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Highly time-resolved 4D MR angiography using golden-angle radial sparse parallel (GRASP) MRI.

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This study shows that routine Gradient-Echo Acquisition with Undersampled Parallel Slices (GRASP) MRI can be retrospectively reconstructed into high-quality, 4D dynamic MR angiography (MRA) with excellent vascular detail.

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Area of Science:

  • Magnetic Resonance Imaging
  • Vascular Imaging
  • Medical Imaging Technology

Background:

  • Current dynamic MRA techniques face limitations in temporal resolution and signal-to-noise ratio.
  • Gradient-Echo Acquisition with Undersampled Parallel Slices (GRASP) is a flexible MRI technique enabling continuous volumetric data acquisition.
  • GRASP combines compressed sensing, parallel imaging, and golden-angle radial sampling for enhanced data acquisition.

Purpose of the Study:

  • To describe a custom pipeline for retrospectively reconstructing ultrahigh temporal resolution, dynamic MRA from routine GRASP imaging.
  • To evaluate the feasibility and image quality of post-hoc 4D MRA reconstruction from clinical GRASP scans.
  • To assess the diagnostic quality and artifact levels of GRASP-derived dynamic MRA.

Main Methods:

  • A custom pipeline was developed to reconstruct dynamic MRA from GRASP MRI data.
  • GRASP scans were reconstructed using a custom GRASP algorithm implementation and post-processed with MeVisLab.
  • Twenty consecutive GRASP examinations were retrospectively analyzed and scored by three neuroradiologists for angiographic quality and artifacts.

Main Results:

  • Distinct arterial and capillary phases were identified in all reconstructions, with a median of 2 frames per phase.
  • Median rating for vascular segment quality was excellent (3 out of 4) across all reconstructions and nearly all segments.
  • Excellent intraclass correlation (0.91-1.00) was achieved, with no cases degraded by artifacts.

Conclusions:

  • Routine GRASP-MRI can be effectively repurposed to generate high-quality 4D MRA with 1-2 second resolved isotropic cerebrovascular angiography.
  • This approach offers a seamless method for obtaining advanced dynamic MRA from existing clinical datasets.
  • Further research is warranted to explore the diagnostic accuracy of GRASP-derived 4D MRA in specific disease applications.