Highly accelerated non-contrast-enhanced time-resolved 4D MRA using stack-of-stars golden-angle radial acquisition with a self-calibrated low-rank subspace reconstruction

Magn Reson Med. 2025 Feb;93(2):615-629. doi: 10.1002/mrm.30304. Epub 2024 Sep 30.

Abstract

Purpose: To develop a highly accelerated non-contrast-enhanced 4D-MRA technique by combining stack-of-stars golden-angle radial acquisition with a modified self-calibrated low-rank subspace reconstruction.

Methods: A low-rank subspace reconstruction framework was introduced in radial 4D MRA (SUPER 4D MRA) by combining stack-of-stars golden-angle radial acquisition with control-label k-space subtraction-based low-rank subspace modeling. Radial 4D MRA data were acquired and reconstructed using the proposed technique on 12 healthy volunteers and 1 patient with steno-occlusive disease. The performance of SUPER 4D MRA was compared with two temporally constrained reconstruction methods (golden-angle radial sparse parallel [GRASP] and GRASP-Pro) at different acceleration rates in terms of image quality and delineation of blood dynamics.

Results: SUPER 4D MRA outperformed the other two reconstruction methods, offering superior image quality with a clear background and detailed delineation of cerebrovascular structures as well as great temporal fidelity in blood flow dynamics. SUPER 4D MRA maintained excellent performance even at higher acceleration rates.

Conclusions: SUPER 4D MRA is a promising technique for highly accelerating 4D MRA acquisition without comprising both temporal fidelity and image quality.

Keywords: 4D MRA; arterial spin labeling (ASL); cerebral vasculature; low‐rank subspace reconstruction; non‐contrast enhanced MR angiography.

MeSH terms

  • Adult
  • Algorithms
  • Cerebrovascular Circulation
  • Female
  • Healthy Volunteers
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / methods
  • Imaging, Three-Dimensional* / methods
  • Magnetic Resonance Angiography* / methods
  • Male
  • Reproducibility of Results