Browsing by Subject "Variable density"
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Item Open Access Accelerated phase-cycled SSFP imaging with compressed sensing(Institute of Electrical and Electronics Engineers Inc., 2015) Çukur, T.Balanced steady-state free precession (SSFP) imaging suffers from irrecoverable signal losses, known as banding artifacts, in regions of large B0 field inhomogeneity. A common solution is to acquire multiple phase-cycled images each with a different frequency sensitivity, such that the location of banding artifacts are shifted in space. These images are then combined to alleviate signal loss across the entire field-of-view. Although high levels of artifact suppression are viable using a large number of images, this is a time costly process that limits clinical utility. Here, we propose to accelerate individual acquisitions such that the overall scan time is equal to that of a single SSFP acquisition. Aliasing artifacts and noise are minimized by using a variable-density random sampling pattern in k-space, and by generating disjoint sampling patterns for separate acquisitions. A sparsity-enforcing method is then used for image reconstruction. Demonstrations on realistic brain phantom images, and in vivo brain and knee images are provided. In all cases, the proposed technique enables robust SSFP imaging in the presence of field inhomogeneities without prolonging scan times. © 2014 IEEE.Item Open Access Rapid reconstruction for parallel magnetic resonance imaging with non-Cartesian variable-density sampling trajectories(2020-01) Şenel, Celal FurkanDue to long acquisition times, the use of magnetic resonance imaging (MRI) remains challenging in some applications. Variable-density acquisitions enable scan acceleration while maintaining a desirable trade-off between signal-to-noise ratio (SNR) and spatial resolution. Several image-domain and k-space algorithms were previously proposed for parallel-imaging reconstructions of variabledensity acquisitions. However, these methods involve iterative procedures for non-Cartesian data, resulting in substantial computational burden in particular for three-dimensional (3D) reconstructions. An efficient method based on partially parallel imaging with localized sensitivities (PILS) was recently proposed for fast reconstructions of 2D non-Cartesian data. This thesis introduces a generalized image-domain implementation for 3D non-Cartesian variable-density data, and compares it against conventional gridding, PILS, and ESPIRiT (iterative self-consistent parallel imaging reconstruction using eigenvector maps) reconstructions on brain and knee data accelerated at R=2.5 to 4.2. The results indicate that the proposed 3D variable-FOV method outperforms SOS (sum of squares) and PILS methods, and performs equally or better than ESPIRiT reconstruction at less than half of the processing time required by ESPIRiT. Thus, the proposed method provides fast, high-SNR, artifact-suppressed reconstructions.Item Open Access Statistically segregated k-space sampling for accelerating multiple-acquisition MRI(IEEE, 2019) Şenel, L. Kerem; Kılıç, Toygan; Güngör, Alper; Kopanoğlu, Emre; Güven, H. Emre; Sarıtaş, Emine U.; Koç, Aykut; Çukur, TolgaA central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo $\text{T}_{\text{2}}$ -weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.