Recent studies have found correlation between the risk of rupture of saccular aneurysms and their morphological
characteristics, such as volume, surface area, neck length, among others. For reliably exploiting these parameters
in endovascular treatment planning, it is crucial that they are accurately quantified. In this paper, we present
a novel framework to assist physicians in accurately assessing saccular aneurysms and efficiently planning for
endovascular intervention. The approach consists of automatically segmenting the pathological vessel, followed
by the construction of its surface representation. The aneurysm is then separated from the vessel surface
through a graph-cut based algorithm that is driven by local geometry as well as strong prior information. The
corresponding healthy vessel is subsequently reconstructed and measurements representing the patient-specific
geometric parameters of pathological vessel are computed. To better support clinical decisions on stenting and
device type selection, the placement of virtual stent is eventually carried out in conformity with the shape of the
diseased vessel using the patient-specific measurements. We have implemented the proposed methodology as a
fully functional system, and extensively tested it with phantom and real datasets.
In this paper we present a learning-based guidewire localization algorithm which can be constrained by user inputs. The
proposed algorithm automatically localizes guidewires in fluoroscopic images. In cases where the results are not satisfactory,
the user can provide input to constrain the algorithm by clicking on the guidewire segment missed by the detection
algorithm. The algorithm then re-localizes the guidewire and updates the result in less than 0.3 second. In extreme cases,
more constraints can be provided until a satisfactory result is reached. The proposed algorithm can not only serve as an
efficient initialization tool for guidewire tracking, it can also serve as an efficient annotation tool, either for cardiologists
to mark the guidewire, or to build up a labeled database for evaluation. Through the improvement of the initialization of
guidewire tracking, it also helps to improve the visibility of the guidewire during interventional procedures. Our study
shows that even highly complicated guidewires can mostly be localized within 5 seconds by less than 6 clicks.
Digital subtraction angiography (DSA) is a well-known technique for improving the visibility and perceptibility of
blood vessels in the human body. Coronary DSA extends conventional DSA to dynamic 2D fluoroscopic sequences
of coronary arteries which are subject to respiratory and cardiac motion. Effective motion compensation is the
main challenge for coronary DSA. Without a proper treatment, both breathing and heart motion can cause
unpleasant artifacts in coronary subtraction images, jeopardizing the clinical value of coronary DSA. In this
paper, we present an effective method to separate the dynamic layer of background structures from a fluoroscopic
sequence of the heart, leaving a clean layer of moving coronary arteries. Our method combines the techniques
of learning-based vessel detection and robust motion estimation to achieve reliable motion compensation for
coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where
the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing
breathing and cardiac movement. Perceptibility improvement is significant especially for very thin vessels. The
potential clinical benefit is expected in the context of obese patients and deep angulation, as well as in the
reduction of contrast dose in normal size patients.
In this paper, we present a novel hierarchical framework of guidewire tracking for image-guided interventions. Our method
can automatically and robustly track a guidewire in fluoroscopy sequences during interventional procedures. The method
consists of three main components: learning based guidewire segment detection, robust and fast rigid tracking, and nonrigid
guidewire tracking. Each component aims to handle guidewire motion at a specific level. The learning based segment
detection identifies small segments of a guidewire at the level of individual frames, and provides unique primitive features
for subsequent tracking. Based on identified guidewire segments, the rigid tracking method robustly tracks the guidewire
across successive frames, assuming that a major motion of guidewire is rigid, mainly caused by the breathing motion and
table movement. Finally, a non-rigid tracking algorithm is applied to finely deform the guidewire to provide accurate shape.
The presented guidewire tracking method has been evaluated on a test set of 47 sequences with more than 1000 frames.
Quantitative evaluation demonstrates that the mean tracking error on the guidewire body is less than 2 pixels. Therefore
the presented guidewire tracking method has a great potential for applications in image guided interventions.