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001 9781351029261
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006 m d
007 cr |||||||||||
008 191029s2019 flua o 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781351029247
_q(ePub ebook) :
020 _a135102924X
020 _a9781351029254
_q(PDF ebook) :
020 _a1351029258
020 _a9781351029230
_q(Mobipocket ebook) :
020 _a1351029231
020 _a9781351029261
_q(ebook) :
020 _a1351029266
020 _z9780815361473 (hbk.)
024 7 _a10.1201/9781351029261
_2doi
035 _a(OCoLC)1127830069
035 _a(OCoLC-P)1127830069
050 4 _aQC762.6.M34
072 7 _aMED
_x009000
_2bisacsh
072 7 _aSCI
_x055000
_2bisacsh
072 7 _aTEC
_x015000
_2bisacsh
072 7 _aPHVN
_2bicssc
082 0 4 _a538.36
_223
100 1 _aPaul, Joseph Suresh,
_eauthor.
_920330
245 1 0 _aRegularized image reconstruction in parallel MRI with MATLAB /
_cJoseph Suresh Paul, Raji Susan Mathew.
250 _a1st.
264 1 _aBoca Raton :
_bCRC Press,
_c2019.
300 _a1 online resource :
_billustrations (black and white)
336 _atext
_2rdacontent
336 _astill image
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
500 _a<P>Preface. Acknowledgement. Author Biography. Parallel MR image reconstruction. Regularization techniques for MR image reconstruction. Regularization parameter selection methods in parallel MR image reconstruction. Multi-filter calibration for autocalibrating parallel MRI. Parameter adaptation for wavelet regularization in parallel MRI. Parameter adaptation for total variation based regularization in parallel MRI. Combination of parallel magnetic resonance imaging and compressed sensing using L1-SPIRiT. Matrix completion methods. References. L MATLAB Codes.</P>
520 _aRegularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMagnetic resonance imaging.
_94091
650 7 _aMEDICAL / Biotechnology
_2bisacsh
_920331
650 7 _aSCIENCE / Physics
_2bisacsh
_910678
650 7 _aTECHNOLOGY / Imaging Systems
_2bisacsh
_910809
700 1 _aMathew, Raji Susan,
_eauthor.
_920332
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781351029261
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c72358
_d72358