Abstract
ObjectivesTo evaluate an image-navigated isotropic high-resolution 3D late gadolinium enhancement (LGE) prototype sequence with compressed sensing and Dixon water-fat separation in a clinical routine setting.Material and methodsForty consecutive patients scheduled for cardiac MRI were enrolled prospectively and examined with 1.5 T MRI. Overall subjective image quality, LGE pattern and extent, diagnostic confidence for detection of LGE, and scan time were evaluated and compared to standard 2D LGE imaging. Robustness of Dixon fat suppression was evaluated for 3D Dixon LGE imaging. For statistical analysis, the non-parametric Wilcoxon rank sum test was performed.ResultsLGE was rated as ischemic in 9 patients and non-ischemic in 11 patients while it was absent in 20 patients. Image quality and diagnostic confidence were comparable between both techniques (p = 0.67 and p = 0.66, respectively). LGE extent with respect to segmental or transmural myocardial enhancement was identical between 2D and 3D (water-only and in-phase). LGE size was comparable (3D 8.4 ± 7.2 g, 2D 8.7 ± 7.3 g, p = 0.19). Good or excellent fat suppression was achieved in 93% of the 3D LGE datasets. In 6 patients with pericarditis, the 3D sequence with Dixon fat suppression allowed for a better detection of pericardial LGE. Scan duration was significantly longer for 3D imaging (2D median 9:32 min vs. 3D median 10:46 min, p = 0.001).ConclusionThe 3D LGE sequence provides comparable LGE detection compared to 2D imaging and seems to be superior in evaluating the extent of pericardial involvement in patients suspected with pericarditis due to the robust Dixon fat suppression.Key Points• Three-dimensional LGE imaging provides high-resolution detection of myocardial scarring.• Robust Dixon water-fat separation aids in the assessment of pericardial disease.• The 2D image navigator technique enables 100% respiratory scan efficacy and permits predictable scan times.
Highlights
In cardiac MRI (CMR), late gadolinium enhancement (LGE) is an important tool in the assessment of necrosis and fibrosis after myocardial infarction, as well as in various non-ischemic cardiomyopathies, e.g., to determine the severity of infectious myocarditis [1]
Overall image quality and artifacts not related to fat suppression were rated on a 5-point scale for 2D and 3D datasets [29]: 5 = excellent image quality, interpretable with no artifacts; 4 = good image quality, interpretable with minimal artifacts; 3 = average image quality, interpretation mildly degraded by image artifacts; 2 = below average image quality, interpretable but moderately degraded; and 1 = poor image quality, uninterpretable images
Water-fat separation in the 3D LGE water-only images was evaluated using a 5-point scale [29]: 5 = excellent fat suppression quality, interpretable with no artifacts; 4 = good fat suppression, interpretable with minimal artifacts; 3 = average fat suppression, interpretation mildly degraded by image artifacts; 2 = below average image fat suppression, interpretable but moderately degraded; and 1 = poor fat suppression, uninterpretable images
Summary
In cardiac MRI (CMR), late gadolinium enhancement (LGE) is an important tool in the assessment of necrosis and fibrosis after myocardial infarction, as well as in various non-ischemic cardiomyopathies, e.g., to determine the severity of infectious myocarditis [1]. A twodimensional (2D) inversion recovery fast spoiled gradientecho or balanced steady-state free precession (bSSFP) sequence acquired in multiple breath-holds is commonly used to evaluate LGE. Such 2D approaches may suffer from slice misregistration (incomplete breath-holding), artifacts due to respiratory motion, and constraints in spatial resolution [3]. Developed 2D image navigation (iNAV) provides direct respiratory motion tracking of the heart in head–foot and left–right directions [12] This technique outperforms conventional respiratory motion compensation techniques such as diaphragmatic navigator gating [9], because it does not require a motion model and enables 100% respiratory scan efficiency resulting in shorter and predictable scan time, and has been successfully tested in 3D LGE imaging with lower acquired resolution of (2.0 mm)3 [10, 13]. Compressed sensing is based on the three principles of a sparse representation of the acquired object, a pseudo-random sub-sampling of k-space in order to create incoherent (noise-like) undersampling artifacts and a non-linear, iterative image reconstruction [14,15,16,17]
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