We propose a Markov Random Field (MRF) formulation for the intensity-based N-view 2D-3D registration problem. The
transformation aligning the 3D volume to the 2D views is estimated by iterative updates obtained by discrete optimization
of the proposed MRF model. We employ a pairwise MRF model with a fully connected graph in which the nodes represent
the parameter updates and the edges encode the image similarity costs resulting from variations of the values of adjacent
nodes. A label space refinement strategy is employed to achieve sub-millimeter accuracy. The evaluation on real and
synthetic data and comparison to state-of-the-art method demonstrates the potential of our approach.
Tracer kinetic modeling with dynamic contrast enhanced MRI (DCE-MRI) and the quantification of the kinetic parameters are active fields of research which have the potential to improve the measurement of renal function. However, the strong coronal motion of the kidney in the time series inhibits an accurate assessment of the kinetic parameters. Automatic motion correction is challenging due to the large movement of the kidney and the strong intensity changes caused by the injected bolus. In this work, we improve the quantification results by a template matching motion correction method using a gradient-based similarity measure. Thus, a tedious manual motion correction is replaced by an automatic procedure. The only remaining user interaction is reduced to a selection of a reference slice and a coarse manual segmentation of the kidney in this slice. These steps do not present an overhead to the interaction needed for the assessment of the kinetic parameters. In order to achieve reliable and fast results, we constrain the degrees of freedom for the correction method as far as possible. Furthermore, we compare our method to deformable registration using the same similarity measure. In all our tests, the presented template matching correction was superior to the deformable approach in terms of reliability, leading to more accurate parameter quantification. The evaluation on 10 patient data series with 180-230 images each demonstrate that the quantitative analysis by a two-compartment model can be improved by our method.
Nowadays, hepatic artery catheterizations are performed under live 2D X-ray fluoroscopy guidance, where the visualization of blood vessels requires the injection of contrast agent. The projection of a 3D static roadmap of the complex branches of the liver artery system onto 2D fluoroscopy images can aid catheter navigation and minimize the use of contrast agent. However, the presence of a significant hepatic motion due to patient's respiration necessitates a real-time
motion correction in order to align the projected vessels. The objective of our work is to introduce dynamic roadmaps into
clinical workflow for hepatic artery catheterizations and allow for continuous visualization of the vessels in 2D fluoroscopy
images without additional contrast injection. To this end, we propose a method for real-time estimation of the apparent displacement of the hepatic arteries in 2D flouroscopy images. Our approach approximates respiratory motion of hepatic arteries from the catheter motion in 2D fluoroscopy images. The proposed method consists of two main steps. First, a filtering is applied to 2D fluoroscopy images in order to enhance the catheter and reduce the noise level. Then, a part of the catheter is tracked in the filtered images using template matching. A dynamic template update strategy makes our method robust to deformations. The accuracy and robustness of the algorithm are demonstrated by experimental studies on 22 simulated and 4 clinical sequences containing 330 and 571 image frames, respectively.
Alignment of angiographic preoperative 3D scans to intraoperative 2D projections is an important issue for 3D
depth perception and navigation during interventions. Currently, in a setting where only one 2D projection is
available, methods employing a rigid transformation model present the state of the art for this problem. In
this work, we introduce a method capable of deformably registering 3D vessel structures to a respective single
projection of the scene. Our approach addresses the inherent ill-posedness of the problem by incorporating a
priori knowledge about the vessel structures into the formulation. We minimize the distance between the 2D
points and corresponding projected 3D points together with regularization terms encoding the properties of
length preservation of vessel structures and smoothness of deformation. We demonstrate the performance and
accuracy of the proposed method by quantitative tests on synthetic examples as well as real angiographic scenes.