2012
DOI: 10.1148/radiol.12112489
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Accelerated Late Gadolinium Enhancement Cardiac MR Imaging with Isotropic Spatial Resolution Using Compressed Sensing: Initial Experience

Abstract: Purpose:To evaluate the use of low-dimensional-structure self-learning and thresholding (LOST) compressed sensing acquisition and reconstruction in the assessment of left atrial (LA) and left ventricular (LV) scar by using late gadolinium enhancement (LGE) magnetic resonance (MR) imaging with isotropic spatial resolution. Materials andMethods:The study was approved by the local institutional review board and was compliant with HIPAA. All subjects provided written informed consent. Twenty-eight patients (eight … Show more

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Cited by 78 publications
(69 citation statements)
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References 32 publications
(14 reference statements)
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“…Imaging parameters were as follows: repetition time = 6.1 ms, echo time = 2.7 ms, flip angle = 25°, field of view = 270 × 270 × 112 mm 3 , spatial resolution =1×1×1 mm 3 , and compressed sensing acceleration factor = 4. 9 A respiratory navigator (2-dimensional spiral pencil beam) placed on the dome of the right hemidiaphragm was used for respiratory motion compensation with use of prospective real-time correction with a 5-mm end-expiration gating window. Saturation bands along the phase-encoding direction were used to reduce fold-over artifacts.…”
Section: Methodsmentioning
confidence: 99%
“…Imaging parameters were as follows: repetition time = 6.1 ms, echo time = 2.7 ms, flip angle = 25°, field of view = 270 × 270 × 112 mm 3 , spatial resolution =1×1×1 mm 3 , and compressed sensing acceleration factor = 4. 9 A respiratory navigator (2-dimensional spiral pencil beam) placed on the dome of the right hemidiaphragm was used for respiratory motion compensation with use of prospective real-time correction with a 5-mm end-expiration gating window. Saturation bands along the phase-encoding direction were used to reduce fold-over artifacts.…”
Section: Methodsmentioning
confidence: 99%
“…Although there are promising studies applying fast CS-MRI in clinical environments [31], [32], [33], most routine clinical MRI scanning is still based on standard fully-sampled Cartesian sequences or is accelerated only using parallel imaging. The main challenges are: (1) satisfying the incoherence criteria required by CS-MRI [1]; (2) the widely applied sparsifying transforms might be too simple to capture complex image details associated with subtle differences of biological tissues, e.g., TV based sparsifying transform penalises local variation in the reconstructed images but can introduce staircase artefacts and the wavelet transform enforces point singularities and isotropic features but orthogonal wavelets may lead to blocky artefacts [34], [35], [36]; (3) nonlinear optimisation solvers usually involve iterative computation that may result in relatively long reconstruction time [1]; (4) inappropriate hyperparameters predicted in current CS-MRI methods can cause over-regularisation that will yield overly smooth and unnatural looking reconstructions or images with residual undersampling artefacts [1].…”
Section: Related Work and Our Contributions A Classic Model-basementioning
confidence: 99%
“…Cerebral MRA is a promising candidate for accelerated acquisition with CS because high-signal vessels are sparse in space. Despite increased investigations into the clinical application of this technique, [13][14][15][16] only a few studies of sparse MRI have been reported in the field of NCE MRA. 17 Introduction of this novel technique into clinical practice will require a valid comparison with an appropriate evaluation that simulates clinical practice.…”
mentioning
confidence: 99%