Intensity-based three-dimensional to two-dimensional (3D/2D) X-ray image registration algorithms usually require
generating digitally reconstructed radiographs (DRRs) in every iteration during their optimization phase.
Thus a large part of the computation time of such registration algorithms is spent in computing these DRRs. In
a 3D-to-multiple-2D image registration framework where a sequence of DRRs is calculated, not only the computation
but also the memory cost is high. We present an efficient DRR generation method to reduce both costs on
a graphical processing units (GPU) implementation. The method relies on integrating a precomputation stage
and a narrow-band region-of-interest calculation into the DRR generation. We have demonstrated its benefits on
a previously proposed non-rigid 4D-to-multiple-2D image registration framework to estimate cerebral aneurysm
wall motion. The two tested algorithms initially required several hours of highly intensive computation that
involves generating a large number of DRRs in every iteration. In this paper, results on datasets of digital
and physical pulsating cerebral aneurysm phantoms showed a speedup factor of around 50x in the generation of
DRRs. In further image registration based wall motion estimation experiments using our implementation, we
could obtain estimation results through the whole cardiac cycle within 5 minutes without degrading the overall performance.
Endovascular treatment of intracranial aneurysms is a minimally-invasive technique recognized as a valid alternative
to surgical clipping. However, endovascular treatment can be associated to aneurysm recurrence, either
due to coil compaction or aneurysm growth. The quantification of coil compaction or aneurysm growth is usually
performed by manual measurements or visual inspection of images from consecutive follow-ups. Manual measurements
permit to detect large global deformation but might have insufficient accuracy for detecting subtle or
more local changes between images. Image inspection permits to detect a residual neck in the aneurysm but do
not differentiate aneurysm growth from coil compaction. In this paper, we propose to quantify independently coil
compaction and aneurysm growth using non-rigid image registration. Local changes of volume between images
at successive time points are identified using the Jacobian of the non-rigid transformation.
Two different non-rigid registration strategies are applied in order to explore the sensitivity of Jacobian-based
volume changes against the registration method, FFD registration based on mutual information and Demons.
This volume-variation measure has been applied to four patients of which a series of 3D Rotational Angiography
(3DRA) images obtained at different controls separated from two months to two years were available. The
evolution of coil and aneurysm volumes along the period has been obtained separately, which allows distinguishing
between coil compaction and aneurysm growth. On the four cases studied in this paper, aneurysm recurrence
was always associated to aneurysm growth, as opposed to strict coil compaction.
Highly accurate avatars of humans promise a new level of realism in engineering and entertainment applications, including areas such as computer animated movies, computer game development interactive virtual environments and tele-presence. In order to provide high-quality avatars, new techniques for the automatic acquisition and creation are required. A framework for the capture and construction of arbitrary avatars from image data is presented in this paper. Avatars are automatically reconstructed from multiple static images of a human subject by utilizing image information to reshape a synthetic three-dimensional articulated reference model. A pipeline is presented that combines a set of hardware-accelerated stages into one seamless system. Primary stages in this pipeline include pose estimation, skeleton fitting, body part segmentation, geometry construction and coloring, leading to avatars that can be animated and included into interactive environments. The presented system removes traditional constraints in the initial pose of the captured subject by using silhouette-based modification techniques in combination with a reference model. Results can be obtained in near-real time with very limited user intervention.