Technical note: Minimizing CIED artifacts on a 0.35 T MRI‐Linac using deep learning

Abstract

AbstractBackgroundArtifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)‐guided radiotherapy (MRgRT).PurposeThis study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady‐state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT.MethodsFourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI‐Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave‐One‐Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole‐heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal‐to‐noise ratio (PSNR), and multiscale structural similarity (MS‐SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD.ResultsFor the whole‐heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877 mm; and 3) Mean 95% HD dropped from 10.236 to 7.700 mm. For the whole‐body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS‐SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart.ConclusionCycleGAN‐generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.