Residual motion artifact removal enables dynamic μMRI of a behaving Pachnoda marginata

Microscopic magnetic resonance imaging, also referred to as μMRI, is a non-invasive imaging modality ideal
for studying small live model organisms. However, μMRI raw data acquisition is inherently sequential and slow
in comparison to the biomechanics timescale of the behaving organism, leading to motion artifacts upon image
reconstruction. Recently, we have developed an integrated spherical treadmill with a prospectively triggered
k-space acquisition technique to provide position consistency for studying live, behaving insect using μMRI.
Despite this advancement, behaving insects on the treadmill still exhibited motion artifacts due to tethered
locomotion being coupled with internal organ dynamics. Here, we are addressing the large-scale non-rigid
nature of the abdominal motion of the behaving insect by developing a fully retrospective gating strategy
using the motion information obtained from an in-situ computer vision system. Residual motion artifacts
persisting after gating are effectively managed through a deep learning technique. We trained a U-Net-based
deep convolutional neural network using pairs of simulated motion-corrupted and motion-free images as a
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supervised image-to-image translation problem. Our results demonstrate that combining retrospective gated
μMRI reconstruction with a deep learning residual motion compensation technique can significantly reduce
the motional artifacts, thereby paving the way for the non-invasive dynamic imaging studies of behaving
organisms with 117 μm in-plane resolution.