Abstract
Diffusion magnetic resonance imaging is a powerful, non-invasive technique to investigate the microscopic properties of tissues, based on analyzing the diffusion of water molecules, which is influenced by the tissues' microstructure. However, the clinical application of high resolution diffusion imaging is impeded due to its long scanning time. To reduce scan time, fast acquisition schemes such as multi-shot acquisition and undersampled data acquisition have been introduced. However, these acquisition schemes may introduce serious artifacts in the reconstructed diffusion parameter maps if not complemented with smart image reconstruction.
In this project, we introduce an advanced reconstruction framework that allows accelerated imaging by varying the diffusion contrast settings during the acquisition of a single image, e.g. for each shot in the multi-shot acquisition, introducing intra-scan modulation. This model-based reconstruction framework estimates diffusion parameter maps directly from the acquired intra-scan modulated data and simultaneously corrects for artifacts related to shot-to-shot phase inconsistencies. By now, the statistical performance of this framework has been assessed in Monte Carlo simulation studies.
In the next phase of the project, the framework will be extended to include higher-order phase patterns to account for more complex subject motion. In addition, the framework will be combined with common acceleration methods such as parallel imaging to aim for higher acceleration rates. Finally, we aim to define the optimal imaging settings including sampling strategy and optimal diffusion contrast set-ups using a statistical experiment design based on a Cramér-Rao Lower Bound analysis.
The feasibility of this approach will be investigated in real-data experiments considering at first, retrospective acceleration and, secondly, direct acquisition of multi-shot intra-scan modulated data.
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