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

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.

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 inte

SUBMITTER: Gurbani SS 

PROVIDER: S-EPMC6414236 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature