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  • On the role of tissue mechanics in fluid-structure interaction simulations of patient-specific aortic dissection INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING Schussnig, R., Rolf-Pissarczyk, M., Baeumler, K., Fries, T., Holzapfel, G. A., Kronbichler, M. 2024

    View details for DOI 10.1002/nme.7478

    View details for Web of Science ID 001197278900001

  • Hemodynamic effects of entry and exit tear size in aortic dissection evaluated with in vitro magnetic resonance imaging and fluid-structure interaction simulation. Scientific reports Zimmermann, J., Bäumler, K., Loecher, M., Cork, T. E., Marsden, A. L., Ennis, D. B., Fleischmann, D. 2023; 13 (1): 22557


    Understanding the complex interplay between morphologic and hemodynamic features in aortic dissection is critical for risk stratification and for the development of individualized therapy. This work evaluates the effects of entry and exit tear size on the hemodynamics in type B aortic dissection by comparing fluid-structure interaction (FSI) simulations with in vitro 4D-flow magnetic resonance imaging (MRI). A baseline patient-specific 3D-printed model and two variants with modified tear size (smaller entry tear, smaller exit tear) were embedded into a flow- and pressure-controlled setup to perform MRI as well as 12-point catheter-based pressure measurements. The same models defined the wall and fluid domains for FSI simulations, for which boundary conditions were matched with measured data. Results showed exceptionally well matched complex flow patterns between 4D-flow MRI and FSI simulations. Compared to the baseline model, false lumen flow volume decreased with either a smaller entry tear (- 17.8 and - 18.5%, for FSI simulation and 4D-flow MRI, respectively) or smaller exit tear (- 16.0 and - 17.3%). True to false lumen pressure difference (initially 11.0 and 7.9 mmHg, for FSI simulation and catheter-based pressure measurements, respectively) increased with a smaller entry tear (28.9 and 14.6 mmHg), and became negative with a smaller exit tear (- 20.6 and - 13.2 mmHg). This work establishes quantitative and qualitative effects of entry or exit tear size on hemodynamics in aortic dissection, with particularly notable impact observed on FL pressurization. FSI simulations demonstrate acceptable qualitative and quantitative agreement with flow imaging, supporting its deployment in clinical studies.

    View details for DOI 10.1038/s41598-023-49942-0

    View details for PubMedID 38110526

    View details for PubMedCentralID PMC10728172

  • Longitudinal investigation of aortic dissection in mice with computational fluid dynamics. Computer methods in biomechanics and biomedical engineering Bäumler, K., Phillips, E. H., Grande Gutiérrez, N., Fleischmann, D., Marsden, A. L., Goergen, C. J. 2023: 1-14


    Predicting late adverse events in aortic dissections is challenging. One commonly observed risk factor is partial thrombosis of the false lumen. In this study we investigated false lumen thrombus progression over 7 days in four mice with angiotensin II-induced aortic dissection. We performed computational fluid dynamic simulations with subject-specific boundary conditions from velocity and pressure measurements. We investigated endothelial cell activation potential, mean velocity, thrombus formation potential, and other hemodynamic factors. Our findings support the hypothesis that flow stagnation is the predominant hemodynamic factor driving a large thrombus ratio in false lumina, particularly those with a single fenestration.

    View details for DOI 10.1080/10255842.2023.2274281

    View details for PubMedID 37897230

  • Early clinical outcomes and molecular smooth muscle cell phenotyping using a prophylactic aortic arch replacement strategy in Loeys-Dietz syndrome. The Journal of thoracic and cardiovascular surgery Pedroza, A. J., Cheng, P., Dalal, A. R., Baeumler, K., Kino, A., Tognozzi, E., Shad, R., Yokoyama, N., Nakamura, K., Mitchel, O., Hiesinger, W., MacFarlane, E. G., Fleischmann, D., Woo, Y. J., Quertermous, T., Fischbein, M. P. 2023


