# Maximum Likelihood Estimation of Head Motion using Epipolar Consistency

## TL;DR

This paper introduces a probabilistic maximum likelihood method using Student's t-distribution to estimate and compensate involuntary patient head motion in interventional C-arm imaging, improving image quality during stroke therapy procedures.

## Contribution

It presents a novel probabilistic framework that models image artifacts affecting consistency measures, enhancing motion estimation accuracy in challenging interventional imaging scenarios.

## Key findings

- Effective motion estimation in slow data acquisition settings
- Robustness to artifacts like beam hardening and scatter
- Improved image alignment during interventional procedures

## 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.