. {0, ±1}-valued x
  • 6.3. Discussion.

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