With the development of three-dimensional (3D) printing, 3D-printed products have been widely used in medical fields, such as plastic surgery, orthopedics, dentistry, etc. In cardiovascular research, 3D-printed models are becoming more realistic in shape. However, from a biomechanical point of view, only a few studies have explored printable materials that can represent the properties of the human aorta. This study focuses on 3D-printed materials that might simulate the stiffness of human aortic tissue. First, the biomechanical properties of a healthy human aorta were defined and used as reference. The main objective of this study was to identify 3D printable materials that possess similar properties to the human aorta. Three synthetic materials, NinjaFlex (Fenner Inc., Manheim, USA), FilasticTM (Filastic Inc., Jardim Paulistano, Brazil), and RGD450+TangoPlus (Stratasys Ltd.©, Rehovot, Israel), were printed in different thicknesses. Uniaxial and biaxial tensile tests were performed to compute several biomechanical properties, such as thickness, stress, strain, and stiffness. We found that with the mixed material RGD450+TangoPlus, it was possible to achieve a similar stiffness to healthy human aorta. Moreover, the 50-shore-hardness RGD450+TangoPlus had similar thickness and stiffness to the human aorta.
A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.
OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure. METHODS: Automatic segmentation of the aorta was performed with methods based on deep learning and multi-atlas using the systolic phase in the 4D flow MRI magnitude image of 36 patients. Using mesh morphing, isotopological meshes were generated, and CFD was performed to calculate the aortic wall pressure. Node-to-node comparisons of the pressure results were made to identify the most robust automatic method respect to the pressures obtained with a manually segmented model. RESULTS: Deep learning approach presented the best segmentation performance with a mean Dice similarity coefficient and a mean Hausdorff distance (HD) equal to 0.92+/- 0.02 and 21.02+/- 24.20 mm, respectively. At the global level HD is affected by the performance in the abdominal aorta. Locally, this distance decreases to 9.41+/- 3.45 and 5.82+/- 6.23 for the ascending and descending thoracic aorta, respectively. Moreover, with respect to the pressures from the manual segmentations, the differences in the pressures computed from deep learning were lower than those computed from multi-atlas method. CONCLUSION: To reduce biases in the calculation of aortic wall pressure, accurate segmentation is needed, particularly in regions with high blood flow velocities. Thus, the deep learning segmen-tation method should be preferred.
Deep Learning , Humans , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Aorta, Abdominal/diagnostic imaging , Blood Flow Velocity
BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure iteration which can be obtained from 4D flow MRI. However, the generation of these biomarkers requires prior 4D segmentation of the aorta. OBJECTIVE: To develop an automatic deep learning model to segment the aorta in 4D from 4D flow MRI. METHODS: Segmentation is addressed with a U-Net based segmentation model that treats each 4D flow MRI frame as an independent sample. Performance is measured with respect to Dice score (DS) and Hausdorff distance (HD). In addition, the maximum and minimum surface areas at the level of the ascending aorta are measured and compared with those obtained from cine-MRI. RESULTS: The segmentation performance was 0.90 ± 0.02 for the DS and the mean HD was 9.58 ± 4.36 mm. A correlation coefficient of r = 0.85 was obtained for the maximum surface and r = 0.86 for the minimum surface between the 4D flow MRI and cine-MRI. CONCLUSION: The proposed automatic approach of 4D aortic segmentation from 4D flow MRI seems to be accurate enough to contribute to the wider use of this imaging technique in the analysis of pathologies such as TAA.
Aortic Aneurysm, Thoracic , Deep Learning , Humans , Aorta, Thoracic , Magnetic Resonance Imaging/methods , Aorta , Magnetic Resonance Imaging, Cine/methods , Blood Flow Velocity
Introducción: el lupus eritematoso sistémico (LES) es una enfermedad sistémica que se ha asociado a mayor severidad con la infección por SARS-CoV-2. Particularmente la alta actividad de la enfermedad y algunos inmunosupresores se han vinculado a peores desenlaces. Objetivos: describir las características por SARS-CoV-2 en pacientes con LES en Argentina del registro SAR-COVID y establecer los factores asociados a peor desenlace de la misma. Materiales y métodos: estudio observacional. Se incluyeron pacientes con diagnóstico de LES con infección confirmada por SARS-CoV-2 (RT-PCR y/o serología positiva) del registro SAR-COVID. Los datos se recolectaron desde agosto de 2020 hasta marzo de 2022. El desenlace de la infección se midió mediante la escala ordinal de la Organización Mundial de la Salud (EO-OMS). Se definió COVID-19 severo con un valor EO-OMS ≥5. Análisis descriptivo, test T de Student, test de Mann Whitney U, ANOVA, chi2 y Fisher. Regresión logística múltiple. Resultados: se incluyeron 399 pacientes, el 93% de sexo femenino, con una edad media de 40,9 años (DE 12,2). El 39,6% tenía al menos una comorbilidad. Al momento de la infección, el 54,9% recibía glucocorticoides, el 30,8% inmunosupresores y el 3,3% agentes biológicos. La infección por SARS-CoV-2 fue leve en la mayoría de los casos, mientras que un 4,6% tuvo curso severo y/o falleció. Estos últimos presentaban comorbilidades, usaban glucocorticoides y tenían síndrome antifosfolipídico (SAF) con mayor frecuencia y mayor actividad de la enfermedad al momento de la infección. En el análisis multivariado, la hipertensión arterial, el diagnóstico de SAF y el uso de glucocorticoides se asociaron a hospitalización severa y/o muerte por COVID-19 (EO-OMS ≥5). Conclusiones: en esta cohorte de pacientes con LES con infección por SARS-CoV-2 confirmada, la mayoría cursó de manera sintomática, un 22,1% fue hospitalizado y un 5% requirió ventilación mecánica. La mortalidad fue cercana al 3%. El diagnóstico de SAF, tener hipertensión arterial y el uso de glucocorticoides se asociaron significativamente con COVID-19 severo.
