Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency

Abstract

Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student's t-distribution to model image artifacts that affect the consistency measure without sourcing from motion. arXiv:1812.05405v2 [physics.med-ph]

Motion gradients for epipolar consistency

“…Other imaging artifacts as scatter or beam hardening degrade the EC and appear as noise in the gradient. 7 This is observable in the analytic gradient, whereas the approximation characteristics of the finite differences reveal a smoothed curve.…”

Appearance Learning for Image-Based Motion Estimation in Tomography

“…I-B1) have been used for the compensation of various other image artifacts as beam hardening, scatter correction or truncation correction [37], [38], [39], [40]. This is due to consistency being deduced from a physical model, which only holds on an approximate basis for real applications [41]. Additionally, they are insensitive to certain motion directions and their application is limited to motion patterns outside the acquisition plane [18], [21].…”