Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging
| Autor: | Alfred Mertins, Kay Nehrke, Peter Börnert, Malte Riedel Né Steinhoff |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Partial fourier
Computer science brain SENSE ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 030218 nuclear medicine & medical imaging Motion 03 medical and health sciences 0302 clinical medicine Motion artifacts Robustness (computer science) Motion estimation Humans Computer Simulation Radiology Nuclear Medicine and imaging Segmentation motion correction model-based image reconstruction Spectroscopy Sensitivity encoding Echo-planar imaging Echo-Planar Imaging multi-shot DWI Diffusion imaging EPI Diffusion Magnetic Resonance Imaging Anisotropy Molecular Medicine Algorithm Algorithms 030217 neurology & neurosurgery |
| Zdroj: | NMR in Biomedicine |
| Popis: | Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|