In diagnostics and therapy control of cardiovascular diseases, detailed knowledge about the patient-specific behavior of blood flow and pressure can be essential. The only method capable of measuring complete time-resolved three-dimensional vector fields of the blood flow velocities is velocity-encoded magnetic resonance imaging (MRI), often denoted as 4D flow MRI. Furthermore, relative pressure maps can be computed from this data source, as presented by different groups in recent years. Hence, analysis of blood flow and pressure using 4D flow MRI can be a valuable technique in management of cardiovascular diseases. In order to perform these tasks, all necessary steps in the corresponding process chain can be carried out in our in-house developed software framework MEDIFRAME. In this article, we apply MEDIFRAME for a study of hemodynamics in the pulmonary arteries of five healthy volunteers. The study included measuring vector fields of blood flow velocities by phase-contrast MRI and subsequently computing relative blood pressure maps. We visualized blood flow by streamline depictions and computed characteristic values for the left and the right pulmonary artery (LPA and RPA). In all volunteers, we observed a lower amount of blood flow in the LPA compared to the RPA. Furthermore, we visualized blood pressure maps using volume rendering and generated graphs of pressure differences between the LPA, the RPA and the main pulmonary artery. In most volunteers, blood pressure was increased near to the bifurcation and in the proximal LPA, leading to higher average pressure values in the LPA compared to the RPA.
The velocity-encoded magnetic resonance imaging (PC-MRI) is a valuable technique to measure the blood flow velocity in terms of time-resolved 3D vector fields. For diagnosis, presurgical planning and therapy control monitoring the patient’s hemodynamic situation is crucial. Hence, an accurate and robust segmentation of the diseased vessel is the basis for further methods like the computation of the blood pressure. In the literature, there exist some approaches to transfer the methods of processing DT-MR images to PC-MR data, but the potential of this approach is not fully exploited yet. In this paper, we present a method to extract the centerline of the aorta in PC-MR images by applying methods from the DT-MRI. On account of this, in the first step the velocity vector fields are converted into tensor fields. In the next step tensor-based features are derived and by applying a modified tensorline algorithm the tracking of the vessel course is accomplished. The method only uses features derived from the tensor imaging without the use of additional morphology information. For evaluation purposes we applied our method to 4 volunteer as well as 26 clinical patient datasets with good results. In 29 of 30 cases our algorithm accomplished to extract the vessel centerline.
Michael Delles, Sebastian Schalck, Yves Chassein, Tobias Müller, Fabian Rengier, Stefanie Speidel, Hendrik von Tengg-Kobligk, Hans-Ulrich Kauczor, Rüdiger Dillmann, Roland Unterhinninghofen
Patient-specific blood pressure values in the human aorta are an important parameter in the management of cardiovascular diseases. A direct measurement of these values is only possible by invasive catheterization at a limited number of measurement sites. To overcome these drawbacks, two non-invasive approaches of computing patient-specific relative aortic blood pressure maps throughout the entire aortic vessel volume are investigated by our group. The first approach uses computations from complete time-resolved, three-dimensional flow velocity fields acquired by phasecontrast magnetic resonance imaging (PC-MRI), whereas the second approach relies on computational fluid dynamics (CFD) simulations with ultrasound-based boundary conditions. A detailed evaluation of these computational methods under realistic conditions is necessary in order to investigate their overall robustness and accuracy as well as their sensitivity to certain algorithmic parameters. We present a comparative study of the two blood pressure computation methods in an experimental phantom setup, which mimics a simplified thoracic aorta. The comparative analysis includes the investigation of the impact of algorithmic parameters on the MRI-based blood pressure computation and the impact of extracting pressure maps in a voxel grid from the CFD simulations. Overall, a very good agreement between the results of the two computational approaches can be observed despite the fact that both methods used completely separate measurements as input data. Therefore, the comparative study of the presented work indicates that both non-invasive pressure computation methods show an excellent robustness and accuracy and can therefore be used for research purposes in the management of cardiovascular diseases.
