Date of Award
Spring 5-15-2023
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Magnetic resonance image guided radiation therapy (MRgRT) devices are a recently developed technology that integrate the excellent soft tissue contrast and real-time imaging capabilities of MRI with a medical linear accelerator (Linac). This provides an unprecedented ability to guide and adapt radiation therapy treatments based on real-time cine imaging. However, the merging of these technologies has come with unique challenges. MRI lacks the geometric fidelity of computed tomography (CT). Spatial inaccuracies in MRI can result from magnetic field (B0) or center frequency variations, gradient-induced eddy currents, and magnetic field gradient imperfections (e.g., nonlinearities, poor calibration, concomitant fields, and unsatisfactory electronic fidelity). Previous work identified gantry angle dependent shifts in the imaging isocenter of a commercial 0.35 T MRI-Linac. Additionally, the balanced steady state free precession (bSSFP) sequences used in MRgRT offer excellent signal to noise ratios (SNRs) and temporal resolution, but require high levels of B0 homogeneity, B0 stability, and precise control over the gradient systems. Banding artifacts appear in the resulting images if these stipulations are violated and intravoxel dephasing approaches an odd multiple of π. Rotation of the radiation therapy gantry also results electromagnetic interference (EMI). The gantry-related EMI causes banding artifacts on images collected during that time. Similarly, cardiac implanted electronic devices (CIEDs) result in magnetic susceptibility artifacts primarily due to ferromagnetic components. These artifacts manifest as banding artifacts in bSSFP images and make tracking structures in or near the heart challenging during treatment imaging. The work presented in this dissertation investigates and quantifies the causes of imaging isocenter shifts, develops a method for real-time B0 compensation during rotation of the radiation therapy gantry, and introduces a deep learning solution to CIED induced artifacts on a commercial low-field MRgRT system.
Language
English (en)
Chair
H M. Gach
Committee Members
Abhinav Jha, Hongyu An, Geoffrey Hugo, Umberto Villa,