Conference PaperPDF Available

Guided analysis of cardiac 4D PC-MRI blood flow data

  • University of Leipzig -Heart Center

Abstract and Figures

a) Graph cut-assisted vessel segmentation. (b) Semi-automatic vortex extraction. (c) Cardiac function assessment. Figure 1: Screenshots of the presented software Bloodline for cardiac 4D PC-MRI data evaluation. Abstract Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows the non-invasive acquisition of temporally resolved, three-dimensional blood flow information. Quantitative and qualitative data analysis help to assess the cardiac function, severity of diseases and find indications of different cardiovascular pathologies. However, various steps are necessary to achieve expressive visualizations and reliable results. This comprises the correction of special MR-related artifacts, the segmentation of vessels, flow integration with feature extraction and the robust quantification of clinically important measures. A fast and easy-to-use processing pipeline is essential since the target user group are physicians. We present a system that offers such a guided workflow for cardiac 4D PC-MRI data. The aorta and pulmonary artery can be analyzed within ten minutes including vortex extraction and robust determination of the stroke volume as well as the percentaged backflow. 64 datasets of healthy volunteers and of patients with variable diseases such as aneurysms, coarctations and insufficiencies were processed so far.
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EUROGRAPHICS 2015/ H.-C. Hege and T. Ropinski Dirk Bartz Prize
Guided Analysis of Cardiac 4D PC-MRI Blood Flow Data
Benjamin Köhler1, Uta Preim2, Matthias Grothoff3, Matthias Gutberlet3, Katharina Fischbach4, Bernhard Preim1
1Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany
2Department of Diagnostic Radiology, Hospital Olvenstedt, Magdeburg, Germany
3Department of Diagnostics and Interventional Radiology, Heart Center, Leipzig, Germany
4Department of Radiology and Nuclear Medicine, University Hospital, Magdeburg, Germany
(a) Graph cut-assisted vessel segmentation. (b) Semi-automatic vortex extraction. (c) Cardiac function assessment.
Figure 1: Screenshots of the presented software Bloodline for cardiac 4D PC-MRI data evaluation.
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows the non-invasive acquisition
of temporally resolved, three-dimensional blood flow information. Quantitative and qualitative data analysis help
to assess the cardiac function, severity of diseases and find indications of different cardiovascular pathologies.
However, various steps are necessary to achieve expressive visualizations and reliable results. This comprises the
correction of special MR-related artifacts, the segmentation of vessels, flow integration with feature extraction and
the robust quantification of clinically important measures. A fast and easy-to-use processing pipeline is essential
since the target user group are physicians. We present a system that offers such a guided workflow for cardiac 4D
PC-MRI data. The aorta and pulmonary artery can be analyzed within ten minutes including vortex extraction and
robust determination of the stroke volume as well as the percentaged backflow. 64 datasets of healthy volunteers
and of patients with variable diseases such as aneurysms, coarctations and insufficiencies were processed so far.
Categories and Subject Descriptors (according to ACM CCS): I.4.9 [Computing Methodologies]: Image Processing
and Computer Vision—Applications
1. Introduction
Cardiovascular diseases (CVDs) are the most frequent cause
of death in the world. Understanding their origin and evo-
lution may improve diagnosis and the choice of appropriate
treatments. Vortex flow has been determined as an atypical
flow pattern that is caused by, e.g., heart valve defects or
an altered vessel morphology. Quantitative measures such as
stroke volumes facilitate the assessment of the present car-
diac function and the tracking of disease progression by eval-
uating follow-up examinations.
Four-dimensional phase-contrast magnetic resonance
imaging (4D PC-MRI) gained increasing importance in the
last decade. Its 2D equivalent measures the flow in only one
preangulated slice. Unsatisfactory results make a new acqui-
sition necessary, which is stressful for the patient. In con-
trast, 4D PC-MRI datasets contain the full spatio-temporal
blood flow information and thus allow a more flexible anal-
ysis. Recent advances greatly reduced acquisition times to
levels that are feasible for the clinical routine. Although
it has the potential to replace 2D PC-MRI, 4D flow scans
are mainly performed for research purposes at the moment.
