Literature DB >> 16705635

Encoding and reconstruction in parallel MRI.

Klaas P Pruessmann1.   

Abstract

The advent of parallel MRI over recent years has prompted a variety of concepts and techniques for performing parallel imaging. A main distinguishing feature among these is the specific way of posing and solving the problem of image reconstruction from undersampled multiple-coil data. The clearest distinction in this respect is that between k-space and image-domain methods. The present paper reviews the basic reconstruction approaches, aiming to emphasize common principles along with actual differences. To this end the treatment starts with an elaboration of the encoding mechanisms and sampling strategies that define the reconstruction task. Based on these considerations a formal framework is developed that permits the various methods to be viewed as different solutions of one common problem. Besides the distinction between k-space and image-domain approaches, special attention is given to the implications of general vs lattice sampling patterns. The paper closes with remarks concerning noise propagation and control in parallel imaging and an outlook upon key issues to be addressed in the future. Copyright (c) 2006 John Wiley & Sons, Ltd.

Mesh:

Year:  2006        PMID: 16705635     DOI: 10.1002/nbm.1042

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  31 in total

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4.  Rapid B1 field mapping at 3 T using the 180° signal null method with extended flip angle.

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Authors:  Katherine L Wright; Jesse I Hamilton; Mark A Griswold; Vikas Gulani; Nicole Seiberlich
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Authors:  Frank L Goerner; Timothy Duong; R Jason Stafford; Geoffrey D Clarke
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Authors:  Antonis Kalemis; Bénédicte M A Delattre; Susanne Heinzer
Journal:  MAGMA       Date:  2012-08-07       Impact factor: 2.310

8.  Image reconstruction in k-space from MR data encoded with ambiguous gradient fields.

Authors:  Gerrit Schultz; Daniel Gallichan; Hans Weber; Walter R T Witschey; Matthias Honal; Jürgen Hennig; Maxim Zaitsev
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9.  Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.

Authors:  Saiprasad Ravishankar; Jong Chul Ye; Jeffrey A Fessler
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2019-09-19       Impact factor: 10.961

Review 10.  Image reconstruction: an overview for clinicians.

Authors:  Michael S Hansen; Peter Kellman
Journal:  J Magn Reson Imaging       Date:  2014-06-25       Impact factor: 4.813

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