    Loeys-Dietz syndrome (LDS) patients demonstrate heightened risk of distal thoracic aortic events after valve-sparing aortic root replacement (VSARR). This study assesses the clinical risks and hemodynamic consequences of a prophylactic aortic arch replacement strategy in LDS and characterizes smooth muscle cell (SMC) phenotype in LDS aneurysmal and normal-sized downstream aorta.Patients with genetically confirmed LDS (n=8) underwent prophylactic aortic arch replacement during VSARR. 4D flow magnetic resonance imaging (MRI) studies were performed in n=4 LDS patients (VSARR+arch) and compared with both contemporary Marfan syndrome patients (VSARR only, n=5) and control patients (without aortopathy, n=5). Aortic tissues from n=4 LDS patients and n=2 organ donors were processed for anatomically segmented single-cell RNA sequencing (scRNAseq) and histologic assessment.LDS VSARR+arch patients had no deaths, major morbidity, or aortic events in median 2.00 years follow-up. 4D-MRI demonstrated altered flow parameters in post-operative aortopathy patients relative to controls, but no clear deleterious changes attributable to arch replacement. Integrated analysis of aortic scRNAseq data (>49,000 cells) identified a continuum of abnormal SMC phenotypic modulation in LDS defined by reduced contractility and enriched extracellular matrix synthesis, adhesion receptors, and transforming growth factor-beta signaling. These 'modulated SMCs' populated the LDS tunica media with gradually reduced density from the overtly aneurysmal root to the non-dilated arch.LDS patients demonstrated excellent surgical outcomes without overt downstream flow or shear stress disturbances after concomitant VSARR+arch operations. Abnormal SMC-mediated aortic remodeling occurs within the normal diameter, clinically at-risk LDS arch segment. These initial clinical and pathophysiologic findings support concomitant arch replacement in LDS.

    View details for DOI 10.1016/j.jtcvs.2023.07.023

    View details for PubMedID 37500053

  • Hemodynamic Effects of Entry Versus Exit Tear Size and Tissue Stiffness in Simulations of Aortic Dissection Baumler, K., Zimmermann, J., Ennis, D. B., Marsden, A. L., Fleischmann, D., Tavares, J. M., Bourauel, C., Geris, L., Slote, J. V. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 143-152
  • Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) in Uncomplicated Type B Aortic Dissection: Study Design and Rationale. Radiology. Cardiothoracic imaging Mastrodicasa, D., Willemink, M. J., Turner, V. L., Hinostroza, V., Codari, M., Hanneman, K., Ouzounian, M., Ocazionez Trujillo, D., Afifi, R. O., Hedgire, S., Burris, N. S., Yang, B., Lacomis, J. M., Gleason, T. G., Pacini, D., Folesani, G., Lovato, L., Hinzpeter, R., Alkadhi, H., Stillman, A. E., Chen, E. P., van Kuijk, S. M., Schurink, G. W., Sailer, A. M., Bäumler, K., Miller, D. C., Fischbein, M. P., Fleischmann, D. 2022; 4 (6): e220039


    To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD).The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique.The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling.This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue.

    View details for DOI 10.1148/ryct.220039

    View details for PubMedID 36601455

    View details for PubMedCentralID PMC9806732

  • Artificial Intelligence Applications in Aortic Dissection Imaging. Seminars in roentgenology Mastrodicasa, D., Codari, M., Bäumler, K., Sandfort, V., Shen, J., Mistelbauer, G., Hahn, L. D., Turner, V. L., Desjardins, B., Willemink, M. J., Fleischmann, D. 2022; 57 (4): 357-363

    View details for DOI 10.1053/

    View details for PubMedID 36265987

  • Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection. European radiology Willemink, M. J., Mastrodicasa, D., Madani, M. H., Codari, M., Chepelev, L. L., Mistelbauer, G., Hanneman, K., Ouzounian, M., Ocazionez, D., Afifi, R. O., Lacomis, J. M., Lovato, L., Pacini, D., Folesani, G., Hinzpeter, R., Alkadhi, H., Stillman, A. E., Sailer, A. M., Turner, V. L., Hinostroza, V., Baumler, K., Chin, A. S., Burris, N. S., Miller, D. C., Fischbein, M. P., Fleischmann, D. 2022


    OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning.METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots.RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement.CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models.KEY POINTS: Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

    View details for DOI 10.1007/s00330-022-09056-z

    View details for PubMedID 36029344

  • Artificial intelligence and machine learning in aortic disease. Current opinion in cardiology Hahn, L. D., Baeumler, K., Hsiao, A. 2021


    PURPOSE OF REVIEW: Discuss foundational concepts for artificial intelligence (AI) and review recent literature on its application to aortic disease.RECENT FINDINGS: Machine learning (ML) techniques are rapidly evolving for the evaluation of aortic disease - broadly categorized as algorithms for aortic segmentation, detection of pathology, and risk stratification. Advances in deep learning, particularly U-Net architectures, have revolutionized segmentation of the aorta and show potential for monitoring the size of aortic aneurysm and characterizing aortic dissection. These algorithms also facilitate application of more complex technologies including analysis of flow dynamics with 4D Flow magnetic resonance imaging (MRI) and computational simulation of fluid dynamics for aortic coarctation. In addition, AI algorithms have been proposed to assist in 'opportunistic' screening from routine imaging exams, including automated aortic calcification score, which has emerged as a strong predictor of cardiovascular risk. Finally, several ML algorithms are being explored for risk stratification of patients with aortic aneurysm and dissection, in addition to prediction of postprocedural complications.SUMMARY: Multiple ML techniques have potential for characterization and risk prediction of aortic aneurysm, dissection, coarctation, and atherosclerotic disease on computed tomography and MRI. This nascent field shows considerable promise with many applications in development and in early preclinical evaluation.

    View details for DOI 10.1097/HCO.0000000000000903

    View details for PubMedID 34369401

  • Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors COMPUTER GRAPHICS FORUM Mistelbauer, G., Rossl, C., Baeumler, K., Preim, B., Fleischmann, D. 2021; 40 (3): 423-434

    View details for DOI 10.1111/cgf.14318

    View details for Web of Science ID 000667924000035

  • On the impact of vessel wall stiffness on quantitative flow dynamics in a synthetic model of the thoracic aorta. Scientific reports Zimmermann, J. n., Loecher, M. n., Kolawole, F. O., Bäumler, K. n., Gifford, K. n., Dual, S. A., Levenston, M. n., Marsden, A. L., Ennis, D. B. 2021; 11 (1): 6703


    Aortic wall stiffening is a predictive marker for morbidity in hypertensive patients. Arterial pulse wave velocity (PWV) correlates with the level of stiffness and can be derived using non-invasive 4D-flow magnetic resonance imaging (MRI). The objectives of this study were twofold: to develop subject-specific thoracic aorta models embedded into an MRI-compatible flow circuit operating under controlled physiological conditions; and to evaluate how a range of aortic wall stiffness impacts 4D-flow-based quantification of hemodynamics, particularly PWV. Three aorta models were 3D-printed using a novel photopolymer material at two compliant and one nearly rigid stiffnesses and characterized via tensile testing. Luminal pressure and 4D-flow MRI data were acquired for each model and cross-sectional net flow, peak velocities, and PWV were measured. In addition, the confounding effect of temporal resolution on all metrics was evaluated. Stiffer models resulted in increased systolic pressures (112, 116, and 133 mmHg), variations in velocity patterns, and increased peak velocities, peak flow rate, and PWV (5.8-7.3 m/s). Lower temporal resolution (20 ms down to 62.5 ms per image frame) impacted estimates of peak velocity and PWV (7.31 down to 4.77 m/s). Using compliant aorta models is essential to produce realistic flow dynamics and conditions that recapitulated in vivo hemodynamics.

