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Abstract #4254

Respiratory Motion Detection and Reconstruction Using CAPTURE and Deep Learning for a 0.35T MRI-LINAC System: An Initial Study

Sihao Chen1, Cihat Eldeniz1, Weijie Gan1, Ulugbek Kamilov1, Deshan Yang1, Michael Gach1, and Hongyu An1
1Washington University in St. Louis, Saint Louis, MO, United States

Magnetic Resonance Imaging Guided Linear Accelerator (MRI-LINAC) combines an MRI system with a linear accelerator radiotherapy system to treat patients. The MRI-LINAC uses MR to track organ or lesion motion and gates the radiation beam accordingly. In this study, we demonstrate the combination of a T1-weighted self-navigated respiratory motion detection method (CAPTURE) with a deep learning 4D reconstruction (Phase2Phase, P2P) method to derive the 3D deformable respiratory motion field from data acquired on a 0.35 T ViewRay MRI-LINAC system. This initial study demonstrated promising results despite the low SNR at this field strength.

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