Motion prediction enables simulated MR-imaging of freely moving model organisms

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.Author summary: Magnetic resonance imaging (MRI) requires its subjects not to move, since movement will cause image artifacts. This is hard to achieve for adult humans, whom we can ask to comply, but can currently only be achieved by sedation for other freely moving biological specimens. Because of the importance of non-invasive MRI as a technique to also capture metabolic information during activity, this is a huge deficiency of the methodology that is hampering progress. In our paper we ask the question whether it is possible to computationally combine optical information on specimen movement with MRI. Our approach is to predict the future movement and position of the specimen and thereby anticipate where it will be so as to specify correct MRI parameters. Our computer simulations show, for a freely moving worm, that a reasonable prediction is already possible for a short time window, and that we can control the amount of error of the resulting MRI image. Importantly, with the continuous speedup of computation, our simulations suggest that it is opportune now to implement such a system in hardware.