Çukur, T.2016-02-082016-02-0820150278-0062http://hdl.handle.net/11693/24658Balanced 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.EnglishBanding artifactCompressed sensingMagnetic resonance imaging (MRI)Phase cyclingRandom undersamplingSteady - state free precession (SSFP)Variable densityCompressed sensingSignal reconstructionBanding artifactPhase cyclingRandom under samplingsSteady state free precessionsVariable densityMagnetic resonance imagingAccelerationArtifact reductionBrain sizeDensityGray matterImage qualityImage reconstructionImagingNeuroimagingNuclear magnetic resonance imagingPhantomRadiological proceduresSignal noise ratioSteady state free precession imagingWhite matterAlgorithmAnatomy and histologyImage processingKneeProceduresAlgorithmsArtifactsBrainHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingPhantomsSignal - To - Noise RatioAccelerated phase-cycled SSFP imaging with compressed sensingArticle10.1109/TMI.2014.2346814