04272cam a2200685Ia 4500 ocn871224295 OCoLC 20171031095653.0 m o d cr cnu---unuuu 140301s2014 nju ob 001 0 eng d 9781118760932 (electronic bk.) 111876093X (electronic bk.) 9781118760895 (electronic bk.) 1118760891 (electronic bk.) 9781118760949 (electronic bk.) 1118760948 (electronic bk.) 9781848214743 (hbk.) 184821474X (hbk.) CHBIS 010442005 CHVBK 334092795 DEBBG BV043396571 DEBBG BV043607762 DEBSZ 405661800 NZ1 15497408 NZ1 15906667 (OCoLC)871224295 (OCoLC)871318314 (OCoLC)876043676 EBLCP eng pn EBLCP OCLCQ IDEBK N$T DG1 CUS YDXCP DEBSZ OCLCA OCLCQ UA@ E7B COO DEBBG OCLCQ MAIN T174.7 TEC 009000 bisacsh TEC 035000 bisacsh 620.5 22 Ting, Michael (Software engineer) Molecular Imaging in Nano MRI / Michael Ting. [electronic resource] London, U.K. : ISTE ; Hoboken, N.J. : Wiley, 2014. 1 online resource (x, 77 pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier Focus series Includes bibliographical references and index. 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. 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. Online resource; title from PDF title page (Wiley, viewed April 4, 2014). 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) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Electronic books. Print version: Ting, Michael. Molecular imaging in nano MRI. London, U.K : ISTE ; Hoboken, N.J. : Wiley, 2014 9781848214743 (OCoLC)859185634 Focus nanoscience and nanotechnology series. http://onlinelibrary.wiley.com/book/10.1002/9781118760949 Wiley Online Library ddc BK 207294 207294