Benjamin Köhler

Semi-Automatic Vortex Extraction in 4D PC-MRI Cardiac Blood Flow Data using Line Predicates

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.

A Survey of Cardiac 4D PC-MRI Data Processing

Cardiac four‐dimensional phase‐contrast magnetic resonance imaging (4D PC‐MRI) acquisitions have gained increasing clinical interest in recent years. They allow to non‐invasively obtain extensive information about patient‐specific hemodynamics, and thus have a great potential to improve the diagnosis, prognosis and therapy planning of cardiovascular diseases. A dataset contains time‐resolved, three‐dimensional blood flow directions and strengths, making comprehensive qualitative and quantitative data analysis possible. Quantitative measures, such as stroke volumes, help to assess the cardiac function and to monitor disease progression. Qualitative analysis allows to investigate abnormal flow characteristics, such as vortices, which are correlated to different pathologies. Processing the data comprises complex image processing methods, as well as flow analysis and visualization. In this work, we mainly focus on the aorta. We provide an overview of data measurement and pre‐processing, as well as current visualization and quantification methods. This allows other researchers to quickly catch up with the topic and take on new challenges to further investigate the potential of 4D PC‐MRI data.

Robust Cardiac Function Assessment in 4D PC-MRI Data of the Aorta and Pulmonary Artery

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 planes 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 vessels 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.

A survey of cardiac 4D PC-MRI data processing

Cardiac 4D PC-MRI acquisitions gained increasing clinical interest in recent years. They allow to non-invasively obtain extensive information about patient-specific hemodynamics and thus have a great potential to improve the diagnosis of cardiovascular diseases. A dataset contains time-resolved, three-dimensional blood flow directions and strengths, facilitating comprehensive qualitative and quantitative data analysis. The quantification of measures such as stroke volumes helps to assess the cardiac function and monitor disease progression. Qualitative analysis allows to investigate abnormal flow characteristics, such as vortices, that are correlated to different pathologies. Processing the data comprises complex image processing methods as well as flow analysis and visualization. In this work, we mainly focus on the aorta. We provide an overview from data measurement and preprocessing to current visualization and quantification methods so that other researchers can quickly catch up with the topic and take on new challenges to further investigate the potential of 4D PC-MRI.

On the evaluation of a semi-automatic vortex flow classification in 4D PC-MRI data of the aorta

In this paper, we report on our experiences that we made during our contributions in the field of the visualization of flow characteristics. Mainly, we focused on the vortex flow classification in 4D PC-MRI as current medical studies assume a strong correlation between cardiovascular diseases and blood flow patterns such as vortices. For further analysis, medical experts are asked to manually extract and classify such vortices according to specific properties. We presented and evaluated techniques that enable a fast and robust vortex classification [MLK* 16, MKP* 16] that supports medical experts. The main focus in this paper is a report that describes our conversations with the domain experts. The dialog was the fundament that gave us the direction of what the experts need. We derived several requirements that should be fulfilled by our tool. From this, we developed a prototype that supports the experts. Finally, we describe the evaluation of our framework and discuss currently limitations.

Clustering of Aortic Vortex Flow in Cardiac 4D PC-MRI Data

This paper presents a method for clustering aortic vortical blood flow using a reliable dissimilarity measure combined with a clustering technique. Current medical studies investigate specific properties of aberrant blood flow patterns such as vortices, since a correlation to the genesis and evolution of various cardiovascular diseases is assumed. The classification requires a precise definition of spatio-temporal vortex entities, which is performed manually. This task is time-consuming for larger studies and error-prone due to inter-observer variability. In contrast, our method allows an automatic and reliable vortex clustering that facilitates the vortex classification. We introduce an efficient calculation of a dissimilarity measure that groups spatio-temporally adjacent vortices. We combine our dissimilarity measure with the most commonly used clustering techniques. Each combination was applied to 15 4D PCMRI datasets. The clustering results were qualitatively compared to a manually generated ground truth of two domain experts.

Guided Analysis of Cardiac 4D PC-MRI Blood Flow Data

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 helps 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.

2D Plot Visualization of Aortic Vortex Flow in Cardiac 4D PC-MRI Data

Aortic vortex flow is a strong indicator for various cardiovascular diseases. The correlation of pathologies like bicuspid aortic valves to the occurrence of such flow patterns at specific spatio-temporal positions 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 assessment 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. After a short familiarization with the plots our collaborating cardiologists and radiologists were able distinguish between patient and healthy volunteer datasets with ease.

Robust cardiac function assessment in 4D PC-MRI data

Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) is a relatively young image modality that allows the non-invasive acquisition of time-resolved, three-dimensional blood flow information. Stroke volumes and regurgitation fractions are two of the main measures to assess the cardiac function and severity of pathologies. The flow volumes in forward and backward direction through a plane inside the vessel are required for their quantification. Unfortunately, the calculations are highly sensitive towards the planes 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 stroke volumes in various healthy volunteer and patient datasets with conventional quantification. This facilitated the automatization 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 stroke volume along the vessel course after a peak value above the aortic valve. Conventional methods are often unable to produce this behavior. Thus, we present a procedure to fit a function that ensures such physiologically plausible results. In addition, the technique was adapted for the robust quantification of regurgitation fractions. The performed qualitative evaluation shows the capability of our method to support diagnosis, a parameter evaluation confirms the robustness. Vortex flow was identified as main cause for quantification uncertainties.

Visual and quantitative analysis of great arteries’ blood flow jets in cardiac 4D PC-MRI data

Flow in the great arteries (aorta, pulmonary artery) is normally laminar with a parabolic velocity profile. Eccentric flow jets are linked to various diseases like aneurysms. Cardiac 4D PC‐MRI data provide spatio‐temporally resolved blood flow information for the whole cardiac cycle. In this work, we establish a time‐dependent visualization and quantification of flow jets. For this purpose, equidistant measuring planes are automatically placed along the vessels centerline. The flow jet position and region with highest velocities are extracted for every plane in each time step. This is done during pre‐processing and without user‐defined parameters. We visualize the main flow jet as geometric tube. High‐velocity areas are depicted as a net around this tube. Both geometries are time‐dependent and can be animated. Quantitative values are provided during cross‐sectional measuring plane‐based evaluation. Moreover, we offer a plot visualization as summary of flow jet characteristics for the selected plane. Our physiologically plausible results are in accordance with medical findings. Our clinical collaborators appreciate the possibility to view the flow jet in the whole vessel at once, which normally requires repeated pathline filtering due to varying velocities along the vessel course. The overview plots are considered as valuable for documentation purposes.