Abstract
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.
Highlights
A major challenge in biological science is to relate molecular regulation at the cellular level to response and behaviour at the organism level
We further introduce an Magnetic resonance imaging (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
Motion prediction enables simulated MR-imaging of freely moving model organisms
Summary
A major challenge in biological science is to relate molecular regulation at the cellular level to response and behaviour at the organism level. Even taken individually, these items are very hard to achieve in general, and correspond to major research areas in their own right Small organisms such as C. elegans are biological model organisms useful for studying many human disorders, including neurodegenerative diseases [1, 2]. Model organisms have been the mainstay of biological sciences for decades, and a broad knowledge base already exists starting from the genome level, through developmental cycles, and up to behavioural response to applied stress These platforms offer the opportunity to address the connection between molecular phenotype, which can be conveniently implemented due to the very rapid yet standardized life cycle of C. elegans, to behaviour. What remains is the technological challenge of satisfying the three requirements for robustly linking phenotype to behaviour
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