Within the first 10 years of diabetes duration, the prevalence of retinopathy is low and the progression infrequent. Most patients have a non-proliferative form which can be reversible and rarely requires interventions. Patients with DM2 without retinopathy and good glycaemic control do not run into additional risk from expanding funduscopy intervals to biennial.

This indicates an improperly closing aortic valve and supports the decision whether or not to implant an artificial valve.Abstract-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.

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.

Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows the non-invasive acquisition of timeresolved, 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.

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.

Background Four-dimensional (4D) flow magnetic resonance imaging (MRI) sequences with advanced parallel imaging have the potential to reduce scan time with equivalent image quality and accuracy compared with standard two-dimensional (2D) flow MRI. We compared 4D flow to standard 2D flow sequences using a constant and pulsatile flow phantom at 3 T. Methods Two accelerated 4D flow sequences (GRAPPA2 and k-t -GRAPPA5) were evaluated regarding the concordance of flow volumes, flow velocities, and reproducibility as well as dependency on measuring plane and velocity encoding ( V enc ). The calculated flow volumes and peak velocities of the phantom were used as reference standard. Flow analysis was performed using the custom-made software “ Bloodline ”. Results No significant differences in flow volume were found between the 2D, both 4D flow MRI sequences, and the pump reference ( p = 0.994) or flow velocities ( p = 0.998) in continuous and pulsatile flow. An excellent correlation ( R = 0.99–1.0) with a reference standard and excellent reproducibility of measurements ( R = 0.99) was achieved for all sequences. A V enc overestimated by up to two times had no impact on flow measurements. However, misaligned measuring planes led to an increasing underestimation of flow volume and mean velocity in 2D flow accuracy, while both 4D flow measurements were not affected. Scan time was significantly shorter for k-t -GRAPPA5 (1:54 ± 0:01 min, mean ± standard deviation) compared to GRAPPA2 (3:56 ± 0:02 min) ( p = 0.002). Conclusions Both 4D flow sequences demonstrated equal agreement with 2D flow measurements, without impact of V enc overestimation and plane misalignment. The highly accelerated k-t -GRAPPA5 sequence yielded results similar to those of GRAPPA2.

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We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi-automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex-representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. The classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. The results of the semi-automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.