A Convolutional Neural Network to Filter Artifacts in Spectroscopic Magnetic Resonance Imaging
By S. Gurbani, E. Schreibmann, A. Maudsley, J. Cordova, B. Soher, Harish Poptani, G. Verma, Peter B. Barker, H. Shim, Lee A. D. Cooper. 2018
83 citations recorded by the source corpus.
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.
Subfields: Materials Chemistry, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “poor quality spectra in MRSI [187]; detection and removal of ghosting artifacts in MR spectroscopy [188]; and automated reference-free detection of patient motion artifacts in MRI [189].”