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Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting
Author(s) -
Gurbani Saumya S.,
Sheriff Sulaiman,
Maudsley Andrew A.,
Shim Hyunsuk,
Cooper Lee A.D.
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27641
Subject(s) - computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , a priori and a posteriori , philosophy , epistemology
Purpose MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectrum to the data. For high‐resolution volumetric MRSI of the brain, which may have ~10,000 spectra, significant processing time is required for spectral analysis and generation of metabolite maps. Methods A novel unsupervised deep learning architecture that combines a convolutional neural network with a priori models of the spectrum is presented. This architecture, a convolutional encoder–model decoder (CEMD), combines the strengths of adaptive and unbiased convolutional networks with models of magnetic resonance and is readily interpretable. Results The CEMD architecture performs accurate spectral fitting for volumetric MRSI in patients with glioblastoma, provides whole‐brain fitting in 1 min on a standard computer, and handles a variety of spectral artifacts. Conclusion A new architecture combining physics domain knowledge with convolutional neural networks has been developed and is able to perform rapid spectral fitting of whole‐brain data. Rapid processing is a critical step toward routine clinical practice.