In management of cardiovascular diseases, information about the patient-specific behavior of blood flow and pressure
can be essential. In the human aorta, velocity-encoded magnetic resonance imaging (MRI) is the only method capable of measuring complete time-resolved three-dimensional vector fields of the blood flow velocities. Additionally,
computations of relative blood pressure from this data source have been presented in recent years. Thus, velocityencoded MRI can be a valuable measurement technique for both blood flow and blood pressure values in diagnostics and therapy control of aortic diseases. In the last years, we have developed the software framework MEDIFRAME for cardiovascular diagnostics based on MRI acquisitions. In this article, we apply our in-house developed software framework for a MRI-based hemodynamic analysis in five patients with surgically treated aortic coarctations. We compared our results to a control group of five healthy volunteers. The study included the measurement of blood flow velocities by phase-contrast MRI and the subsequent computation of relative blood pressure values. We generated a set of suitable visualizations for flow and pressure and created centerline diagrams of the cross-sectional area, flow and mean relative blood pressure. Additionally, characteristic values were computed from the centerline diagrams for every subject. In the vast majority of the visualization and quantification techniques of our software framework, we observed significant effects of the treated aortic coarctations. Therefore, we draw the conclusion that this kind of MRI-based hemodynamic analysis can be a valuable tool for diagnostics and therapy control of aortic coarctations.
Dealing with cardiovascular diseases the velocity-encoded magnetic resonance imaging (PC-MRI) is a well-known
technique to acquire non-invasive measurements of the blood flow. However, the application of conventional vessel
segmentation methods in PC-MR images often leads to problems due to the reduced quality of the morphology image.
We proposed a robust centerline extraction method in PC-MR images to overcome those problems. The method yielded
satisfying results for the centerline extraction of large vessels but did not consider vessel branches. Therefore, in this
paper we present an approach for the detection of bifurcations in PC-MR images. The developed algorithm requires two
inputs: the previously computed centerline points of the main vessel and a minimal user input. For each point on the
centerline it determines, if there exists a bifurcation in the cross-sectional plane at that position. This is accomplished by
an a* path finding algorithm, which computes the path costs for a potential bifurcation point to its corresponding center
point. The path costs are determined by the combination of different features derived from the morphology and flow
information. By comparison of all cost sums, bifurcations can be detected due to their low amount/value. The algorithm,
evaluated on 7 volunteer and 12 patient PC-MRI datasets, yielded satisfying results.
To obtain hemodynamic-relevant parameters in case of cardiovascular diseases the velocity-encoded magnetic resonance
imaging (PC-MRI) is used for the non-invasive measurement of the blood flow in terms of 3D velocity fields. During the
segmentation of the vessel lumen in those datasets conventional segmentation methods often fail due to reduced image
quality. In this paper we present a method for the centerline extraction of great vessels in PC-MR images using
additional features extracted from vector flow information. The proposed algorithm can be divided in the following
steps: the propagation along the vessel course by using streamlines and the largest eigenvector, the radial search for the
vessel boundary, the determination of the center position in the cross-sectional plane of the vessel and the adjustment of
the propagation step size subject to the vessel curvature. This is done by using a combination of morphology and flow
information: the Sobel filtered and the threshold filtered image as morphologic features as well as the coherence values
of the flow vectors and the behaviour of the blood flow streamlines within the vessel and around the borders as flow
features. The developed algorithm was evaluated on clinical PC-MRI datasets with encouraging results. The centerline
points of the entire aorta as well as corresponding border points were successfully extracted for 16 out of 17 examined
datasets. For the detection of the vessel boundary the features extracted from flow information showed to yield more
reliable results than morphology features.