This points out the need for standardized and guided tech-
niques to analyze these highly complex data. Software solu-
The Eurographics Association 2015.
Köhler et al. / Cardiac 4D PC-MRI Data Analysis
tions that integrate such methods into easy-to-use workflows
are of equal importance. We present a tool that facilitates
data analysis within ten minutes. This includes an automated
data preprocessing, a graph cut-assisted vessel segmentation,
semi-automatic vortex flow extraction and analysis of the
stroke volume as well as percentaged backflow. Resulting
visualizations can easily be saved and shared using the pro-
vided one-click solutions for videos of the animated flow and
screenshots of the 3D view or GUI.
2. Previous and Related Work
Markl et al. [MFK12] provide an overview about 4D PC-
MRI acquisitions, Calkoen et al. [CRvdG14] document its
high flexibility by describing recent applications. Line predi-
cates were used by Born et al. [BPM13] to extract flow fea-
tures and employed in a previous work to extract vortex flow
[KGP13]. Relevant quantitative measures such as stroke
volumes are described by Hope et al. [HSD13]. We devel-
oped a method to robustly determine stroke volumes and
percentaged backflow (regurgitation fractions) [KPG14].
MeVisFlow by Hennemuth et al. [HFS11] and FourFlow
by Heiberg et al. [HSU10] are softwares that provide a
pipeline including preprocessing, segmentation and interac-
tive data exploration. The Siemens Flow Demonstrator is
a similar prototype [SCG14]. The Quantitative Flow Ex-
plorer by van Pelt et al. [vPBB10] encompasses interac-
tive, illustrative visualizations for data exploration. Ensight
and GyroTools GTFlow are commercial tools that, however,
do not focus on cardiac blood flow.
3. Medical Background
A goal of current medical research papers is to correlate car-
diovascular diseases with specific flow behaviors, i.e. vor-
tex flow patterns. For instance, Hope et al. [HHM10] found
systolic vortex flow in 75 % of their patients with bicuspid
aortic valves – a defect where the aortic valve consists of
only two instead of three leaflets. Altered vessel morphology
can be another important factor that promotes the formation
of vortices. Slight dilations are called ectasia, severe dila-
tions are referred to as aneurysm. Pathological narrowings
are called stenosis or, in case of the aortic arch, coarctation.
The stroke volume is the amount of pumped blood per
heart beat in ml and can be determined as flow that passes
a plane, usually located above the valve, orthogonally. It is
calculated as integral of the time-dependent flow rate which
is given in ml/s throughout the cardiac cycle. The cardiac
output is the stroke volume multiplied with the heart rate and
thus describes the heart’s pumping capacity in l/min. These
measures help to assess the cardiac function. Regurgitation
fraction denotes the percentaged amount of blood that flows
back into the ventricle during systole due to improperly clos-
ing valves. It is below 5% in a healthy person. High values
of 20% and more can indicate a valve replacement surgery
if the patient shows severe symptoms.
3.1. Data Acquisition
Most of our datasets were acquired with a 3 T Magnetom
Verio (Siemens Healthcare, Erlangen, Germany). A 4D PC-
MRI dataset consists of each three (x-, y-, z-dimension)
magnitude and flow images that represent the flow strength
and direction, respectively, per voxel. The grid size is 132×
192 in the plane with 15 to 23 and between 14 and 21 tempo-
ral positions. The spatio-temporal resolution is 1.77 mm ×
1.77 mm ×3.5 mm ×50 ms. The velocity encoding (VENC )
– an a-priori MR parameter that describes the maximum ex-
pected velocity – was set to 1.5 m/s, which is a common
choice for aortic blood flow [MFK12].
4. Requirement Analysis
The use of (semi-)automatic methods is essential to estab-
lish a fluent workflow. Exploitation of the GPU’s computa-
tional power is desirable to speed up the data processing.
Required input should fit into the mental model of our tar-
get user group with a medical background. Thus, employed
algorithms should allow to make use of physicians’ expert
knowledge. Reasonable default parameters have to be pro-
vided for everything that is unintuitive from their perspec-
tive. It is necessary that results of evaluated datasets can eas-
ily be shared via screenshots or videos.
