Person:

Roujol, Sébastien

Electro-anatomical voltage mapping (EAM) is an invasive technique used for the identification of ventricular tachycardia (VT) substrate and subsequent guidance of VT ablation [1]. The mapping of VT substrate is very time consuming procedure, requires highly skilled electrophysiologist, is associated with patient risk and is an invasive procedure. Late gadolinium enhancement (LGE) MRI allows non-invasive evaluation of 3D structure of scar. Therefore, LGE has the potential to identify the VT substrate and can now be integrated in the current clinical platform for guidance of VT ablation as a roadmap. However, fusion of the two imaging modality is very challenging due to respiratory and cardiac motion during the mapping which results in large errors in data fusion. Our aim in this study is to develop a novel algorithm to detect the respiratory and cardiac-induced motion of the mapping catheter during the VT ablation to facilitate integration of LGE MRI to EAM data.

Background: To investigate the feasibility of accelerated electrocardiogram (ECG)-triggered contrast enhanced pulmonary vein magnetic resonance angiography (CE-PV MRA) with isotropic spatial resolution using compressed sensing (CS). Methods: Nineteen patients (59 ± 13 y, 11 M) referred for MR were scanned using the proposed accelerated free breathing ECG-triggered 3D CE-PV MRA sequence (FOV = 340 × 340 × 110 mm3, spatial resolution = 1.5 × 1.5 × 1.5 mm3, acquisition window = 140 ms at mid diastole and CS acceleration factor = 5) and a conventional first-pass breath-hold non ECG-triggered 3D CE-PV MRA sequence. CS data were reconstructed offline using low-dimensional-structure self-learning and thresholding reconstruction (LOST) CS reconstruction. Quantitative analysis of PV sharpness and subjective qualitative analysis of overall image quality were performed using a 4-point scale (1: poor; 4: excellent). Results: Quantitative PV sharpness was increased using the proposed approach (0.73 ± 0.09 vs. 0.51 ± 0.07 for the conventional CE-PV MRA protocol, p < 0.001). There were no significant differences in the subjective image quality scores between the techniques (3.32 ± 0.94 vs. 3.53 ± 0.77 using the proposed technique). Conclusions: CS-accelerated free-breathing ECG-triggered CE-PV MRA allows evaluation of PV anatomy with improved sharpness compared to conventional non-ECG gated first-pass CE-PV MRA. This technique may be a valuable alternative for patients in which the first pass CE-PV MRA fails due to inaccurate first pass timing or inability of the patient to perform a 20–25 seconds breath-hold.

Accurate fusion of late gadolinium enhancement magnetic resonance imaging (MRI) and electro-anatomical voltage mapping (EAM) is required to evaluate the potential of MRI to identify the substrate of ventricular tachycardia. However, both datasets are not acquired at the same cardiac phase and EAM data is corrupted with respiratory motion limiting the accuracy of current rigid fusion techniques. Knowledge of cardiac and respiratory motion during EAM is thus required to enhance the fusion process. In this study, we propose a novel approach to characterize both cardiac and respiratory motion from EAM data using the temporal evolution of the 3D catheter location recorded from clinical EAM systems. Cardiac and respiratory motion components are extracted from the recorded catheter location using multi-band filters. Filters are calibrated for each EAM point using estimates of heart rate and respiratory rate. The method was first evaluated in numerical simulations using 3D models of cardiac and respiratory motions of the heart generated from real time MRI data acquired in 5 healthy subjects. An accuracy of 0.6–0.7 mm was found for both cardiac and respiratory motion estimates in numerical simulations. Cardiac and respiratory motions were then characterized in 27 patients who underwent LV mapping for treatment of ventricular tachycardia. Mean maximum amplitude of cardiac and respiratory motion was 10.2±2.7 mm (min = 5.5, max = 16.9) and 8.8±2.3 mm (min = 4.3, max = 14.8), respectively. 3D Cardiac and respiratory motions could be estimated from the recorded catheter location and the method does not rely on additional imaging modality such as X-ray fluoroscopy and can be used in conventional electrophysiology laboratory setting.

Background: To evaluate and quantify the impact of a novel image-based motion correction technique in myocardial T2 mapping in terms of measurement reproducibility and spatial variability. Methods: Twelve healthy adult subjects were imaged using breath-hold (BH), free breathing (FB), and free breathing with respiratory navigator gating (FB + NAV) myocardial T2 mapping sequences. Fifty patients referred for clinical CMR were imaged using the FB + NAV sequence. All sequences used a T2 prepared (T2prep) steady-state free precession acquisition. In-plane myocardial motion was corrected using an adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC). DICE similarity coefficient (DSC) and myocardial boundary errors (MBE) were measured to quantify the motion estimation accuracy in healthy subjects. T2 mapping reproducibility and spatial variability were evaluated in healthy subjects using 5 repetitions of the FB + NAV sequence with either 4 or 20 T2prep echo times (TE). Subjective T2 map quality was assessed in patients by an experienced reader using a 4-point scale (1-non diagnostic, 4-excellent). Results: ARCTIC led to increased DSC in BH data (0.85 ± 0.08 vs. 0.90 ± 0.02, p = 0.007), FB data (0.78 ± 0.13 vs. 0.90 ± 0.21, p < 0.001), and FB + NAV data (0.86 ± 0.05 vs. 0.90 ± 0.02, p = 0.002), and reduced MBE in BH data (0.90 ± 0.40 vs. 0.64 ± 0.19 mm, p = 0.005), FB data (1.21 ± 0.65 vs. 0.63 ± 0.10 mm, p < 0.001), and FB + NAV data (0.81 ± 0.21 vs. 0.63 ± 0.08 mm, p < 0.001). Improved reproducibility (4TE: 5.3 ± 2.5 ms vs. 4.0 ± 1.5 ms, p = 0.016; 20TE: 3.9 ± 2.3 ms vs. 2.2 ± 0.5 ms, p = 0.002), reduced spatial variability (4TE: 12.8 ± 3.5 ms vs. 10.3 ± 2.5 ms, p < 0.001; 20TE: 9.7 ± 3.5 ms vs. 7.5 ± 1.4 ms) and improved subjective score of T2 map quality (3.43 ± 0.79 vs. 3.69 ± 0.55, p < 0.001) were obtained using ARCTIC. Conclusions: The ARCTIC technique substantially reduces spatial mis-alignment among T2-weighted images and improves the reproducibility and spatial variability of in-vivo T2 mapping.