Molecular Imaging in Nano MRI - Ting, Michael (Software engineer) (London, U.K. : ISTE ; Hoboken, N.J. : Wiley, 2014)
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.
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.
Includes bibliographical references and index.
Online resource; title from PDF title page (Wiley, viewed April 4, 2014).
Keywords: Magnetic resonance imaging -- Computer programs; Nanoscience; Nuclear magnetic resonance -- Computer programs; Magnetic resonance imaging -- Computer programs; Nanoscience; Nuclear magnetic resonance -- Computer programs; TECHNOLOGY & ENGINEERING -- Engineering (General); TECHNOLOGY & ENGINEERING -- Reference; Electronic books