Abstract
Imaging of the heart during exercise can improve detection and treatment of heart diseases but is challenging using current clinically applied cardiac MRI (cMRI) techniques. Real-time (RT) imaging strategies have recently been proposed for exercise cMRI, but respiratory motion and unreliable cardiac gating introduce significant errors in quantification of cardiac function. Self-navigated cMRI sequences are currently not routinely available in a clinical environment. We aim to establish a method for cardiac and respiratory gated cine exercise cMRI that can be applied in a clinical cMRI setting. We developed a retrospective, image-based cardiac and respiratory gating and reconstruction framework based on widely available highly accelerated dynamic imaging. From the acquired dynamic images, respiratory motion was estimated using manifold learning. Cardiac periodicity was obtained by identifying local maxima in the temporal frequency spectrum of the spatial means of the images. We then binned the dynamic images in respiratory and cardiac phases and subsequently registered and averaged them to reconstruct a respiratory and cardiac gated cine stack. We evaluated our method in healthy volunteers and patients with heart diseases and demonstrate good agreement with existing RT acquisitions (R = .82). We show that our reconstruction pipeline yields better image quality and has lower inter- and intra-observer variability compared to RT imaging. Subsequently, we demonstrate that our method is able to detect a pathological response to exercise in patients with heart diseases, illustrating its potential benefit in cardiac diagnostic and prognostic assessment.
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Ruijsink, B. et al. (2017). Semi-automatic Cardiac and Respiratory Gated MRI for Cardiac Assessment During Exercise. In: Cardoso, M., et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. RAMBO CMMI SWITCH 2017 2017 2017. Lecture Notes in Computer Science(), vol 10555. Springer, Cham. https://doi.org/10.1007/978-3-319-67564-0_9
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DOI: https://doi.org/10.1007/978-3-319-67564-0_9
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