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

Abstract

14Magnetic resonance tomography typically applies the Fourier transform to 15 k-space signals repeatedly acquired from a frequency encoded spatial region of 16 interest, therefore requiring a stationary object during scanning. Any movement of 17 the object results in phase errors in the recorded signal, leading to deformed 18 images, phantoms, and artifacts, since the encoded information does not originate 19 from the intended region of the object. However, if the type and magnitude of 20 movement is known instantaneously, the scanner or the reconstruction algorithm 21 could be adjusted to compensate for the movement, directly allowing high quality 22 imaging with non-stationary objects. This would be an enormous boon to studies 23 that tie cell metabolomics to spontaneous organism behaviour, eliminating the 24 stress otherwise necessitated by restraining measures such as anesthesia or 25 clamping. 26In the present theoretical study, we use a phantom of the animal model 27 C. elegans to examine the feasibility to automatically predict its movement and 28 position, and to evaluate the impact of movement prediction, within a sufficiently 29 long time horizon, on image reconstruction. For this purpose, we use automated 30 image processing to annotate body parts in freely moving C. elegans, and predict 31 their path of movement. We further introduce an MRI simulation platform based 32 on brightfield-videos of the moving worm, combined with a stack of high resolution 33 transmission electron microscope (TEM) slice images as virtual high resolution 34 phantoms. A phantom provides an indication of the spatial distribution of 35 signal-generating nuclei on a particular imaging slice. We show that adjustment of 36 the scanning to the predicted movements strongly reduces distortions in the 37 resulting image, opening the door for implementation in a high-resolution NMR 38 scanner. 39 1 Introduction 40 A major challenge in biological science is to relate molecular regulation at the cellular 41 level to response and behaviour at the organism level. Knowing this relationship lies at 42 the foundation of every disease, and indeed also in understanding the healthy organism. 43 An experiment establishing this relationship, as schematically shown in Fig. 1, requires 44 i) in vivo cellular-level detection of regulation-relevant molecules, such as metabolites 45 March 30, 2019 1/18and their production rates, ii) precise access to and application of biological 46 perturbation mechanisms, and iii) an unburdened (naturally responding) organism. 47Clearly, even taken individually, these items are very hard to achieve in general, and 48 correspond to major research areas in their own right. 49 Experimental control Output Stimulus Organism Response Fig 1. A freely moving organism is subject to a stimulus (mechanical, chemical, light, etc.) causing a response at all levels of detail (metabolomic, behaviour, etc.). The experiment designer relates the correlated output to a biological hypothesis and may adapt the stimulus. Small or...