    View details for DOI 10.1038/s41598-021-86174-6

    View details for PubMedID 33758315

  • Deep Learning-Based 3D Segmentation of True Lumen, False Lumen, and False Lumen Thrombosis in Type-B Aortic Dissection. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Wobben, L. D., Codari, M., Mistelbauer, G., Pepe, A., Higashigaito, K., Hahn, L. D., Mastrodicasa, D., Turner, V. L., Hinostroza, V., Baumler, K., Fischbein, M. P., Fleischmann, D., Willemink, M. J. 2021; 2021: 3912-3915


    Patients with initially uncomplicated typeB aortic dissection (uTBAD) remain at high risk for developing late complications. Identification of morphologic features for improving risk stratification of these patients requires automated segmentation of computed tomography angiography (CTA) images. We developed three segmentation models utilizing a 3D residual U-Net for segmentation of the true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT). Model 1 segments all labels at once, whereas model 2 segments them sequentially. Best results for TL and FL segmentation were achieved by model 2, with median (interquartiles) Dice similarity coefficients (DSC) of 0.85 (0.77-0.88) and 0.84 (0.82-0.87), respectively. For FLT segmentation, model 1 was superior to model 2, with median (interquartiles) DSCs of 0.63 (0.40-0.78). To purely test the performance of the network to segment FLT, a third model segmented FLT starting from the manually segmented FL, resulting in median (interquartiles) DSCs of 0.99 (0.98-0.99) and 0.85 (0.73-0.94) for patent FL and FLT, respectively. While the ambiguous appearance of FLT on imaging remains a significant limitation for accurate segmentation, our pipeline has the potential to help in segmentation of aortic lumina and thrombosis in uTBAD patients.Clinical relevance- Most predictors of aortic dissection (AD) degeneration are identified through anatomical modeling, which is currently prohibitive in clinical settings due to the timeintense human interaction. False lumen thrombosis, which often develops in patients with type B AD, has proven to show significant prognostic value for predicting late adverse events. Our automated segmentation algorithm offers the potential of personalized treatment for AD patients, leading to an increase in long-term survival.

    View details for DOI 10.1109/EMBC46164.2021.9631067

    View details for PubMedID 34892087

  • Quantitative Hemodynamics in Aortic Dissection: Comparing in Vitro MRI with FSI Simulation in a Compliant Model Functional Imaging and Modeling of the Heart Zimmermann, J., Baumler, K., Loecher, M., Cork, T. E., Kolawole, F. O., Gifford, K., Marsden, A. L., Fleischmann, D., Ennis, D. . 2021: 575-586
  • Deep Flow-Net for EPI Distortion Estimation. NeuroImage Zahneisen, B., Baeumler, K., Zaharchuk, G., Fleischmann, D., Zeineh, M. 2020: 116886


    INTRODUCTION: Geometric distortions along the phase encoding direction caused by off-resonant spins are a major issue in EPI based functional and diffusion imaging. The widely used blip up/down approach estimates the underlying distortion field from a pair of images with inverted phase encoding direction. Typically, iterative methods are used to find a solution to the ill-posed problem of finding the displacement field that maps up/down acquisitions onto each other. Here, we explore the use of a deep convolutional network to estimate the displacement map from a pair of input images.METHODS: We trained a deep convolutional U-net architecture that was previously used to estimate optic flow between moving images to learn to predict the distortion map from an input pair of distorted EPI acquisitions. During the training step, the network minimizes a loss function (similarity metric) that is calculated from corrected input image pairs. This approach does not require the explicit knowledge of the ground truth distortion map, which is difficult to get for real life data.RESULTS: We used data from a total of Ntrain=22 healthy subjects to train our network. A separate dataset of Ntest=12 patients including some with abnormal findings and unseen acquisition modes, e.g. LR-encoding, coronal orientation) was reserved for testing and evaluation purposes. We compared our results to FSL's topup function with default parameters that served as the gold standard. We found that our approach results in a correction accuracy that is virtually identical to the optimum found by an iterative search, but with reduced computational time.CONCLUSION: By using a deep convolutional network, we can reduce the processing time to a few seconds per volume, which is significantly faster than iterative approaches like FSL's topup which takes around 10min on the same machine (but using only 1 CPU). This facilitates the use of a blip up/down scheme for all diffusion-weighted acquisitions and potential real-time EPI distortion correction without sacrificing accuracy.