5. Bloodline
In this section, we describe our developed software named
Bloodline, shown in Figure 1. It is written in C++ and uses
OpenGL for rendering, embedded in a Qt/QML-based GUI.
5.1. Data Import
The raw data – one file per slice per temporal position – are
converted to 4D images using information from the DICOM
headers. An eddy current correction is then applied to the
flow image using the method by Walker et al. [WCS93]
with their suggested default parameters.
5.2. Vessel Segmentation
A temporal maximum intensity projection (tMIP) of the
magnitude images is performed, which yields a high-
contrast 3D image. Graph cuts require the specification of
regions in- and outside the target structure as input. The user
provides these information by drawing on the tMIP slices
(see Figure 1a). The better the image quality, the less input
is necessary to achieve satisfactory results. Though, detail
corrections can be performed if the segmentation includes
unwanted or excludes wanted parts. The employed 3D graph
cut with a 26-neighbourhood per voxel allows that the user
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Köhler et al. / Cardiac 4D PC-MRI Data Analysis
does not have to provide input in every slice. Edge weights
in the graph are set to exp(α· ||∇I||), where Iare the
[0,1]-scaled intensities from the tMIP and αis a tolerance
parameter with 1000 as experimentally determined default
value. Noise in the resulting segmentation is reduced with a
3×3×3 morphological opening and closing.
Phase wraps occur when the measured velocity exceeds
the VENC. In this case, values flip to the other end of the do-
main. We correct phase wraps within the obtained segmen-
tations according to Dìaz et al. [DR04]. The rest of the flow
image is not processed to save time.
5.3. Surface Mesh and Centerline Extraction
Marching cubes is employed to automatically extract the
vessel surface from the segmentation. We apply a low-pass
filter [TZG96] and reduce the mesh via quadric decimation
[Hop99]. The user marks a start and end point on the vessel
surface for the subsequent centerline extraction [AEIR03].
Multiple end points are allowed to create centerlines in
branching vessels such as the pulmonary artery. An aorta is,
on average, represented by 2500-3000 triangles with a mean
edge length of 4.9 mm. For comparison, a voxel diagonal is
4.3 mm long. This mesh resolution is sufficient because of
the non-complex shape of the aorta and pulmonary artery.
An adapted graph cut enables the semi-automatic 4D
vessel segmentation from time-resolved anatomical images,
e.g., a SSFP cine sequence. An explicit moving surface is au-
tomatically extracted for visualization purposes and optional
quantification with increased accuracy [KPG15a].
5.4. Qualitative Analysis
Flow Integration: Runge-Kutta-4 with adaptive step size is
implemented on the GPU to integrate the full set of path-
lines. Velocity vectors ~uR3in the 4D flow field Vat
the spatio-temporal position ~x= (x,y,z,t)Tare obtained via
quadrilinear interpolation. The temporally adjacent vectors
~ubtc=V(x,y,z,btc)and ~udte=V(x,y,z,dte), both obtained
via hardware-accelerated trilinear interpolation, are used to
perform a last linear interpolation manually. We ensure that
each voxel of the segmentation is visited at least once in ev-
ery temporal positions. For this purpose, we seed one path-
line at a random position inside each segmentation voxel at
the first temporal position. For each remaining time step, in
succession, we determine the voxels that were not visited,
create new seeds and integrate the pathlines.
Vortex Extraction: During the full flow integration, we
also calculate the λ2vortex criterion for each pathline point.
To alleviate the impact of the low data resolution and noise,
we smooth the values along each pathline using a 1D bi-
nomial filter with kernel size 3. Contrary to our previous
work [KGP13], we do not crop away parts of the pathlines.
Instead, we provide the option to flexibly hide all non-vortex
parts using a slider that adjusts the λ2threshold (see Figure
1b). Filtering flow velocities is possible in the same way. For
the aorta, a circular 2D plot can be generated as overview of
present vortices [KMP15].
Visualization: The vessel front is culled and only hinted
at with a ghosted viewing [GNKP10]. The back faces are
rendered with Phong illumination. Pathlines with halos are
created in the geometry shader as view-aligned quads. Il-
luminated streamlines are implemented in the subsequent
fragment shader. In the animation mode, cone-shaped par-
ticles with trails are drawn on every position where the cur-
rent animation time matches a pathline’s temporal compo-
nent. Order-independent transparency ensures correct alpha
blending. The default line width, particle width and particle
length are set according to the dataset’s voxel diagonal. The
standard trail length depends on the temporal resolution and
number of time steps. Real-time adjustment of all visualiza-
tion parameters is possible via sliders.