Introduction: systemic lupus erythematosus (SLE) is a systemic disease that has been associated with greater severity with SARS-CoV-2 infection. Particularly high disease activity and some immunosuppressants have been linked to worse outcomes. Objectives: to describe the characteristics due to SARS-CoV-2 in patients with SLE in Argentina from the SAR-COVID registry and to establish the factors associated with a worse outcome of the same. Materials and methods: observational study. Patients diagnosed with SLE with confirmed SARS-CoV-2 infection (RT-PCR and/or positive serology) from the SAR-COVID registry were included. Data was collected from August 2020 to March 2022. The outcome of the infection was measured using the World Health Organization - ordinal scale (WHO-OS). Severe COVID-19 was defined as an WHO-OS value ≥5. Descriptive analysis, Student's T test, Mann Whitney U, ANOVA, chi2 and Fisher. Multiple logistic regression. Results: a total of 399 patients were included, 93% female, with a mean age of 40.9 years (SD 12.2), 39.6% had at least one comorbidity. At the time of infection, 54.9% were receiving glucocorticoids, 30.8% immunosuppressants, and 3.3% biological agents. SARS-CoV-2 infection was mild in most cases, while 4.6% had a severe course and/or died. The latter had comorbidities, used glucocorticoids and had antiphospholipid syndrome (APS) more frequently and higher disease activity at the time of infection. In the multivariate analysis, high blood pressure, the diagnosis of APS, and the use of glucocorticoids were associated with severe hospitalization and/or death from COVID-19 (WHO-EO ≥5). Conclusions: in this cohort of SLE patients with confirmed SARS-CoV-2 infection, most had a symptomatic course, 22.1% were hospitalized, and 5% required mechanical ventilation. Mortality was close to 3%. The diagnosis of APS, having high blood pressure, and the use of glucocorticoids were significantly associated with severe COVID-19.
Pandemics
Association of quadricuspid aortic valve (QAV) with ascending aortic aneurysms (AsAA) is rare. A 63-year-old female with hypertension was found (on MRI) to have an ascending aortic aneurysm (52 mm in maximum diameter) and dilatation at the level of the sinotubular junction (38 mm in diameter) associated with quadricuspid aortic valve. An ascending aortic wall replacement surgery was performed. In this study, we focus on the behavior of the aorta associated with QAV considering the in vitro biomechanical characteristics and histology. The properties of QAV are closer to bicuspid aortic valve than tricuspid aortic valve, but with higher wall thickness.
Ascending aortic aneurysm (AsAA) is a high-risk cardiovascular disease with an increased incidence over years. In this study, we compared different risk factors based on the pre-failure behavior (from a biomechanical point of view) obtained ex-vivo from an equi-biaxial tensile test. A total of 100 patients (63 ± 12 years, 72 males) with AsAA replacement, were recruited. Equi-biaxial tensile tests of AsAA walls were performed on freshly sampled aortic wall tissue after ascending aortic replacement. The aneurysmal aortic walls were divided into four quadrants (medial, anterior, lateral, and posterior) and two directions (longitudinal and circumferential) were considered. The stiffness was represented by the maximum Young modulus (MYM). Based on patient information, the following subgroups were considered: age, gender, hypertension, obesity, dyslipidemia, diabetes, smoking history, aortic insufficiency, aortic stenosis, coronary artery disease, aortic diameter and aortic valve type. In general, when the aortic diameter increased, the aortic wall became thicker. In terms of the MYM, the longitudinal direction was significantly higher than that in the circumferential direction. In the multivariant analysis, the impact factors of age (p = 0.07), smoking (p = 0.05), diabetes (p = 0.03), aortic stenosis (p = 0.02), coronary artery disease (p < 10-3), and aortic diameters (p = 0.02) were significantly influencing the MYM. There was no significant MYM difference when the patients presented arterial hypertension, dyslipidemia, obesity, or bicuspid aortic valve. To conclude, the pre-failure aortic stiffness is multi-factorial, according to our population of 100 patients with AsAA. STATEMENT OF SIGNIFICANCE: Our research on the topic of "Aortic local biomechanical properties in case of ascending aortic aneurysms" is about the biomechanical properties on one hundred aortic samples according to the aortic wall quadrants and the direction. More than ten factors and risks which may impact ascending aortic aneurysms have been studied. According to our knowledge, so far, this article involved the largest population on this topic. It will be our pleasure to share this information with all the readers.
Aortic Aneurysm, Thoracic , Aortic Aneurysm , Aortic Valve Stenosis , Diabetes Mellitus , Hypertension , Aorta , Aortic Aneurysm/etiology , Aortic Valve , Biomechanical Phenomena , Humans , Male , Obesity
