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, leadin...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1006997 |
| _version_ | 1819022954769416192 |
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| author | Markus Reischl Mazin Jouda Neil MacKinnon Erwin Fuhrer Natalia Bakhtina Andreas Bartschat Ralf Mikut Jan G Korvink |
| author_facet | Markus Reischl Mazin Jouda Neil MacKinnon Erwin Fuhrer Natalia Bakhtina Andreas Bartschat Ralf Mikut Jan G Korvink |
| author_sort | Markus Reischl |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T04:31:12Z |
| format | Article |
| id | doaj.art-82568c31f2c845e9a0aa7bd65605599e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T04:31:12Z |
| publishDate | 2019-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-82568c31f2c845e9a0aa7bd65605599e2022-12-21T19:15:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-12-011512e100699710.1371/journal.pcbi.1006997Motion prediction enables simulated MR-imaging of freely moving model organisms.Markus ReischlMazin JoudaNeil MacKinnonErwin FuhrerNatalia BakhtinaAndreas BartschatRalf MikutJan G KorvinkMagnetic 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.https://doi.org/10.1371/journal.pcbi.1006997 |
| spellingShingle | Markus Reischl Mazin Jouda Neil MacKinnon Erwin Fuhrer Natalia Bakhtina Andreas Bartschat Ralf Mikut Jan G Korvink Motion prediction enables simulated MR-imaging of freely moving model organisms. PLoS Computational Biology |
| title | Motion prediction enables simulated MR-imaging of freely moving model organisms. |
| title_full | Motion prediction enables simulated MR-imaging of freely moving model organisms. |
| title_fullStr | Motion prediction enables simulated MR-imaging of freely moving model organisms. |
| title_full_unstemmed | Motion prediction enables simulated MR-imaging of freely moving model organisms. |
| title_short | Motion prediction enables simulated MR-imaging of freely moving model organisms. |
| title_sort | motion prediction enables simulated mr imaging of freely moving model organisms |
| url | https://doi.org/10.1371/journal.pcbi.1006997 |
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