Open Source Software for Medical Imaging

Medical imaging requires an enormous amount of expert knowledge in signal processing, image reconstruction and image processing. At our institute we develop imaging algorithms using the open source scientific programming language Julia and make them available via the collaborative version control platform GitHub under the MIT license. This enables the documentation of scientific methodology and ensures the reproducibility of our own research contributions. Furthermore, even scientists with only rudimentary knowledge of medical imaging are enabled to use state-of-the-art image reconstruction methods. We are happy about any feedback / suggestions that can be send by email to us. Improvements and amendments can be also directly made on GitHub.

All MPI related projects are collected in the organization https://github.com/MagneticParticleImaging, while the institute's contributions to MRI are collected in the organization https://github.com/MagneticResonanceImaging. Projects, which cannot be assigned clearly to an imaging method, are maintained under the account of Tobias Knopp, where our main contributions are:

Magnetic Particle Imaging
Magnetic Resonance Imaging
Further Projects

Publications

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.