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Molecular imaging in nano MRI DUP_1

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Molecular imaging in nano MRI DUP_1/ Michael Ting.
Author:	Ting, Michael
Published:	Hoboken :Wiley, : 2014.,
Description:	1 online resource (89 p.)
Subject:	Magnetic resonance imaging - Computer programs. -
Online resource:	http://onlinelibrary.wiley.com/book/10.1002/9781118760949
ISBN:	9781118760932 (electronic bk.)

Molecular imaging in nano MRI DUP_1
Ting, Michael(Software engineer)

Molecular imaging in nano MRI DUP_1[electronic resource] /Michael Ting. - Hoboken :Wiley,2014. - 1 online resource (89 p.) - FOCUS Series. - Colección "FOCUS.".

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.

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.

ISBN: 9781118760932 (electronic bk.)Subjects--Topical Terms:

1059528
Magnetic resonance imaging
--Computer programs.

LC Class. No.: T174.7

Dewey Class. No.: 620.5

Molecular imaging in nano MRI DUP_1
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100 1 $a Ting, Michael $c (Software engineer) $3 1059526
245 1 0 $a Molecular imaging in nano MRI DUP_1 $h [electronic resource] / $c Michael Ting.
260 $a Hoboken : $b Wiley, $c 2014.
300 $a 1 online resource (89 p.)
490 1 $a FOCUS Series
505 0 $a 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.
505 8 $a 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.
505 8 $a 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.
520 $a 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.
588 $a Description based on print version record.
650 0 $a Magnetic resonance imaging $x Computer programs. $3 1059528
650 0 $a Nanoscience. $3 632473
650 0 $a Nuclear magnetic resonance $x Computer programs. $3 1059529
830 0 $a Colección "FOCUS." $3 1059527
856 4 0 $u http://onlinelibrary.wiley.com/book/10.1002/9781118760949