A convolutional neural network to filter artifacts in spectroscopic MRI.

ABSTRACT:

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

SUBMITTER: Gurbani SS 

PROVIDER: S-EPMC6107370 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature