Kawasaki disease, a childhood pathology, is marked by the potential for coronary artery complications, which can lead to the dilation or inflammation of the blood vessel wall if left untreated. Intravascular Optical coherence tomography (IVOCT) was introduced for intravascular imaging of coronary arteries to provide valuable navigation guidance information to cardiologists. It requires a skilled operator, and the acquisition protocol is complex. The goal of this study is to present a framework to reproduce patient specific coronary OCT phantoms using polyvinyl alcohol cryogel (PVA-c), which can be used for training cardiologists and for better understanding of the OCT image formation process. This innovative approach enables us to produce phantoms with both mechanical and optical properties very similar to human tissue. To produce these phantoms, we design and print in 3D modular cylindrical molds from real OCT arterial images. A mixture of PVA is poured into the molds and submitted to three thaw and freeze cycles to create soft tissue that represent coronary arteries affected by Kawasaki disease. Once the phantoms have been created, OCT pull-back sequences are acquired and compared to the original images. We acknowledged that our PVA-c phantoms reproduces morphological shape and visual appearance on OCT very similar to human tissue. This holds true even when applied to extremely small morphologies.
Skeletal maturity assessment is an important step in the treatment of adolescent idiopathic scoliosis (AIS). Traditional methods rely on the bone age assessment using 2D X-ray radiography. Assessment is generally performed using the Risser stages that is routinely used to assess the skeletal maturity through the observation of the level of ossification of iliac crests. This bone maturity assessment method is preferred in AIS but in practice, shows a rather high-level interobserver variability. This study aims to use an automatic Risser stage classification for a longitudinal study of follow-up visits to observe growth indicators of AIS patients. A regression model will then be used to evaluate the maturity changes of patients from à Risser stage to another. For the classification task, the pre-trained model of VGG16 was implemented with Python 3.10. The network parameters were changed since the task we were training it for contained a smaller dataset. The first experiments of this work were for the classification of the patient’s Risser signs. After several tests of optimization of the SVR classifiers hyperparameter, a mean square error of 0.38, a mean absolute error of 0.31 and an R2 of 0.33. An optimization of the network and a pre-processing of the images will be done in the next phases of this project.
3D reconstruction of vessels from 2D X-ray angiography is highly relevant to improve the visualization and the
assessment of vascular structures such as pulmonary arteries by interventional cardiologists. However, to ensure
a robust and accurate reconstruction, C-arm gantry parameters must be properly calibrated to provide clinically
acceptable results. Calibration procedures often rely on calibration objects and complex protocol which is not
adapted to an intervention context. In this study, a novel calibration algorithm for C-arm gantry is presented
using the instrumentation such as catheters and guide wire. This ensures the availability of a minimum set of
correspondences and implies minimal changes to the clinical workflow. The method was evaluated on simulated
data and on retrospective patient datasets. Experimental results on simulated datasets demonstrate a calibration
that allows a 3D reconstruction of the guide wire up to a geometric transformation. Experiments with patients
datasets show a significant decrease of the retro projection error to 0.17 mm 2D RMS. Consequently, such
procedure might contribute to identify any calibration drift during the intervention.
Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.
Percutaneous cardiac interventions rely mainly on the experience of the cardiologist to safely navigate inside
soft tissues vessels under X-ray angiography guidance. Additional navigation guidance tool might contribute to
improve reliability and safety of percutaneous procedures. This study focus on major aorta-pulmonary collateral
arteries (MAPCAs) which are pediatric structures. We present a fully automatic intensity-based 3D/2D
registration method that accurately maps pre-operatively acquired 3D tomographic vascular data of a newborn
patient over intra-operatively acquired angiograms. The tomographic dataset 3D pose is evaluated by comparing
the angiograms with simulated X-ray projections, computed from the pre-operative dataset with a proposed
splatting-based projection technique. The rigid 3D pose is updated via a transformation matrix usually defined
in respect of the C-Arm acquisition system reference frame, but it can also be defined in respect of the projection
plane local reference frame. The optimization of the transformation is driven by two algorithms. First the hill
climbing local search and secondly a proposed variant, the dense hill climbing. The latter makes the search space
denser by considering the combinations of the registration parameters instead of neighboring solutions only.
Although this study focused on the registration of pediatric structures, the same procedure could be applied for
any cardiovascular structures involving CT-scan and X-ray angiography. Our preliminary results are promising
that an accurate (3D TRE 0.265 ± 0.647mm) and robust (99% success rate) bi-planes registration of the aorta
and MAPCAs from a initial displacement up to 20mm and 20° can be obtained within a reasonable amount of
time (13.7 seconds).
This document presents a novel method for the problem of image segmentation, based on random-walks. This
method shares similarities with the Mean-shift algorithm, as it finds the modes of the intensity histogram of
images. However, unlike Mean-shift, our proposed method is stochastic and also provides class membership
probabilities. Also, unlike other random-walk based methods, our approach does not require any form of user
interaction, and can scale to very large images. To illustrate the usefulness, efficiency and scalability of our
method, we test it on the task of segmenting anatomical structures present in cardiac CT and brain MRI
images.
The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time.
The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask.
This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.
Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject
to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by
finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated
structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be
subject to flooding whereas region-based level set methods may not be selective enough. The main contribution
of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached
MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method.
In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points
at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that
prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are
identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while
being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the
MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of
the aorta and MAPCAs can be obtained with reasonable amount of effort.
3D roadmap provided by pre-operative volumetric data that is aligned with fluoroscopy helps visualization and
navigation in Interventional Cardiology (IC), especially when contrast agent-injection used to highlight coronary vessels
cannot be systematically used during the whole procedure, or when there is low visibility in fluoroscopy for partially or
totally occluded vessels. The main contribution of this work is to register pre-operative volumetric data with intraoperative
fluoroscopy for specific vessel(s) occurring during the procedure, even without contrast agent injection, to
provide a useful 3D roadmap. In addition, this study incorporates automatic ECG gating for cardiac motion. Respiratory
motion is identified by rigid body registration of the vessels. The coronary vessels are first segmented from a multislice
computed tomography (MSCT) volume and correspondent vessel segments are identified on a single gated 2D
fluoroscopic frame. Registration can be explicitly constrained using one or multiple branches of a contrast-enhanced
vessel tree or the outline of guide wire used to navigate during the procedure. Finally, the alignment problem is solved
by Iterative Closest Point (ICP) algorithm. To be computationally efficient, a distance transform is computed from the
2D identification of each vessel such that distance is zero on the centerline of the vessel and increases away from the
centerline. Quantitative results were obtained by comparing the registration of random poses and a ground truth
alignment for 5 datasets. We conclude that the proposed method is promising for accurate 2D-3D registration, even for
difficult cases of occluded vessel without injection of contrast agent.
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