We comprehensively studied morphological and functional aortic aging in a population study using modern three-dimensional MR imaging to allow future comparison in patients with diseases of the aortic ...valve or aorta. We followed 80 of 126 subjects of a population study (20 to 80 years of age at baseline) using the identical methodology 6.0 ± 0.5 years later. All underwent 3 T MRI of the thoracic aorta including 3D T1 weighted MRI (spatial resolution 1 mm
) for measuring aortic diameter and plaque thickness and 4D flow MRI (spatial/temporal resolution = 2 mm
/20 ms) for calculating global and regional aortic pulse wave velocity (PWV) and helicity of aortic blood flow. Mean diameter of the ascending aorta (AAo) decreased and plaque thickness increased significantly in the aortic arch (AA) and descending aorta (DAo) in females. PWV of the thoracic aorta increased (6.4 ± 1.5 to 7.0 ± 1.7 m/s and 6.8 ± 1.5 to 7.3 ± 1.8 m/s in females and males, respectively) over time. Local normalized helicity volumes (LNHV) decreased significantly in the AAo and AA (0.33 to 0.31 and 0.34 to 0.32 in females and 0.34 to 0.32 and 0.32 to 0.28 in males). By contrast, helicity increased significantly in the DAo in both genders (0.28 to 0.29 and 0.29 to 0.30, respectively). 3D MRI was able to characterize changes in aortic diameter, plaque thickness, PWV and helicity during six years in our population. Aortic aging determined by 3D multi-parametric MRI is now available for future comparisons in patients with diseases of the aortic valve or aorta.
Purpose
Careful assessment of the aortic root is paramount to select an appropriate prosthesis for transcatheter aortic valve implantation (TAVI). Relevant information about the aortic root anatomy, ...such as the aortic annulus diameter, can be extracted from pre-interventional CT. In this work, we investigate a neural network-based approach for segmenting the aortic root as a basis for obtaining these parameters.
Methods
To support valve prosthesis selection, geometric measures of the aortic root are extracted from the patient’s CT scan using a cascade of convolutional neural networks (CNNs). First, the image is reduced to the aortic root, valve, and left ventricular outflow tract (LVOT); within that subimage, the aortic valve and ascending aorta are segmented; and finally, the region around the aortic annulus. From the segmented annulus region, we infer the annulus orientation using principal component analysis (PCA). The area-derived diameter of the annulus is approximated based on the segmentation of the aortic root and LVOT and the plane orientation resulting from the PCA.
Results
The cascade of CNNs was trained using 90 expert-annotated contrast-enhanced CT scans routinely acquired for TAVI planning. Segmentation of the aorta and valve within the region of interest achieved an F1 score of 0.94 on the test set of 36 patients. The area-derived diameter within the annulus region was determined with a mean error below 2 mm between the automatic measurement and the diameter derived from annotations. The calculated diameters and resulting errors are comparable to published results of alternative approaches.
Conclusions
The cascaded neural network approach enabled the assessment of the aortic root with a relatively small training set. The processing time amounts to 30 s per patient, facilitating time-efficient, reproducible measurements. An extended training data set, including different levels of calcification or special cases (e.g., pre-implanted valves), could further improve this method’s applicability and robustness.
We present a novel approach to simulate deformation in aortic valve replacement scenarios with applications in operation planning and batch domain creation for large computational fluid dynamics ...studies of the aortic arch.
Purpose
Various options are available for the treatment of mitral valve insufficiency, including reconstructive approaches such as annulus correction through ring implants. The correct choice of ...general therapy and implant is relevant for an optimal outcome. Additional to guidelines, decision support systems (DSS) can provide decision aid by means of virtual intervention planning and predictive simulations. Our approach on virtual downsizing is one of the virtual intervention tools that are part of the DSS workflow. It allows for emulating a ring implantation based on patient-specific lumen geometry and vendor-specific implants.
Methods
Our approach is fully automatic and relies on a lumen mask and an annulus contour as inputs. Both are acquired from previous DSS workflow steps. A virtual surface- and contour-based model of a vendor-specific ring design (26–40 mm) is generated. For each case, the ring geometry is positioned with respect to the original, patient-specific annulus and additional anatomical landmarks. The lumen mesh is parameterized to allow for a vertex-based deformation with respect to the user-defined annulus. Derived from post-interventional observations, specific deformation schemes are applied to atrium and ventricle and the lumen mesh is altered with respect to the ring location.
Results
For quantitative evaluation, the surface distance between the deformed lumen mesh and segmented post-operative echo lumen close to the annulus was computed for 11 datasets. The results indicate a good agreement. An arbitrary subset of six datasets was used for a qualitative evaluation of the complete lumen. Two domain experts compared the deformed lumen mesh with post-interventional echo images. All deformations were deemed plausible.
Conclusion
Our approach on virtual downsizing allows for an automatic creation of plausible lumen deformations. As it takes only a few seconds to generate results, it can be added to a virtual intervention toolset without unnecessarily increasing the pipeline complexity.
Cardiac diseases manifest in a multitude of interconnected changes in morphology and dynamics. Radiomics approaches are a promising technique to analyze such changes directly from image data. We ...propose novel features to specifically describe moving cardiac structures, and an interactive 4D visualization method to explore such data. Prototypical tests with an open data set containing different diseases show that our approach can be a fast and useful tool for the 4D analysis of heterogeneous cohort data.
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac ...structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12 th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.
. This study assesses age-related differences of thoracic aorta blood flow profiles and provides age- and sex-specific reference values using 4D flow cardiovascular magnetic resonance (CMR) data.
. ...126 volunteers (age 20-80 years, female 51%) underwent 4D flow CMR and 12 perpendicular analysis planes in the thoracic aorta were specified. For these planes the following parameters were evaluated: body surface area-adjusted aortic area (
'), normalized flow displacement (NFD), the degree of wall parallelism (WPD), the minimal relative cross-sectional area through which 80% of the volume flow passes (A80) and the angle between flow direction and centerline (
).
. Age-related differences in blood flow parameters were seen in the ascending aorta with higher values for NFD and angle and lower values for WPD and A80 in older subjects. All parameters describing blood flow patterns correlated with the cross-sectional area in the ascending aorta. No relevant sex-differences regarding blood flow profiles were found.
. These age- and sex-specific reference values for quantitative parameters describing blood flow within the aorta might help to study the clinical relevance of flow profiles in the future.
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