Media: Results can easily be shared by taking a high-
resolution screenshot of the GUI or render window. The an-
imated flow can be exported to a 1080p video with a sin-
gle click. Patient and dataset information are automatically
added to the top left corner.
5.5. Quantitative Analysis
Measuring planes are automatically oriented orthogonal to
the centerline and their size is automatically determined so
that they fit the vessel (see Figure 1c). The user can drag a
plane along the centerline or adjust the angulation, i.e. ro-
tate it. A diagram shows the time-dependent flow rate deter-
mined for this plane configuration. Additionally, the stroke
volume, cardiac output, regurgitation fraction, mean as well
as peak velocity and the vessel diameter are provided. Un-
fortunately, the calculations are highly sensitive towards the
plane’s angulation. Therefore, a robust stroke volume and
regurgitation fraction analysis can be performed [KPG14].
This work received an honorable mention and was invited to
be submitted in an extended version to the Computer Graph-
ics Forum [KPG15b]. Another quantifiable measure on the
vessel surface is the vectorial wall shear stress.
6. Application
Bloodline is used by the Heart Center in Leipzig, Germany,
who also use a prototype by Siemens, and the university hos-
pital in Magdeburg, Germany. 64 datasets were evaluated for
research purposes so far in close collaboration with radiol-
ogists specialized on the cardiovascular system. Besides 36
healthy volunteers, the following pathologies were present:
1 aneurysm in the left subclavian artery, 3 aortic insufficien-
cies, 3 ectasias / aneurysms in the ascending aorta, 15 bi-
cuspid aortic valves, some of them with ectatic ascending
aortas, 1 tetralogy of Fallot with pulmonary insufficiency, 3
vascular prostheses and 2 coarctations.
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Köhler et al. / Cardiac 4D PC-MRI Data Analysis
After familiarization, physicians are able to perform a
standard evaluation, i.e. vortex flow extraction and stroke
volume as well as regurgitation fraction analysis, in less than
ten minutes, which was rated as feasible for the clinical rou-
tine. The robust quantification is most expensive and takes
about 30 s using an Intel i7-3930K and a GeForce GTX 680.
Other costly computations such as the full flow integration
including vortex extraction are each performed within 10 s.
The graph cut-assisted segmentation shows high accep-
tance due to the exploitation of the physicians’ anatomy
knowledge. The enabled hiding of vessels to reduce occlu-
sions was appreciated. The independence of specific MRI
scanners was emphasized positively. A suggestion was to let
the program perform pending automatic operations such as
pathline integrations for all new datasets at once. This way,
the concentrated waiting time could be used for other things.
7. Conclusion and Future Work
We presented the cardiac 4D PC-MRI data evaluation soft-
ware Bloodline that allows to process datasets within ten
minutes. It integrates a full preprocessing pipeline as well
as quantitative and qualitative data analysis. The use of
(semi-)automatic methods enables a fluent workflow. Rea-
sonable defaults strongly reduce the necessity to adjust pa-
rameters. State-of-the-art visualizations can easily be created
and saved in order to share results.
Special functionality for the ventricles shall be provided
in the future. Another goal is to automatically generate clin-
ical reports. Hence, larger studies can be evaluated better and
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The Eurographics Association 2015.
... Vessel segmentation, blood flow visualisation and preprocessing. All processing and measurement steps were carried out using the custom-made software tool Bloodline 21,22 (Department of Simulation and Graphics, University of Magdeburg, Germany). Anatomical 3D reconstruction of the aorta was derived from the 3D-T2w-SPACE-STIR sequence. ...
... We corrected for phase wraps, eddy currents and background noise as reported previously. Eddy current correction (ECC) was performed using a background subtraction technique 22,23 , in which an area of static tissue was semiautomatically defined, and the mean phase information of this area was subtracted from the ROI of the vessel. ...
