Generalization of three-dimensional golden-angle radial acquisition to reduce eddy current artifacts in bSSFP CMR imaging

Abstract

Purpose We propose a novel generalization of the three-dimensional double-golden-angle profile ordering, which allows for whole-heart volumetric imaging with retrospective binning and reduced eddy current artifacts. Methods A novel theory bridging the gap between the three-dimensional double golden-angle trajectory, and the two-dimensional tiny-golden-angle trajectory is presented. This enables a class of double golden-angle profile orderings with a smaller angular distance between successive k-space readouts. The novel profile orderings were evaluated through simulations, phantom experiments, and in vivo imaging. Comparisons were made to the original double-golden-angle trajectory. Image uniformity and off-resonance sensitivity were evaluated using phantom measurements, and qualitative image quality was assessed using in vivo images acquired in a healthy volunteer. Results The proposed theory successfully reduced the angular step while maintaining image uniformity after binning. Simulations revealed a slow degradation with decreasing angular steps and an increasing number of physiological bins. The phantom images showed a definite improvement in image uniformity and increased robustness to off-resonance, and in vivo imaging corroborated those findings. Conclusion Reducing the angular step in cardio-respiratory-binned golden-angle imaging shows potential for overcoming eddy current-induced image artifacts associated with 3D golden-angle radial imaging.

Tiny golden angle ultrashort echo‐time lung imaging in mice

“…A major limitation of the proposed technique results from its current restriction to 2D imaging. Encoding schemes providing similar sampling distributions in 3D have been reported 57,58 and have partly been applied to lung imaging. 13 Where in principle a translation of the proposed techniques to 3D is feasible, the resulting long acquisition times required for high-fidelity reconstruction of different respiratory and/or cardiac motion stages will pose challenges, and transfer to dynamic perfusion imaging appears to be impossible, even if advanced parallel of compressed sensing reconstruction or even more advanced deep learning 59,60 approaches are applied.…”

Fetal 3D cardiovascular cine image acquisition using radial sampling and compressed sensing

“…To balance both a uniform k‐space coverage and eddy current artifacts, the fifth pair of golden means from the Fibonacci series was chosen (ie, $$$ {\phi}_1\approx 1.26{}^{\circ} $$$ and $$$ {\phi}_2\approx 0.58{}^{\circ} $$$) 20,21 . Both the azimuthal and polar angle completed a full rotation (ie, they were both defined between 0° and 360°), to minimize step size between successive spokes and thereby decrease eddy current artifacts 22 . A slab‐selective excitation pulse in the maternal feet–head direction was implemented to reduce signal from spins outside the FOV.…”

“…20,21 Both the azimuthal and polar angle completed a full rotation (ie, they were both defined between 0 • and 360 • ), to minimize step size between successive spokes and thereby decrease eddy current artifacts. 22 A slab-selective excitation pulse in the maternal feet-head direction was implemented to reduce signal from spins outside the FOV. The 3D imaging volume covered the fetal thorax and was acquired during maternal free breathing.…”

3D sodium (23Na) magnetic resonance fingerprinting for time‐efficient relaxometric mapping

“…The TE pattern is chosen pseudo‐randomly and is uniformly distributed from the minimal echo time TE min = 0.55 ms to the maximal echo time TE max = TE min + 20 ms. Next, a radial center‐out density‐adapted readout gradient is used for spatial encoding, followed by a rewinder and a spoiler gradient. Each readout direction is successively rotated by the 13th tiny golden angle 28 to achieve full k‐space coverage over the entire measurement. The next cycle within the pulse train starts right after the spoiler gradient, resulting in a variable TR due to the variable TE.…”

“…Furthermore, the initial FA pattern (MRF I) is shown. Last, the optimized pattern (MRF II) can be seen, which is constructed by concatenating the optimized 50-FA-pattern 20 times Each readout direction is successively rotated by the 13th tiny golden angle 28 to achieve full k-space coverage over the entire measurement. The next cycle within the pulse train starts right after the spoiler gradient, resulting in a variable TR due to the variable TE.…”