A convolutional neural network to filter artifacts in spectroscopic MRI

• Published in: Magnetic resonance in medicine Vol. 80; no. 5; pp. 1765 - 1775
• Main Authors: Gurbani, Saumya S., Schreibmann, Eduard, Maudsley, Andrew A., Cordova, James Scott, Soher, Brian J., Poptani, Harish, Verma, Gaurav, Barker, Peter B., Shim, Hyunsuk, Cooper, Lee A. D.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.11.2018
• Subjects: Adaptive radiation; Artificial neural networks; Brain; Contrast agents; deep learning; Filtration; Identification methods; Ionizing radiation; Machine learning; Magnetic resonance imaging; Magnetic resonance spectroscopy; Medical imaging; Metabolism; MR spectroscopic imaging; Neural networks; Neuroimaging; Quality assessment; Radiation therapy; Spectra; spectroscopic MRI; deep learning; MR spectroscopic imaging; machine learning; spectroscopic MRI
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.27166

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.