Open Access Publications

The Institute's work is published in both traditional journals (e.g. the prestigious imaging journal IEEE Transactions on Medical Imaging) and open access journals. For traditional journals, a preprint is uploaded to ArXiv whenever possible to make the research results freely available.

In addition, Tobias Knopp, as Editor-in-Chief, has founded a new scientific Open Access journal, which makes all articles available under the Creative Commons License (CC-BY-4.0). The International Journal on MagneticParticle Imaging (IJMPI) was founded in 2015 and publishes new research developments within the MPI community.

Open Access Publications

[183062]
Title: NFFT.jl: Generic and Fast Julia Implementation of the Nonequidistant Fast Fourier Transform.
Written by: T. Knopp, M. Boberg, and M. Grosser
in: <em>SIAM Journal on Scientific Computing</em>. (2023).
Volume: <strong>45</strong>. Number: (3),
on pages: C179-C205
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1137/22M1510935
URL: https://arxiv.org/abs/2208.00049
ARXIVID:
PMID:

[www]

Note: article, opensoftware, openaccess, generalsoftware

Abstract: The nonequidistant fast Fourier transform (NFFT) is an extension of the famous fast Fourier transform (FFT) that can be applied to nonequidistantly sampled data in time/space or frequency domain. It is an approximative algorithm that allows one to control the approximation error in such a way that machine precision is reached while keeping the algorithmic complexity in the same order as a regular FFT. The NFFT plays a major role in many signal processing applications and has been intensively studied from a theoretical and computational perspective. The fastest CPU implementations of the NFFT are implemented in the low-level programming languages C and C++ and require a compromise between code generalizability, code readability, and code efficiency. The programming language Julia promises new opportunities in optimizing these three conflicting goals. In this work we show that Julia indeed allows one to develop an NFFT implementation which is completely generic and dimension-agnostic and requires about two to three times less code than the other famous libraries NFFT3 and FINUFFT while still being one of the fastest NFFT implementations developed to date.