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To compare two broadly used 4D-flow- with a 2D-flow-sequence in healthy volunteers, regarding absolute flow parameters, image quality (IQ), and eddy current correction (ECC). Forty volunteers (42 ± 11.8 years, 22 females) were examined with a 3T scanner. Thoracic aortic flow was assessed using a 3D-T2w-SPACE-STIR-sequence for morphology and two accelerated 4D-flow sequences for comparison, one with k-t undersampling and one with standard GRAPPA parallel-imaging. 2D-flow was used as reference standard. The custom-made software tool Bloodline enabled flow measurements for all analyses at the same location. Quantitative flow analyses were performed with and without ECC. One reader assessed pathline IQ (IQ-PATH) and occurrence of motion artefacts (IQ-ART) on a 3-point grading scale, the higher the better. k-t GRAPPA allowed a significant mean scan time reduction of 46% (17:56 ± 5:26 min vs. 10:40 ± 3:15 min) and provided significantly fewer motion artefacts than standard GRAPPA (IQ-ART 1.57 ± 0.55 vs. 0.84 ± 0.48; p < 0.001). Neither 4D-flow sequence significantly differed in flow volume nor peak velocity results with or without ECC. Nevertheless, the correlation between both 4D-flow sequences and 2D-flow was better with ECC; the k-t GRAPPA sequence performed best (R = 0.96 vs. 0.90). k-t GRAPPA 4D-flow was not inferior to a standard GRAPPA-sequence, showed fewer artefacts, comparable IQ and was almost two-fold faster.
... and Pre-processing. All processing, segmentation and measurement steps were carried out using Bloodline, a custom-made software tool for guided analysis of 4D flow MRI datasets, which has been introduced before [16][17][18] (Department of Simulation and Graphics, University of Magdeburg, Germany). A centerline was drawn through the whole ascending aorta semi-automatically, beginning at the level of the aortic root. ...
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4D flow MRI enables quantitative assessment of helical flow. Current methods are susceptible to noise. To evaluate helical flow patterns in healthy volunteers and patients with bicuspid aortic valves (BAV) at 1.5 T and 3 T using pressure-based helix-extraction in 4D flow MRI. Two intraindividual 4D flow MRI examinations were performed at 1.5 T and 3 T in ten healthy volunteers (5 females, 32 ± 3 years) and 2 patients with BAV using different acceleration techniques (kt-GRAPPA and centra). Several new quantitative parameters for the evaluation of volumes [ml], lengths [mm] as well as temporal parameters [ms] of helical flow were introduced and analyzed using the software tool Bloodline. We found good correlations between measurements in volunteers at 1.5 T and 3 T regarding helical flow volumes (R = 0.98) and temporal existence (R = 0.99) of helices in the ascending aorta. Furthermore, we found significantly larger (11.7 vs. 77.6 ml) and longer lasting (317 vs. 769 ms) helices in patients with BAV than in volunteers. The assessed parameters do not depend on the magnetic field strength used for the acquisition. The technique of pressure-based extraction of 4D flow MRI pattern is suitable for differentiation of normal and pathological flow.
... Circumferential velocities are the portion that lies in the vessel's cross-section. Moreover, we calculate the rotation direction (left / right) relative to the centerline tangent by projecting a flow vector as line segment into the vessel's cross-section and analyzing the angle between start and end point via atan2 [28] (see Fig. 13c). ...
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Aortic vortex flow is a strong indicator for various cardiovas-cular diseases. The correlation of pathologies like bicuspid aortic valves to the occurrence of such flow patterns at specific spatio-temporal posi-tions during the cardiac cycle is of great interest to medical researchers. Dataset analysis is performed manually with common flow visualization techniques such as particle animations. For larger patient studies this is time-consuming and quickly becomes tedious. In this paper, we present a two-dimensional plot visualization of the aorta that facilitates the as-sessment of occurring vortex behavior at one glance. For this purpose, we explain a mapping of the 4D flow data to circular 2D plots and describe the visualization of the employed λ2-vortex criterion. A grid view allows the simultaneous investigation and comparison of multiple datasets. Af-ter a short familiarization with the plots our collaborating cardiologists and radiologists were able distinguish between patient and healthy vol-unteer datasets with ease.
