http://www.cnr.it/ontology/cnr/individuo/prodotto/ID322614
Regularization techniques on least squares non-uniform fast Fourier transform (Articolo in rivista)
- Type
- Label
- Regularization techniques on least squares non-uniform fast Fourier transform (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1002/cnm.2540 (literal)
- Alternative label
Gibiino, Fabio; Positano, Vincenzo; Landini, Luigi; Santarelli, Maria Filoména (2013)
Regularization techniques on least squares non-uniform fast Fourier transform
in International journal for numerical methods in biomedical engineering (Print)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Gibiino, Fabio; Positano, Vincenzo; Landini, Luigi; Santarelli, Maria Filoména (literal)
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- http://www.scopus.com/record/display.url?eid=2-s2.0-84877592591&origin=inward (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Universita di Pisa; Consiglio Nazionale delle Ricerche; Istituto di Fisiologia Clinica del CNR (literal)
- Titolo
- Regularization techniques on least squares non-uniform fast Fourier transform (literal)
- Abstract
- Non-Cartesian acquisition strategies are widely used in MRI to dramatically reduce the acquisition time
while at the same time preserving the image quality. Among non-Cartesian reconstruction methods, the
least squares non-uniform fast Fourier transform (LS_NUFFT) is a gridding method based on a local data
interpolation kernel that minimizes the worst-case approximation error. The interpolator is chosen using a
pseudoinverse matrix. As the size of the interpolation kernel increases, the inversion problem may become
ill-conditioned. Regularization methods can be adopted to solve this issue. In this study, we compared three
regularization methods applied to LS_NUFFT. We used truncated singular value decomposition (TSVD),
Tikhonov regularization and L1-regularization. Reconstruction performance was evaluated using the direct
summation method as reference on both simulated and experimental data. We also evaluated the processing
time required to calculate the interpolator. First, we defined the value of the interpolator size after which
regularization is needed. Above this value, TSVD obtained the best reconstruction. However, for large interpolator
size, the processing time becomes an important constraint, so an appropriate compromise between
processing time and reconstruction quality should be adopted. (literal)
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