C-arm images suffer from pose dependant distortion, which needs to be corrected for intra-operative quantitative
3D surgical guidance. Several distortion correction techniques have been proposed in the literature, the current
state of art using a dense grid pattern rigidly attached to the detector. These methods become cumbersome
for intra-operative use, such as 3D reconstruction, since the grid pattern interferes with patient anatomy. The
primary contribution of this paper is a framework to statistically analyze the distortion pattern which enables
us to study alternate intra-operative distortion correction methods. In particular, we propose a new phantom
that uses very few BBs, and yet accurately corrects for distortion.
The high dimensional space of distortion pattern can be effectively characterized by principal component analysis
(PCA). The analysis shows that only first three eigen modes are significant and capture about 99% of the
variation. Phantom experiments indicate that distortion map can be recovered up to an average accuracy of
less than 0.1 mm/pixel with these three modes. With this prior statistical knowledge, a subset of BBs can
be sufficient to recover the distortion map accurately. Phantom experiments indicate that as few as 15 BBs
can recover distortion with average error of 0.17 mm/pixel, accuracy sufficient for most clinical applications.
These BBs can be arranged on the periphery of the C-arm detector, minimizing the interference with patient
anatomy and hence allowing the grid to remain attached to the detector permanently. The proposed method
is fast, economical, and C-arm independent, potentially boosting the clinical viability of applications such as
quantitative 3D fluoroscopic reconstruction.