Golden angle

Abstract: Purpose To develop a fast and flexible free-breathing dynamic volumetric MRI technique, iterative Golden-angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden-angle radial sampling. Methods Radial k-space data are acquired continuously using the golden-angle scheme and sorted into time series by grouping an arbitrary number of consecutive spokes into temporal frames. An iterative reconstruction procedure is then performed on the undersampled time series where joint multicoil sparsity is enforced by applying a total-variation constraint along the temporal dimension. Required coil-sensitivity profiles are obtained from the time-averaged data. Results iGRASP achieved higher acceleration capability than either parallel imaging or coil-by-coil compressed sensing alone. It enabled dynamic volumetric imaging with high spatial and temporal resolution for various clinical applications, including free-breathing dynamic contrast-enhanced imaging in the abdomen of both adult and pediatric patients, and in the breast and neck of adult patients. Conclusion The high performance and flexibility provided by iGRASP can improve clinical studies that require robustness to motion and simultaneous high spatial and temporal resolution. Magn Reson Med 72:707–717, 2014. © 2013 Wiley Periodicals, Inc.

Abstract: Purpose To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Methods Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. Results XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. Conclusion XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. Magn Reson Med 75:775–788, 2016. © 2015 Wiley Periodicals, Inc.

Abstract: Breast tumor diagnosis requires both high spatial resolution to obtain information about tumor morphology and high temporal resolution to probe the kinetics of contrast uptake. Adaptive sampling of k-space allows images in dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) to be reconstructed at various spatial or temporal resolutions from the same dataset. However, conventional radial approaches have limited flexibility that restricts image reconstruction to predetermined resolutions. Golden-angle radial k-space sampling achieves flexibility in-plane with samples that are incremented by the golden angle, which fills two-dimensional (2D) k-space with radial spokes that have a relatively uniform angular distribution for any time interval. We extend this method to three-dimensional (3D) radial sampling, or 3D-Projection Reconstruction (3D-PR) using multidimensional golden means, which are derived from modified Fibonacci sequences by an eigenvalue approach. We quantitatively compare this technique to conventional 3D radial methods in terms of the fluctuation in error caused by undersampling artifacts, and show that the golden 3D-PR method can substantially improve the temporal stability of quantitative measurements made from dynamic images when compared to conventional 3D radial approaches of k-space sampling.

Abstract: In golden angle radial magnetic resonance imaging a constant azimuthal radial profile spacing of $111.246\ldots {}^{\circ }$ guarantees a nearly uniform azimuthal profile distribution in ${\rm k}$ -space for an arbitrary number of radial profiles. Even though this profile order is advantageous for various real-time imaging methods, in combination with balanced steady-state free precession (SSFP) sequences the large azimuthal angle increment may lead to strong image artifacts, due to the varying eddy currents introduced by the rapidly switching gradient scheme. Based on a generalized Fibonacci sequence, a new sequence of smaller irrational angles is introduced ( $49.750\ldots {}^{\circ }, 32.039\ldots {}^{\circ }, 27.198\ldots {}^{\circ }, 23.628\ldots{}^{\circ }, \ldots \!$ ). The subsequent profile orders guarantee the same sampling efficiency as the golden angle if at least a minimum number of radial profiles is used for reconstruction. The suggested angular increments are applied for dynamic imaging of the heart and the temporomandibular joint. It is shown that for balanced SSFP sequences, trajectories using the smaller golden angle surrogates strongly reduce the image artifacts, while the free retrospective choice of the reconstruction window width is maintained.