We convert the information-rich measurements of parallel and phased-array MRI into
noisier data that a corresponding single-coil scanner could have taken. Specifically,
we replace the responses from multiple receivers with a linear combination
that emulates the response from only a single, aggregate receiver, replete
with the low signal-to-noise ratio and phase problems of any single one of
the original receivers (combining several receivers is necessary, however,
since the original receivers usually have limited spatial sensitivity). This
enables experimentation in the simpler context of a single-coil scanner prior
to development of algorithms for the full complexity of multiple receiver
coils.
Keywords
magnetic resonance imaging, parallel imaging, multicoil,
fastMRI, deep learning, machine learning
