ArticleMagnetic resonance in medicine2019
Compressed Sensing 3D-GRASE for faster High-Resolution MRI.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Rapid whole-brain venous cerebral blood volume mapping using velocity-selective venous-spin-labeling with 3D GRASE.NeuroImage · 2025Article
- Deep-learning reconstruction of the prostate improves image quality and acquisition time in T2-weighted imaging.Nagoya journal of medical science · 2025Article
- Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.Journal of imaging informatics in medicine · 2024Article
- Ultrafast Brain MRI at 3 T for MS: Evaluation of a 51-Second Deep Learning-Enhanced T2-EPI-FLAIR Sequence.Diagnostics (Basel, Switzerland) · 2024Article
- Sub-millisecond 2D MRI of the vocal fold oscillation using single-point imaging with rapid encoding.Magma (New York, N.Y.) · 2022Article
- Rotated spiral RARE for high spatial and temporal resolution volumetric arterial spin labeling acquisition.NeuroImage · 2020Article
- Compressed Sensing 3D-GRASE for faster High-Resolution MRI.Magnetic resonance in medicine · 2019Article
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Authors and funding
5 authors.
Funding
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Abstract
purposeHigh-resolution three-dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high-resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D-GRASE sequence and to compare it with a (CS)PI 3D-FSE sequence. Several sampling patterns were investigated to assess their influence on image quality.
methodsThe proposed k-space sampling patterns are based on two undersampled k-space grids, variable density (VD) Poisson-disc, and VD pseudo-random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full-width at half-maximum (FWHM). Prospective CSPI 3D-GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D-GRASE, PI 3D-FSE, and CSPI 3D-FSE acquisitions.
resultsSampling patterns with VD Poisson-disc obtain the lowest coherence for both PD-weighted and
conclusionsCSPI 3D-GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D-GRASE image acquisitions.
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