Surgical guidance during minimally invasive intervention could be greatly enhanced if the 3D location and
orientation of instruments, especially catheters, is available. In this paper, we present a new method for the 3D
reconstruction of deforming curvilinear objects such as catheters, using the framework of Non-Rigid Structurefrom-
Motion (NRSfM). We combine NRSfM with a kinematics model from the field of Robotics, which provides
a low-dimensional parametrization of the object deformation. This is used in the context of an X-ray imaging
system where multiple views are acquired with a small view separation. We show that using such a kinematics
model, a non-linear optimization scheme succeeds in retrieving the deformable 3D pose from the 2D projections.
Experiments on synthetic and real X-ray data show promising results of the proposed method as compared to
This work aims at defining an information-theoretic quality assessment technique for cardiovascular X-ray
images, using a full-reference scheme (relying on averaging a sequence to obtain a noiseless reference). With the
growth of advanced signal processing in medical imaging, such an approach will enable objective comparisons
of the quality of processed images. A concept for describing the quality of an image is to express it in terms
of its information capacity. Shannon has derived this capacity for noisy channel coding. However, for X-ray
images, the noise is signal-dependent and non-additive, so that Shannon's theorem is not directly applicable.
To overcome this complication, we exploit the fact that any invertible mapping on a signal does not change
its information content. We show that it is possible to transform the images in such a way that the Shannon
theorem can be applied. A general method for calculating such a transformation is used, given a known relation
between signal mean and noise standard deviation. After making the noise signal-independent, it is possible to
assess the information content of an image and to calculate an overall quality metric (e.g. information capacity)
which includes the effects of sharpness, contrast and noise. We have applied this method on phantom images
under different acquisition conditions and computed the information capacity for those images. We aim to show
that the results of this assessment are consistent with variations in noise, contrast and sharpness, introduced by
system settings and image processing.