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Acquisition techniques related to 4-dimensional (4D) flow magnetic resonance imaging (MRI) improved rapidly over the last 3 decades. Most importantly, a major improvement was the acceleration of the acquisition, which resulted in a clinically feasible scan duration and led to more comprehensive use of 4D flow MRI in clinical research. This resulted in several new applications of 4D flow MRI for the evaluation of various physiological and pathologic cardiovascular flow patterns. Visualization tools aim at displaying the direction and magnitude of blood flow velocity from 4D flow data, by using for instance a vector glyph or streamline representation or by constructing pathlines from particle tracing. Such tools are applied to provide insight in the temporal distribution of the 3D flow velocity and enable the quantification of hemodynamic markers. These hemodynamic markers play an important role in the quantitation of abnormalities in cardiovascular blood flow patterns and the characterization of vascular and myocardial remodelling, which can possibly be used to predict pathology such as heart failure, aortic dissection, or aneurysm or thrombus formation. This review focuses on the clinical use of 4D flow MRI and presents an overview of new applications of visualization and quantification tools to describe physiological and pathologic cardiovascular blood flow.
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Cardiovascular diseases (CVD) are the leading cause of death worldwide. Their initiation and evolution depends strongly on the blood flow characteristics. In recent years, advances in 4D PC-MRI acquisition enable reliable and time-resolved 3D flow measuring, which allows a qualitative and quantitative analysis of the patient-specific hemodynamics. Currently, medical researchers investigate the relation between characteristic flow patterns like vortices and different pathologies. The manual extraction and evaluation is tedious and requires expert knowledge. Standardized, (semi-)automatic and reliable techniques are necessary to make the analysis of 4D PC-MRI applicable for the clinical routine. In this work, we present an approach for the extraction of vortex flow in the aorta and pulmonary artery incorporating line predicates. We provide an extensive comparison of existent vortex extraction methods to determine the most suitable vortex criterion for cardiac blood flow and apply our approach to ten datasets with different pathologies like coarctations, Tetralogy of Fallot and aneurysms. For two cases we provide a detailed discussion how our results are capable to complement existent diagnosis information. To ensure real-time feedback for the domain experts we implement our method completely on the GPU.
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows the non-invasive acquisition of time-resolved, 3D blood flow information. Stroke volumes (SVs) and regurgitation fractions (RFs) are two of the main measures to assess the cardiac function and severity of valvular pathologies. The flow rates in forward and backward direction through a plane above the aortic or pulmonary valve are required for their quantification. Unfortunately, the calculations are highly sensitive towards the plane's angulation since orthogonally passing flow is considered. This often leads to physiologically implausible results. In this work, a robust quantification method is introduced to overcome this problem. Collaborating radiologists and cardiologists were carefully observed while estimating SVs and RFs in various healthy volunteer and patient 4D PC-MRI data sets with conventional quantification methods, that is, using a single plane above the valve that is freely movable along the centerline. By default it is aligned perpendicular to the vessel's centerline, but free angulation (rotation) is possible. This facilitated the automation of their approach which, in turn, allows to derive statistical information about the plane angulation sensitivity. Moreover, the experts expect a continuous decrease of the blood flow volume along the vessel course. Conventional methods are often unable to produce this behaviour. Thus, we present a procedure to fit a monotonous function that ensures such physiologically plausible results. In addition, this technique was adapted for the usage in branching vessels such as the pulmonary artery. The performed informal evaluation shows the capability of our method to support diagnosis; a parameter evaluation confirms the robustness. Vortex flow was identified as one of the main causes for quantification uncertainties.
