for imaging of patients with Late

: Background Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) of ventricular scar has been shown to be accurate for detection and characterization of arrhythmia substrates. However, the majority of patients referred for ventricular tachycardia (VT) ablation have an implantable cardioverter-defibrillator (ICD), which obscures image integrity and the clinical utility of MRI. Objective The purpose of this study was to develop and validate a w ideband LGE MRI technique for device artifact removal. Methods A novel wideband LGE MRI technique was developed to allow for improved scar evaluation on patients with ICDs. The wideband technique and the standard LGE MRI were tested on 18 patients with ICDs. VT ablation was performed in 13 of 18 patients with either endocardial and/or epicardial approach and the correlation between the scar identified on MRI and electroanatomic mapping (EAM) was analyzed. Results Hyperintensity artifact was present in 16 of 18 of patients using standard MRI, which was eliminated using the wideband LGE and allowed for MRI interpretation in 15 of 16 patients. All patients had ICD lead characteristics confirmed as unchanged post-MRI and had no adverse events. LGE scar was seen in 11 of 18 patients. Among the 15 patients in whom wideband LGE allowed visualization of myocardium, 10 had LGE scar and 5 had normal myocardium in the regions with image artifacts when using the standard LGE. The left ventricular scar size measurements using wideband MRI and EAM were correlated with R 2 = 0.83 and P =. 00003. Conclusion Wideband LGE MRI improves the ability to visualize myocardium for clinical interpretation, which correlated well with EAM findings during VT ablation. © 2014 Heart Rhythm Society. Diffusion tensor magnetic resonance imaging (DT-MRI) is a technique used to quantify the microstruc- tural organization of biological tissues. Multiple images are necessary to reconstruct the tensor data and each acquisition is subject to complex thermal noise. As such, measures of tensor invariants, which characterize components of tensor shape, derived from the tensor data will be biased from their true values. Previous work has examined this bias, but over a narrow range of tensor shape. Herein, we deﬁne the mathematics for constructing a tensor from tensor invariants, which permits an intuitive and principled means for building tensors with a complete range of tensor shape and salient microstructural properties. Thereafter, we use this development to evaluate by simulation the effects of noise on characterizing tensor shape over the complete space of tensor shape for three encoding schemes with different SNR and gradient directions. We also deﬁne a new framework for determining the distribution of the true values of tensor invariants given their measures, which provides guidance about the conﬁdence the observer should have in the measures. Finally, we present the statistics of tensor invariant estimates over the complete space of tensor shape to demonstrate how the noise sensitivity of tensor invariants varies across the space of tensor shape as well as how the imaging protocol impacts measures of tensor invariants.