In cardiovascular diagnostics, the knowledge of blood pressure is essential for the physician. Nowadays, blood pressures
are usually obtained by catheter measurements or sphygmomanometric methods. These techniques suffer from different
drawbacks in terms of invasiveness, observable vessels and the resolution of the pressure values, respectively. Magnetic
resonance imaging (MRI) offers a promising approach to establish a method for blood pressure measurements that is
able to overcome these difficulties. Phase-contrast MRI is used to acquire velocity-encoded data. Fluid pressure
gradients can be derived from the measured velocities using the Navier-Stokes equations. Unfortunately, this technique
is known to suffer from a strong sensitivity to imaging quality. Especially the low signal-to-noise ratios (SNR) of phase
contrast MRI data combined with the limited spatial and temporal resolution could severely reduce the reliability of
computations. In this paper, we analyze computations of blood pressure gradients based on phase contrast MRI
measurements of steady and pulsatile flow in a phantom. The influence of image quality of the velocity-encoded data as
well as of different segmentation techniques is evaluated. In case of steady flow, the pressure gradient values computed
via Navier-Stokes equations show good agreement with theoretical values if physical a-priori knowledge is incorporated.
If a pulsatile aortic flow profile is applied, the computed pressure gradients generally match catheter measurements well.
Nevertheless, an underestimation of pressure gradient peaks is observed. Different segmentation techniques influence the
size of root mean squared errors between computation and measurement as well as their reduction by the use of higher
SNRs.
KEYWORDS: Image segmentation, Arteries, 3D modeling, Detection and tracking algorithms, Magnetic resonance imaging, Natural surfaces, Blood circulation, Image quality, 3D acquisition, Image processing algorithms and systems
Tridirectional Phase-Contrast (PC)-MRI sequences provide spatially and temporally resolved measurements of
blood flow velocity vectors in the human body. Analyzing flow conditions based on these datasets requires
prior segmentation of the vessels of interest. In view of decreased quality of morphology images in PC-MRI
sequences, the flow data provides valuable information to support reliable segmentation. This work presents a
semi-automatic approach for segmenting the large arteries utilizing both morphology and flow information. It
consists of two parts, the extraction of a simplified vessel model based on vessel centerlines and diameters, and a
following refinement of the resulting surface for each time frame. Vessel centerlines and diameters are extracted
using an offset adaptive medialness function that estimates a voxel's likelihood of belonging to a vessel centerline.
The resulting centerline model is manually post-processed to select the appropriate centerlines and link possible
gaps. The surface described by the final centerline model is used to initialize a 3D level set segmentation of each
time frame. Deformation velocities that depend on both morphology and flow information are proposed and a
new approach to account for the curved shape of vessels is introduced. The described segmentation system has
been successfully applied on a total of 22 datasets of the thoracic aorta and the pulmonary arteries. Resulting
segmentations have been assessed by an expert radiologist and were considered to be very satisfactory.
KEYWORDS: Magnetic resonance imaging, Blood circulation, Doppler effect, Data acquisition, Heart, Ultrasonography, Computer programming, Scanners, 3D metrology, Medical imaging
Tridirectionally encoded phase-contrast MRI is a technique to
non-invasively acquire time-resolved velocity vector
fields of blood flow. These may not only be used to analyze pathological flow patterns, but also to quantify flow at
arbitrary positions within the acquired volume. In this paper we examine the validity of this approach by analyzing the
consistency of related quantifications instead of comparing it with an external reference measurement. Datasets of the thoracic aorta were acquired from 6 pigs, 1 healthy volunteer and 3 patients with artificial aortic valves. Using in-house software an elliptical flow quantification plane was placed manually at 6 positions along the descending
aorta where it was rotated to 5 different angles. For each configuration flow was computed based on the original data and
data that had been corrected for phase offsets. Results reveal that quantifications are more dependent on changes in
position than on changes in angle. Phase offset correction considerably reduces this dependency. Overall consistency is
good with a maximum variation coefficient of 9.9% and a mean variation coefficient of 7.2%.
