Evaluation of the impact of cardiac implantable electronic devices on cine MRI for real-time adaptive cardiac radioablation on a 1.5 T MR-linac

Abstract

Background: Stereotactic arrhythmia radioablation (STAR) is a novel treatment approach for refractory ventricular tachycardia (VT). The risk of treatment-induced toxicity and geographic miss can be reduced with online MRI-guidance on an MR-linac. However, most VT patients carry cardiac implantable electronic devices (CIED), which compromise MR images. Purpose: Robust MR-linac imaging sequences are required for cardiac visualization and accurate motion monitoring in presence of a CIED during MRI-guided STAR. We optimized two clinically available cine sequences for cardiorespiratory motion estimation in presence of a CIED on a 1.5 T MR-linac. The image quality, motion estimation accuracy, and geometric fidelity using these cine sequences were evaluated. Methods: Clinically available 2D balanced steady-state free precession (bSSFP, voxel size = 3.0 (Formula presented.) 3.0 (Formula presented.) 10 mm3, Tscan = 96 ms, bandwidth (BW) = 1884 Hz/px) and (Formula presented.) -spoiled gradient echo ((Formula presented.) -GRE, voxel size = 4.0 (Formula presented.) 4.0 (Formula presented.) 10 mm3, Tscan = 97 ms, BW = 500 Hz/px) sequences were adjusted for real-time cardiac visualization and cardiorespiratory motion estimation on a 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden), while complying with safety guidelines for MRI in presence of CIEDs (specific absorption rate (Formula presented.) 2 W/kg and (Formula presented.) 80 mT/s). Cine acquisitions were performed in five healthy volunteers, with and without an implantable cardioverter– defibrillator (ICD) placed on the clavicle, and a VT patient. Generalized divergence-curl (GDC) deformable image registration (DIR) was used for automated landmark motion estimation in the left ventricle (LV). Gaussian processes (GP), a machine-learning technique, was trained using GDC landmarks and deployed for real-time cardiorespiratory motion prediction. (Formula presented.) -mapping was performed to assess geometric image fidelity in the presence of CIEDs. Results: CIEDs introduced banding artifacts partially obscuring cardiac structures in bSSFP acquisitions. In contrast, the (Formula presented.) -GRE was more robust to CIED-induced artifacts at the expense of a lower signal-to-noise ratio. In presence of an ICD, image-based cardiorespiratory motion estimation was possible for 85% (100%) of the volunteers using the bSSFP ((Formula presented.) -GRE) sequence. The in-plane 2D root-mean-squared deviation (RMSD) range between GDC-derived landmarks and manual annotations using the bSSFP (T1-GRE) sequence was 3.1–3.3 (3.3–4.1) mm without ICD and 4.6–4.6 (3.2–3.3) mm with ICD. Without ICD, the RMSD between the GP-predictions and GDC-derived landmarks ranged between 0.9 and 2.2 mm (1.3–3.0 mm) for the bSSFP (T1-GRE) sequence. With ICD, the RMSD between the GP-predictions and GDC-derived landmarks ranged between 1.3 and 2.2 mm (1.2–3.2 mm) using the bSSFP (T1-GRE) sequence resulting in an RMSD-increase of 42%–143% (bSSFP) and −61%–142% (T1-GRE). Lead-induced spatial distortions ranged between −0.2 and 0.2 mm (−0.7–1.2 mm) using the bSSFP ((Formula presented.) -GRE) sequence. The 98th percentile range of the spatial distortions in the gross target volume of the patient was between 0.0 and 0.4 mm (0.0–1.8 mm) when using bSSFP ((Formula presented.) -GRE). Conclusions: Tailored bSSFP and (Formula presented.) -GRE sequences can facilitate real-time cardiorespiratory estimation using GP trained with GDC-derived landmarks in the majority of landmark locations in the LV despite the presence of CIEDs. The need for high temporal resolution noticeably reduced achievable spatial resolution of the cine MRIs. However, the effect of the CIED-induced artifacts is device, patient and sequence dependent and requires specific assessment per case.