Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy

TL;DR

The paper investigates motion artifacts in high-resolution in-vivo X-ray microscopy of murine bone and tests whether epipolar consistency conditions (ECC) can correct rigid-motion-induced inconsistencies. ECC relies on redundancy across projection data and minimizes mismatches between epipolar lines to align projection geometries. Using an ex-vivo tibia with 1601 projections and a spline-based six-degree-of-freedom motion model, the authors compare optimization scenarios with a gradient-free Nelder–Mead approach. Results show ECC improves alignment for out-of-plane motion but cannot fully compensate six-DOF motion, suggesting ECC as a valuable first alignment step before reconstruction-based motion correction in preclinical XRM, with limitations due to geometry, truncation, and noise.

Abstract

Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are characteristic of osteoporosis. The complexity of this method stems from the requirement for high-quality 3D reconstructions of the murine bones. However, respiratory motion and muscle relaxation lead to inconsistencies in the projection data which result in artifacts in uncompensated reconstructions. Motion compensation using epipolar consistency conditions (ECC) has previously shown good performance in clinical CT settings. Here, we explore whether such algorithms are suitable for correcting motion-corrupted XRM data. Different rigid motion patterns are simulated and the quality of the motion-compensated reconstructions is assessed. The method is able to restore microscopic features for out-of-plane motion, but artifacts remain for more realistic motion patterns including all six degrees of freedom of rigid motion. Therefore, ECC is valuable for the initial alignment of the projection data followed by further fine-tuning of motion parameters using a reconstruction-based method.

Figures (8)

• Figure 1.1: The original projection matrices are first manipulated and then recovered by minimizing the inconsistency in the projection images measured by ECC.
• Figure 1.2: OOP only before optimization
• Figure 1.3: IP only before optimization
• Figure 1.4: Full rigid before optimization
• Figure 1.5: Original