Image-based modeling of cardiovascular biomechanics may be very helpful for patients with aortic aneurysms to predict
the risk of rupture and evaluate the necessity of a surgical intervention. In order to generate a reliable support it is
necessary to develop exact patient-specific models that simulate biomechanical parameters and provide individual
structural analysis of the state of fatigue and characterize this to the potential of rupture of the aortic wall.
The patient-specific geometry used here originates from a CT scan of an Abdominal Aortic Aneurysm (AAA). The
computations are based on the Finite Element Method (FEM) and simulate the wall stress distribution and the vessel
deformation. The wall transient boundary conditions are based on real time-dependent pressure simulations obtained
from a previous computational fluid dynamics study. The physiological wall material properties consider a nonlinear
hyperelastic constitutive model, based on realistic ex-vivo analysis of the aneurismal arterial tissue.
The results showed complex deformation and stress distribution on the AAA wall. The maximum stresses occurred at
the systole and are found around the aneurismal bulge in regions close to inflection points.
Biomechanical modeling based on medical images and coupled with patient-specific hemodynamics allows analysing
and quantifying the effects of dilatation of the arterial wall due to the pulsatile aortic pressure. It provides a physical and
realistic insight into the wall mechanics and enables predictive simulations of AAA growth and assessment of rupture.
Further development integrating endovascular models would help evaluating non-invasively individual treatment
strategies for optimal placement and improved device design.
Blood flow properties in the heart can be examined non invasively by means of Phase Contrast MRI (PC MRI), an imaging technique that provides not only morphology images but also velocity information. We present a novel feature combination for level set segmentation of the heart's cavities in multidirectional 4D PC MRI data. The challenge in performing the segmentation task successfully in this context is first of all the bad image quality, as compared to classical MRI. As generally in heart segmentation, the intra and inter subject variability of the heart has to be coped with as well. The central idea of our approach is to integrate a set of essentially differing sources of information into the segmentation process to make it capable of handling qualitatively bad and highly varying data. To the best of our knowledge our system is the first to concurrently incorporate a flow measure as well as a priori shape knowledge into a level set framework in addition to the commonly used edge and curvature information. The flow measure is derived from PC MRI velocity data. As shape knowledge we use a 3D shape of the respective cavity. We validated our system design by a series of qualitative performance tests. The combined use of shape knowledge and a flow measure increases segmentation quality compared to results obtained by using only one of those features. A first clinical study was performed on two 4D datasets, from which we segmented the left ventricle and atrium. The segmentation results were examined by an expert and judged suitable for use in clinical practice.
KEYWORDS: Image segmentation, Statistical modeling, Data modeling, Diagnostics, Visualization, Heart, Principal component analysis, Visual process modeling, 3D modeling, Mahalanobis distance
The manual segmentation and analysis of high-resolution multi-slice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium and compute the relevant diagnostic parameters. In this work we present a semi-automatic cardiac segmentation approach with minimal user interaction. It is based on a combination of an adaptive slice-based regiongrowing and a modified Active Shape Model (ASM). Starting with a single manual click point in the ascending aorta, the aorta, the left atrium and the left ventricle get segmented with the slice-based adaptive regiongrowing. The approximate position of the aortic and mitral valve as well as the principal axes of the left ventricle (LV) are determined. To prevent the regiongrowing from draining into neighboring anatomical structures via CT artifacts, we implemented a draining control by examining a cubic region around the currently processed voxel. Additionally, we use moment-based parameters to integrate simple anatomical knowledge into the regiongrowing process.
Using the results of the preceding regiongrowing process, a ventricle-centric and normalized coordinate system is established
which is used to adapt a previously trained ASM to the image, using an iterative multi-resolution approach. After fitting the ASM to the image, we can use the generated model-points to create an exact surface model of the left ventricular myocardium for visualization and for computing the diagnostically relevant parameters, like the ventricular blood volume and the myocardial wall thickness.
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