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020 _a9783031016677
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024 7 _a10.1007/978-3-031-01667-7
_2doi
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082 0 4 _a620
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100 1 _aBuonincontri, Guido.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985003
245 1 0 _aFast Quantitative Magnetic Resonance Imaging
_h[electronic resource] /
_cby Guido Buonincontri, Joshua Kaggie, Martin Graves.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXV, 124 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
505 0 _aIntroduction -- Spatial Encoding -- Contrast Encoding -- Spatial Decoding -- Contrast Decoding -- Conclusion -- Author Biographies .89.
520 _aAmong medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections. Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques. Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_985004
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aKaggie, Joshua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985005
700 1 _aGraves, Martin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985006
710 2 _aSpringerLink (Online service)
_985008
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000461
776 0 8 _iPrinted edition:
_z9783031005398
776 0 8 _iPrinted edition:
_z9783031027956
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_985010
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01667-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85756
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