Molecular imaging in nano MRI /
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Main Author: | |
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Corporate Author: | |
Format: | Electronic eBook |
Language: | English |
Published: |
London : Hoboken, NJ :
ISTE ; Wiley,
2014.
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Series: | Focus series in waves.
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Subjects: | |
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.