1- or 2-directional MRI blood flow mapping sequences are an integral part of standard MR protocols for diagnosis and therapy control in heart diseases. Recent progress in rapid MRI has made it possible to acquire volumetric, 3-directional cine images in reasonable scan time. In addition to flow and velocity measurements relative to arbitrarily oriented image planes, the analysis of 3-dimensional trajectories enables the visualization of flow patterns, local features of flow trajectories or possible paths into specific regions. The anatomical and functional information allows for advanced hemodynamic analysis in different application areas like stroke risk assessment, congenital and acquired heart disease, aneurysms or abdominal collaterals and cranial blood flow. The complexity of the 4D MRI flow datasets and the flow related image analysis tasks makes the development of fast comprehensive data exploration software for advanced flow analysis a challenging task. Most existing tools address only individual aspects of the analysis pipeline such as pre-processing, quantification or visualization, or are difficult to use for clinicians. The goal of the presented work is to provide a software solution that supports the whole image analysis pipeline and enables data exploration with fast intuitive interaction and visualization methods. The implemented methods facilitate the segmentation and inspection of different vascular systems. Arbitrary 2- or 3-dimensional regions for quantitative analysis and particle tracing can be defined interactively. Synchronized views of animated 3D path lines, 2D velocity or flow overlays and flow curves offer a detailed insight into local hemodynamics. The application of the analysis pipeline is shown for 6 cases from clinical practice, illustrating the usefulness for different clinical questions. Initial user tests show that the software is intuitive to learn and even inexperienced users achieve good results within reasonable processing times.
Multidimensional blood flow imaging with magnetic resonance has rapidly evolved over the last decade. The technique, often referred to as 4-dimensional (4D) flow, can now reliably image the heart and principal vessels of the chest in ≤15 minutes. In addition to dynamic 3D flow visualization, a range of unique quantitative hemodynamic markers can be calculated from 4D flow data. In this review article, we describe some of the more promising of these hemodynamic markers, including pulse wave velocity, pressure, turbulent kinetic energy, wall shear stress, and flow eccentricity. Evaluation of a range of cardiothoracic disorders has been explored with 4D flow, and many applications have been proposed. We also review the potential clinical applications of 4D flow in 4 broad contexts: the aorta, the pulmonary artery, acquired heart disease, and complex congenital heart disease. Promising preliminary results will be highlighted, including the use of abnormal systolic blood flow to risk-stratify patients for progressive valve-related aortic disease, turbulent kinetic energy to directly assess the hemodynamic impact of a stenotic lesion, and altered intracardiac flow to identify early heart failure. We discuss ongoing research efforts in the context of the larger clinical goals of 4D flow: the use of unique hemodynamic markers to (1) identify cardiovascular disease processes early in their course before clinical manifestation so that preemptive treatment can be undertaken; (2) refine the assessment of cardiovascular disease so as to better identify optimal medical or surgical therapies; and (3) enhance the evaluation and monitoring of the hemodynamic impact of different treatment options.
Four-dimensional MRI is an in vivo flow imaging modality that is expected to significantly enhance the understanding of cardiovascular diseases. Among other fields, 4D MRI provides valuable data for the research of cardiac blood flow and with that the development, diagnosis, and treatment of various cardiac pathologies. However, to gain insights from larger research studies or to apply 4D MRI in the clinical routine later on, analysis techniques become necessary that allow to robustly identify important flow characteristics without demanding too much time and expert knowledge. Heart muscle contractions and the particular complexity of the flow in the heart imply further challenges when analyzing cardiac blood flow. Working toward the goal of simplifying the analysis of 4D MRI heart data, we present a visual analysis method using line predicates. With line predicates precalculated integral lines are sorted into bundles with similar flow properties, such as velocity, vorticity, or flow paths. The user can combine the line predicates flexibly and by that carve out interesting flow features helping to gain overview. We applied our analysis technique to 4D MRI data of healthy and pathological hearts and present several flow aspects that could not be shown with current methods. Three 4D MRI experts gave feedback and confirmed the additional benefit of our method for their understanding of cardiac blood flow.
Background phase distortion and random noise can adversely affect the quality of magnetic resonance (MR) phase velocity measurements. A semiauto-mated method has been developed that substantially reduces both effects. To remove the background phase distortion, the following steps were taken: The time standard deviations of the phase velocity images over a cardiac cycle were calculated. Static regions were identified as those in which the standard deviation was low. A flat surface representing an approximation to the background distortion was fitted to the static regions and subtracted from the phase velocity images to give corrected phase images. Random noise was removed by setting to zero those regions in which the standard deviation was high. The technique is demonstrated with a sample set of data in which the in-plane velocities have been measured in an imaging section showing the left ventricular outflow tract of a human left ventricle. The results are presented in vector and contour form, superimposed on the conventional MR angiographic images.