2,631 research outputs found
3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography
Three-dimensional (3D) information on blood
flow and vessel morphology is important when
assessing cerebrovascular disease and when monitoring interventions. Rotational angiography
is nowadays routinely used to determine the geometry of the cerebral vasculature. To this end,
contrast agent is injected into one of the supplying arteries and the x-ray system rotates around
the head of the patient while it acquires a sequence of x-ray images. Besides information on the
3D geometry, this sequence also contains information on blood flow, as it is possible to observe
how the contrast agent is transported by the blood. The main goal of this thesis is to exploit
this information for the quantitative analysis of blood flow.
I propose a model-based method, called
flow map fitting, which determines the blood flow
waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a
model of contrast agent transport to determine the
flow parameters from the spatio-temporal
progression of the contrast agent concentration, represented by a flow map. Furthermore, it
overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some
projection angles) of the c-arm using a reliability map.
For the flow quantification, small changes to the clinical protocol of rotational angiography
are desirable. These, however, hamper the standard 3D reconstruction. Therefore, a new method
for the 3D reconstruction of the vessel morphology which is tailored to this application is also presented.
To the best of my knowledge, I have presented the first quantitative results for blood flow
quantification from rotational angiography. Additionally, the model-based approach overcomes
several problems which are known from flow quantification methods for planar angiography.
The method was mainly validated on images from different phantom experiments. In most
cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between
10% and 15% for the blood flow waveform. Additionally, the applicability of the flow model was shown on clinical images from planar angiographic acquisitions. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during
cerebrovascular interventions
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences
Dynamic reconstructions (3D+T) of coronary arteries could give important
perfusion details to clinicians. Temporal matching of the different views,
which may not be acquired simultaneously, is a prerequisite for an accurate
stereo-matching of the coronary segments. In this paper, we show how a neural
network can be trained from angiographic sequences to synchronize different
views during the cardiac cycle using raw x-ray angiography videos exclusively.
First, we train a neural network model with angiographic sequences to extract
features describing the progression of the cardiac cycle. Then, we compute the
distance between the feature vectors of every frame from the first view with
those from the second view to generate distance maps that display stripe
patterns. Using pathfinding, we extract the best temporally coherent
associations between each frame of both videos. Finally, we compare the
synchronized frames of an evaluation set with the ECG signals to show an
alignment with 96.04% accuracy
Automatic Estimation of Coronary Blood Flow Velocity Step 1 for Developing a Tool to Diagnose Patients With Micro-Vascular Angina Pectoris
Aim: Our aim was to automatically estimate the blood velocity in coronary arteries using cine X-ray angiographic sequence. Estimating the coronary blood velocity is a key approach in investigating patients with angina pectoris and no significant coronary artery disease. Blood velocity estimation is central in assessing coronary flow reserve.
Methods and Results: A multi-step automatic method for blood flow velocity estimation based on the information extracted solely from the cine X-ray coronary angiography sequence obtained by invasive selective coronary catheterization was developed. The method includes (1) an iterative process of segmenting coronary arteries modeling and removing the heart motion using a non-rigid registration, (2) measuring the area of the segmented arteries in each frame, (3) fitting the measured sequence of areas with a 7◦ polynomial to find start and stop time of dye propagation, and (4) estimating the blood flow velocity based on the time of the dye propagation and the length of the artery-tree. To evaluate the method, coronary angiography recordings from 21 patients with no obstructive coronary artery disease were used. In addition, coronary flow velocity was measured in the same patients using a modified transthoracic Doppler assessment of the left anterior descending artery. We found a moderate but statistically significant correlation between flow velocity assessed by trans thoracic Doppler and the proposed method applying both Spearman and Pearson tests.
Conclusion: Measures of coronary flow velocity using a novel fully automatic method that utilizes the information from the X-ray coronary angiographic sequence were statistically significantly correlated to measurements obtained with transthoracic Doppler recordings.publishedVersio
Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions
Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter
The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review
Understanding the mechanisms that regulate atherosclerotic plaque formation and
evolution is a crucial step for developing treatment strategies that will prevent plaque
progression and reduce cardiovascular events. Advances in signal processing and the
miniaturization of medical devices have enabled the design of multimodality intravascular
imaging catheters that allow complete and detailed assessment of plaque morphology
and biology. However, a significant limitation of these novel imaging catheters is that they
provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they
cannot portray vessel geometry and 3D lesion architecture. To address this limitation
computer-based methodologies and user-friendly software have been developed. These
are able to off-line process and fuse intravascular imaging data with X-ray or computed
tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The
aim of this review article is to summarize the evolution in the field of coronary artery
modeling; we thus present the first methodologies that were developed to model vessel
geometry, highlight the modifications introduced in revised methods to overcome the
limitations of the first approaches and discuss the challenges that need to be addressed,
so these techniques can have broad application in clinical practice and research
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Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images
We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes
Deep motion tracking from multiview angiographic image sequences for synchronization of cardiac phases
In the diagnosis and interventional treatment of coronary artery disease, the 3D+time reconstruction of the coronary artery on the basis of x-ray angiographic image sequences can provide dynamic structural information. The synchronization of cardiac phases in the sequences is
essential for minimizing the influence of cardiorespiratory motion and realizing precise 3D+time reconstruction. Key points are initially extracted from the first image of a sequence. Matching grid points between consecutive images in the sequence are extracted by a multi-layer matching strategy. Then deep motion tracking (DMT) of key points is achieved by local deformation based on the neighboring grid points of key points. The local deformation is optimized by the Random sample consensus (RANSAC) algorithm. Then, a simple harmonic motion (SHM) model is utilized to distinguish cardiac motion from other motion sources (e.g. respiratory, patient movement, etc). Next, the signal which is composed of cardiac motions is filtered by a band-pass filter to reconstruct the cardiac phases. Finally, the synchronization of cardiac phases from different imaging angles is realized by a piece-wise linear transformation. The proposed method was evaluated using clinical x-ray angiographic image sequences from 13 patients. 85% matching points can be accurately computed by the DMT method. The mean peak temporal distance (MPTD) between the reconstructed cardiac phases and the electrocardiograph signal is 0.027 s. The correlation between the cardiac phases of the same patient is over 89%. Compared with three other state-of-the-art methods, the proposed method accurately reconstructs and synchronizes the cardiac phases from different sequences of the same patient. The proposed DMT method is robust and highly effective in synchronizing cardiac phases of angiographic image sequences captured from different imaging angles
3D reconstruction of coronary arteries from angiographic sequences for interventional assistance
Introduction -- Review of literature -- Research hypothesis and objectives -- Methodology -- Results and discussion -- Conclusion and future perspectives
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