Patient-specific measurements of cerebral blood flow provide valuable diagnostic information concerning cerebrovascular diseases rather than visually driven qualitative evaluation. In this paper, we present a quantitative method to estimate blood flow parameters with high temporal resolution from digital subtraction angiography (DSA) image sequences. Using a 3D DSA dataset and a 2D+t DSA sequence, the proposed algorithm employs a 1D Computational Fluid Dynamics (CFD) model for estimation of time-dependent flow values along a cerebral vessel, combined with an additional Advection Diffusion Equation (ADE) for contrast agent propagation. The CFD system, followed by the ADE, is solved with a finite volume approximation, which ensures the conservation of mass. Instead of defining a new imaging protocol to obtain relevant data, our cost function optimizes the bolus arrival time (BAT) of the contrast agent in 2D+t DSA sequences. The visual determination of BAT is common clinical practice and can be easily derived from and be compared to values, generated by a 1D-CFD simulation. Using this strategy, we ensure that our proposed method fits best to clinical practice and does not require any changes to the medical work flow. Synthetic experiments show that the recovered flow estimates match the ground truth values with less than 12% error in the mean flow rates.
This study performs 3D to 2D rigid registration of segmented pre-operative CTA coronary arteries with a single segmented intra-operative X-ray Angio frame in both frequency and spatial domains for real-time Angiography interventions by C-arm fluoroscopy. Most of the work on rigid registration in literature required a close initial-
ization of poses and/or positions because of the abundance of local minima and high complexity that searching algorithms face. This study avoids such setbacks by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. First, template DRRs as candidate poses of 3D vessels of segmented CTA are produced by rotating the camera (image intensifier) around the DICOM angle values with a wide range as in C-arm setup. We have compared the 3D poses of template DRRs with the real X-ray after equalizing the scales (due to disparities in focal length distances) in 3 domains, namely Fourier magnitude, Fourier phase and Fourier polar. The best pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that these methods are robust against noise and occlusion which was also validated by our results. Translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of our objective function without local minima due to distance maps. Final results were evaluated in 2D projection space rather than with actual values in 3D due to lack of ground truth, ill-posedness of the problem which we intend to address in future.
Quantification of abdominal aortic deformation is an important requirement for the evaluation of endovascular stenting
procedures and the further refinement of stent graft design. During endovascular aortic repair (EVAR) treatment, the aortic
shape is subject to severe deformation that is imposed by medical instruments such as guide wires, catheters, and, the
stent graft. This deformation can affect the flow characteristics and morphology of the aorta which have been shown to
be elicitors for stent graft failures and be reason for reappearance of aneurysms. We present a method for quantifying the
deformation of an aneurysmatic aorta imposed by an inserted stent graft device. The outline of the procedure includes
initial rigid alignment of the two abdominal scans, segmentation of abdominal vessel trees, and automatic reduction of
their centerline structures to one specified region of interest around the aorta. This is accomplished by preprocessing and
remodeling of the pre- and postoperative aortic shapes before performing a non-rigid registration. We further narrow the
resulting displacement fields to only include local non-rigid deformation and therefore, eliminate all remaining global rigid
transformations. Finally, deformations for specified locations can be calculated from the resulting displacement fields.
In order to evaluate our method, experiments for the extraction of aortic deformation fields are conducted on 15 patient
datasets from endovascular aortic repair (EVAR) treatment. A visual assessment of the registration results and evaluation
of the usage of deformation quantification were performed by two vascular surgeons and one interventional radiologist
who are all experts in EVAR procedures.
In the current clinical workflow of minimally invasive aortic procedures navigation tasks are performed under 2D
or 3D angiographic imaging. Many solutions for navigation enhancement suggest an integration of the preoperatively
acquired computed tomography angiography (CTA) in order to provide the physician with more image
information and reduce contrast injection and radiation exposure. This requires exact registration algorithms
that align the CTA volume to the intraoperative 2D or 3D images. Additional to the real-time constraint, the registration
accuracy should be independent of image dissimilarities due to varying presence of medical instruments
and contrast agent. In this paper, we propose efficient solutions for image-based 2D-3D and 3D-3D registration
that reduce the dissimilarities by image preprocessing, e.g. implicit detection and segmentation, and adaptive
weights introduced into the registration procedure. Experiments and evaluations are conducted on real patient
Conference Committee Involvement (3)
MICCAI-Workshop on Computer Assisted Stenting
1 October 2012 |
MICCAI-Workshop on Computation and Visualization of (Intra)Vascular Images
18 September 2011 |
Information Processing in Computer Assisted Interventions