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Cardiac Diffusion Tensor Imaging (cDTI) Inference on Digital Twins
Cardiac diffusion tensor imaging (cDTI) provides information about the cardiac microstructure by measuring the diffusion of water molecules within the heart wall. Current imaging standards measure three slices distributed across the left ventricle. However, if not corrected, respiratory motion causes slice misalignments that obstruct microstructure inference. Yet, this motion might also allow us to estimate sample points between slices, thus adjusting for motion and increasing spatial coverage. By using the respiratory navigator data, you will map in-vivo cDTI data to a 3D digital twin mesh and implement a tensor estimation to estimate sample points between slices based on spatial smoothness regularization. You then perform an accuracy evaluation on simulated data.
Keywords: cardiac diffusion tensor imaging, digital twins, respiratory motion, MRI, magnetic resonance imaging, cardiac imaging
The approach utilizes respiratory motion and 3D mesh mapping in cardiac diffusion tensor imaging (cDTI), a subclass of cardiac MRI. By leveraging respiratory navigator data, you will map in-vivo cDTI to a 3D digital twin mesh and implement tensor estimation to discern sample points between slices. This not only corrects motion-induced misalignments but also expands spatial coverage. The method will then be rigorously evaluated for accuracy using simulated data to showcase its potential to advance cDTI.
** images from:
- https://doi.org/10.1002/cnm.3178
- https://doi.org/10.1186/s12968-017-0342-x
- https://doi.org/10.1371/journal.pone.0072795
The approach utilizes respiratory motion and 3D mesh mapping in cardiac diffusion tensor imaging (cDTI), a subclass of cardiac MRI. By leveraging respiratory navigator data, you will map in-vivo cDTI to a 3D digital twin mesh and implement tensor estimation to discern sample points between slices. This not only corrects motion-induced misalignments but also expands spatial coverage. The method will then be rigorously evaluated for accuracy using simulated data to showcase its potential to advance cDTI.
The project aims to utilize respiratory motion to estimate sample points between slices and thus increase spatial coverage by applying spatial smoothness constraints on a digital twin mesh. Accuracy evaluation is performed on simulated data.
The project aims to utilize respiratory motion to estimate sample points between slices and thus increase spatial coverage by applying spatial smoothness constraints on a digital twin mesh. Accuracy evaluation is performed on simulated data.
Sandra Haltmeier (haltmeier@biomed.ee.ethz.ch)
Jonathan Weine (weine@biomed.ee.ethz.ch)
Sandra Haltmeier (haltmeier@biomed.ee.ethz.ch) Jonathan Weine (weine@biomed.ee.ethz.ch)