ArticleMagnetic resonance in medicine2022
Optimization of undersampling parameters for 3D intracranial compressed sensing MR angiography at 7 T.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.Magnetic resonance in medicine · 2026Article
- Mesoscale imaging of the human cerebellum reveals converging regional specialization of its morphology, vasculature, and cytoarchitecture.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA.Frontiers in medicine · 2024Article
- Highly accelerated intracranial time-of-flight magnetic resonance angiography using wave-encoding.Magnetic resonance in medicine · 2023Article
- Optimization of undersampling parameters for 3D intracranial compressed sensing MR angiography at 7 T.Magnetic resonance in medicine · 2022Article
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3 authors.
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Abstract
purpose3D time-of-flight MRA can accurately visualize the intracranial vasculature but is limited by long acquisition times. Compressed sensing reconstruction can be used to substantially accelerate acquisitions. The quality of those reconstructions depends on the undersampling patterns used. In this work, we optimize sets of undersampling parameters for various acceleration factors of Cartesian 3D time-of-flight MRA.
methodsFully sampled datasets, acquired at 7 Tesla, were retrospectively undersampled using variable-density Poisson disk sampling with various autocalibration region sizes, polynomial orders, and acceleration factors. The accuracy of reconstructions from the different undersampled datasets was assessed using the vessel-masked structural similarity index. Identified optimal undersampling parameters were then evaluated in additional prospectively undersampled datasets. Compressed sensing reconstruction parameters were chosen based on a preliminary reconstruction parameter optimization.
resultsFor all acceleration factors, using a fully sampled calibration area of 12
conclusionThe image quality of compressed sensing time-of-flight MRA can be improved by appropriate choice of undersampling parameters. The optimized sets of parameters are independent of the acceleration factor and enable a larger number of vessels to be visualized.
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