Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform

The well-known discrete Fourier transform (DFT) can easily be generalized to arbitrary nodes in the spatial domain. The fast procedure for this generalization is referred to as nonequispaced fast Fourier transform (NFFT). Various applications such as MRI and solution of PDEs are interested in the in...

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Main Authors: Melanie Kircheis, Daniel Potts
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fams.2023.1155484/full
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author Melanie Kircheis
Daniel Potts
author_facet Melanie Kircheis
Daniel Potts
author_sort Melanie Kircheis
collection DOAJ
description The well-known discrete Fourier transform (DFT) can easily be generalized to arbitrary nodes in the spatial domain. The fast procedure for this generalization is referred to as nonequispaced fast Fourier transform (NFFT). Various applications such as MRI and solution of PDEs are interested in the inverse problem, i.e., computing Fourier coefficients from given nonequispaced data. In this article, we survey different kinds of approaches to tackle this problem. In contrast to iterative procedures, where multiple iteration steps are needed for computing a solution, we focus especially on so-called direct inversion methods. We review density compensation techniques and introduce a new scheme that leads to an exact reconstruction for trigonometric polynomials. In addition, we consider a matrix optimization approach using Frobenius norm minimization to obtain an inverse NFFT.
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spelling doaj.art-de2d64bfc3094bba8d98af4e836adfce2023-06-28T09:21:12ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872023-06-01910.3389/fams.2023.11554841155484Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transformMelanie KircheisDaniel PottsThe well-known discrete Fourier transform (DFT) can easily be generalized to arbitrary nodes in the spatial domain. The fast procedure for this generalization is referred to as nonequispaced fast Fourier transform (NFFT). Various applications such as MRI and solution of PDEs are interested in the inverse problem, i.e., computing Fourier coefficients from given nonequispaced data. In this article, we survey different kinds of approaches to tackle this problem. In contrast to iterative procedures, where multiple iteration steps are needed for computing a solution, we focus especially on so-called direct inversion methods. We review density compensation techniques and introduce a new scheme that leads to an exact reconstruction for trigonometric polynomials. In addition, we consider a matrix optimization approach using Frobenius norm minimization to obtain an inverse NFFT.https://www.frontiersin.org/articles/10.3389/fams.2023.1155484/fullinverse nonequispaced fast Fourier transformnonuniform fast Fourier transformdirect inversioniNFFTNFFT
spellingShingle Melanie Kircheis
Daniel Potts
Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
Frontiers in Applied Mathematics and Statistics
inverse nonequispaced fast Fourier transform
nonuniform fast Fourier transform
direct inversion
iNFFT
NFFT
title Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
title_full Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
title_fullStr Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
title_full_unstemmed Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
title_short Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
title_sort fast and direct inversion methods for the multivariate nonequispaced fast fourier transform
topic inverse nonequispaced fast Fourier transform
nonuniform fast Fourier transform
direct inversion
iNFFT
NFFT
url https://www.frontiersin.org/articles/10.3389/fams.2023.1155484/full
work_keys_str_mv AT melaniekircheis fastanddirectinversionmethodsforthemultivariatenonequispacedfastfouriertransform
AT danielpotts fastanddirectinversionmethodsforthemultivariatenonequispacedfastfouriertransform