CoChat
Sign Up
Cited by 102Advanced MRI Techniques and ApplicationsMedical Image Segmentation TechniquesImage and Video Quality Assessment

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Automated reference-free detection of motion artifacts in magnetic resonance images

Küstner, Thomas, Liebgott, Annika, Mauch, Lukas, Martirosian, Petros, Bamberg, Fabian, Nikolaou, Konstantin, Yang, Bin, Schick, Fritz, Gatidis, Sergios·2018·Magnetic Resonance Materials in Physics Biology and Medicine
DOI 10.1007/s10334-017-0650-z
Open this paper in CoChat — free account

Abstract

Objectives Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.Materials and methods T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.Results On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.Conclusion Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Overview

Integrity signals

Registry

Crossref · 10.1007/s10334-017-0650-z

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Küstner T, Bahar P, Würslin C, Gatidis S, Martirosian P, Schwenzer NF, Yang B, Schmidt H (2015)A new approach for automatic image quality assessment. In: Proceedings of the 23rd scientific meeting, International Society for Magnetic Resonance in Medicine, Toronto, Canada. p 4735

Cited by

Sorted by citations

Related

Metadata

Authors
Küstner, T., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Yang, B., Schick, F., & Gatidis, S.
Affiliations
  • University of Stuttgart (DE)
  • University of Tübingen (DE)
Corresponding author
Küstner, Thomas
ORCID
Venue
Magnetic Resonance Materials in Physics Biology and Medicine
Publisher
Springer Science and Business Media LLC
Published
2017-09-20
Volume
31
Issue
2
Pages
243-256
ISSN
  • 0968-5243 (ISSN-L)
  • 1352-8661
DOI
10.1007/s10334-017-0650-z
OpenAlex
W2754887662
Cited by
102
References
36
Citations by year
2026: 3 · 2025: 9 · 2024: 13 · 2023: 13 · 2022: 15 · 2021: 14 · 2020: 14 · 2019: 12 · 2018: 9
FWCI
7.37
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (unspecified)
Locations
  • Magnetic Resonance Materials in Physics Biology and Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
Keywords
motion artifacts, T1-weighted imaging, convolutional neural networks, probability maps, image quality control, head MRI, quality assurance
MeSH terms
Abdomen, Machine Learning, Algorithms, Electronic Data Processing, Automation, Head, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Probability, Quality Assurance, Health Care, Signal Processing, Computer-Assisted, Reproducibility of Results, Artifacts, Neural Networks, Computer, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review