5 research outputs found
3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images
Endovascular navigation, essential for diagnosing and treating endovascular
diseases, predominantly hinges on fluoroscopic images due to the constraints in
sensory feedback. Current shape reconstruction techniques for endovascular
intervention often rely on either a priori information or specialized
equipment, potentially subjecting patients to heightened radiation exposure.
While deep learning holds potential, it typically demands extensive data. In
this paper, we propose a new method to reconstruct the 3D guidewire by
utilizing CathSim, a state-of-the-art endovascular simulator, and a 3D
Fluoroscopy Guidewire Reconstruction Network (3D-FGRN). Our 3D-FGRN delivers
results on par with conventional triangulation from simulated monoplane
fluoroscopic images. Our experiments accentuate the efficiency of the proposed
network, demonstrating it as a promising alternative to traditional methods.Comment: 11 page
Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching
Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework
for image registration to the indirect setting where a template is registered
against a target that is given through indirect noisy observations. The
registration uses diffeomorphisms that transform the template through a (group)
action. These diffeomorphisms are generated by solving a flow equation that is
defined by a velocity field with certain regularity. The theoretical analysis
includes a proof that indirect image registration has solutions (existence)
that are stable and that converge as the data error tends so zero, so it
becomes a well-defined regularization method. The paper concludes with examples
of indirect image registration in 2D tomography with very sparse and/or highly
noisy data.Comment: 43 pages, 4 figures, 1 table; revise