A convolutional neural network to filter artifacts in spectroscopic MRI
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Purpose: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. Methods: A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency‐domain spectra to detect artifacts. Results: When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single‐voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole‐brain spectroscopic MRI volumes in real time. Conclusion: The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning.
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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 [188]
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- Authors
- Gurbani, S. S., Schreibmann, E., Maudsley, A. A., Cordova, J. S., Soher, B. J., Poptani, H., Verma, G., Barker, P. B., Shim, H., & Cooper, L. A. D.
- Affiliations
- Georgia Institute of Technology (US)
- Emory University (US)
- University of Miami (US)
- Duke University (US)
- University of Liverpool (GB)
- Icahn School of Medicine at Mount Sinai (US)
- Johns Hopkins University (US)
- Corresponding author
- Shim, Hyunsuk
- ORCID
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2018-03-09
- Volume
- 80
- Issue
- 5
- Pages
- 1765-1775
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- PMC
- PMC6107370
- OpenAlex
- W2790628621
- Cited by
- 84
- References
- 33
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 6 · 2023: 13 · 2022: 10 · 2021: 17 · 2020: 10 · 2019: 10 · 2018: 3 · 2017: 1
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (U01CA172027, R21NS100244, F30CA206291)
- National Cancer Institute
- National Institute of Neurological Disorders and Stroke
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- magnetic resonance spectroscopic imaging, convolutional neural networks, frequency-domain spectra, neuropathology
- MeSH terms
- Deep Learning, Algorithms, Brain, Brain Neoplasms, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Artifacts
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