Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Journal: Magnetic resonance in medicine

PMID: 30666698

Abstract

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.

Authors

Saumya S Gurbani

Department of Radiation Oncology, Emory University, Atlanta, Georgia.
Sulaiman Sheriff

Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida.
Andrew A Maudsley

Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida.
Hyunsuk Shim

Department of Radiation Oncology, Emory University, Atlanta, Georgia.
Lee A D Cooper

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, 30322, USA. lee.cooper@emory.edu.

Keywords

Algorithms Artifacts Aspartic Acid Brain Mapping Brain Neoplasms Choline Computer Graphics Creatine Databases, Factual Deep Learning Echo-Planar Imaging Glioblastoma Humans Image Processing, Computer-Assisted Magnetic Resonance Spectroscopy Models, Theoretical Neural Networks, Computer Signal-To-Noise Ratio Software User-Computer Interface White Matter

External Resources

View on PubMed Access via DOI PubMed (30666698)

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Abstract

Authors

Keywords

External Resources

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