Molecular imaging in nano MRI / Michael Ting.

Format:

Book

Author/Creator:

Ting, Michael.

Series:

Focus series (London, England)

FOCUS Wave Series, 2051-249X

Language:

English

Subjects (All):

Magnetic resonance imaging.

Molecular diagnosis.

Physical Description:

1 online resource (89 p.)

Edition:

1st ed.

Place of Publication:

London ; Hoboken, New Jersey : ISTE : Wiley, 2014.

Language Note:

English

Summary:

The authors describe a technique that can visualize the atomic structure of molecules, it is necessary, in terms of the image processing, to consider the reconstruction of sparse images. Many works have leveraged the assumption of sparsity in order to achieve an improved performance that would not otherwise be possible. For nano MRI, the assumption of sparsity is given by default since, at the atomic scale, molecules aresparse structures. This work reviews the latest results on molecular imaging for nano MRI. Sparse image reconstruction methods can be categorized as either non-B

Contents:

Cover; Title page; Contents; Introduction; Chapter 1. Nano MRI; Chapter 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; Chapter 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 statistics3.3.1. 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; Chapter 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

Chapter 5. Monte Carlo Approach: Gibbs Sampling5.1. 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 x; 5.3.4. Conditional density of σ2; 5.4. Uncertainty in the blur point spread function; 5.5. Simulation example; Chapter 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; Bibliography; Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781118760949

1118760948

9781118760932

111876093X

9781118760895

1118760891

OCLC:

876043676