Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy
Open accessAbstract
Purpose To make use of deep learning (DL) methods to detect and remove ghosting artifacts in clinical magnetic resonance spectra of human brain. Methods Deep learning algorithms, including fully connected neural networks, deep‐convolutional neural networks, and stacked what‐where auto encoders, were implemented to detect and correct MR spectra containing spurious echo ghost signals. The DL methods were trained on a huge database of simulated spectra with and without ghosting artifacts that represent complex variations of ghost‐ridden spectra, transformed to time‐frequency spectrograms. The trained model was tested on simulated and in vivo spectra. Results The preliminary results for ghost detection are very promising, reaching almost 100% accuracy, and the DL ghost removal methods show potential in simulated and in vivo spectra, but need further refinement and quantitative testing. Conclusions Ghosting artifacts in spectroscopy are problematic, as they superimpose with metabolites and lead to inaccurate quantification. Detection and removal of ghosting artifacts using traditional machine learning approaches with feature extraction/selection is difficult, as ghosts appear at different frequencies. Here, we show that DL methods perform extremely well for ghost detection if the spectra are treated as images in the form of time‐frequency representations. Further optimization for in vivo spectra will hopefully confirm their “ghostbusting” capacity. Magn Reson Med 80:851–863, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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Deep learning methods have also been applied to MR artifact detection.
“Deep learning methods has also been applied to MR artifact detection , e.g. poor quality spectra in MRSI [188]; detection and removal of ghosting artifacts in MR spectroscopy [189]; and automated reference-free detection of patient motion artifacts in MRI [190].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [189]
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An overview of deep learning in medical imaging focusing on MRI
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- Authors
- Kyathanahally, S. P., Döring, A., & Kreis, R.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Kreis, Roland
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2018-02
- Volume
- 80
- Issue
- 3
- Pages
- 851-863
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2800950215
- Cited by
- 92
- References
- 38
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 12 · 2023: 17 · 2022: 14 · 2021: 14 · 2020: 10 · 2019: 11 · 2018: 2 · 2012: 1
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- green
- boris.unibe.ch
- Licence
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- BORIS (University Library Bern) (submittedVersion, other-oa)
- Open Access CRIS of the University of Bern
- Funders
- Swiss National Science Foundation (320030‐156952, 320030‐175984)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- ghosting artifacts, MR spectroscopy, artifact detection, artifact removal, time-frequency spectrograms, deep convolutional neural networks, brain metabolites, spectral quantification
- MeSH terms
- Deep Learning, Algorithms, Brain, Computer Simulation, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Spectroscopy, Normal Distribution, Artifacts, Neural Networks, Computer, Signal-To-Noise Ratio, Healthy Volunteers, Gray Matter
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