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Preprints, Working Papers, ... Year : 2018

Variable-density k-space filling curves for accelerated Magnetic Resonance Imaging


Reducing scan times in magnetic resonance imaging (MRI) is essential for attaining high spatial resolution, which could aid in diagnosing certain pathologies, such as Alzheimer's disease. Methods to accelerate the time of segmented MR acquisitions commonly rely on simple sampling patterns such as straight lines, spirals or slight variations of these elementary shapes. However, such geometrical approaches do not take full advantage of the degrees of freedom offered by the hardware and cannot be easily adapted to fit an arbitrary sampling distribution. Here, we report the use of a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non-Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples and a locally uniform k-space coverage. Combining sampling efficiency with compressed sensing, the resulting sampling patterns allowed up to 20-fold reductions in MR scan time (compared to fully-sampled Cartesian acquisitions) for two-dimensional T * 2-weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in-plane resolution of 390 µm. In comparison to existing non-Cartesian sampling strategies (spiral and radial), the proposed technique also yielded superior image quality. Since our method does not involve additional hardware, this approach offers a cost-free solution that has the potential to improve sampling efficiency in many MRI applications.
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Dates and versions

hal-01861760 , version 1 (25-08-2018)


  • HAL Id : hal-01861760 , version 1


Carole Lazarus, Pierre Weiss, N. Chauffert, F. Mauconduit, Loubna El Gueddari, et al.. Variable-density k-space filling curves for accelerated Magnetic Resonance Imaging. 2018. ⟨hal-01861760⟩
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