    View details for DOI 10.1016/j.neuroimage.2020.116886

    View details for PubMedID 32389728

  • Fluid-structure interaction simulations of patient-specific aortic dissection. Biomechanics and modeling in mechanobiology Baumler, K., Vedula, V., Sailer, A. M., Seo, J., Chiu, P., Mistelbauer, G., Chan, F. P., Fischbein, M. P., Marsden, A. L., Fleischmann, D. 2020


    Credible computational fluid dynamic (CFD) simulations of aortic dissection are challenging, because the defining parallel flow channels-the true and the false lumen-are separated from each other by a more or less mobile dissection membrane, which is made up of a delaminated portion of the elastic aortic wall. We present a comprehensive numerical framework for CFD simulations of aortic dissection, which captures the complex interplay between physiologic deformation, flow, pressures, and time-averaged wall shear stress (TAWSS) in a patient-specific model. Our numerical model includes (1) two-way fluid-structure interaction (FSI) to describe the dynamic deformation of the vessel wall and dissection flap; (2) prestress and (3) external tissue support of the structural domain to avoid unphysiologic dilation of the aortic wall and stretching of the dissection flap; (4) tethering of the aorta by intercostal and lumbar arteries to restrict translatory motion of the aorta; and a (5) independently defined elastic modulus for the dissection flap and the outer vessel wall to account for their different material properties. The patient-specific aortic geometry is derived from computed tomography angiography (CTA). Three-dimensional phase contrast magnetic resonance imaging (4D flow MRI) and the patient's blood pressure are used to inform physiologically realistic, patient-specific boundary conditions. Our simulations closely capture the cyclical deformation of the dissection membrane, with flow simulations in good agreement with 4D flow MRI. We demonstrate that decreasing flap stiffness from [Formula: see text] to [Formula: see text] kPa (a) increases the displacement of the dissection flap from 1.4 to 13.4 mm, (b) decreases the surface area of TAWSS by a factor of 2.3, (c) decreases the mean pressure difference between true lumen and false lumen by a factor of 0.63, and (d) decreases the true lumen flow rate by up to 20% in the abdominal aorta. We conclude that the mobility of the dissection flap substantially influences local hemodynamics and therefore needs to be accounted for in patient-specific simulations of aortic dissection. Further research to accurately measure flap stiffness and its local variations could help advance future CFD applications.

    View details for DOI 10.1007/s10237-020-01294-8

    View details for PubMedID 31993829

  • Computed Tomography Imaging Features in Acute Uncomplicated Stanford Type-B Aortic Dissection Predict Late Adverse Events CIRCULATION-CARDIOVASCULAR IMAGING Sailer, A. M., Van Kuijk, S. M., Nelemans, P. J., Chin, A. S., Kino, A., Huininga, M., Schmidt, J., Mistelbauer, G., Baeumler, K., Chiu, P., Fischbein, M. P., Dake, M. D., Miller, D. C., Schurink, G. W., Fleischmann, D. 2017; 10 (4)


    Medical treatment of initially uncomplicated acute Stanford type-B aortic dissection is associated with a high rate of late adverse events. Identification of individuals who potentially benefit from preventive endografting is highly desirable.The association of computed tomography imaging features with late adverse events was retrospectively assessed in 83 patients with acute uncomplicated Stanford type-B aortic dissection, followed over a median of 850 (interquartile range 247-1824) days. Adverse events were defined as fatal or nonfatal aortic rupture, rapid aortic growth (>10 mm/y), aneurysm formation (≥6 cm), organ or limb ischemia, or new uncontrollable hypertension or pain. Five significant predictors were identified using multivariable Cox regression analysis: connective tissue disease (hazard ratio [HR] 2.94, 95% confidence interval [CI]: 1.29-6.72; P=0.01), circumferential extent of false lumen in angular degrees (HR 1.03 per degree, 95% CI: 1.01-1.04, P=0.003), maximum aortic diameter (HR 1.10 per mm, 95% CI: 1.02-1.18, P=0.015), false lumen outflow (HR 0.999 per mL/min, 95% CI: 0.998-1.000; P=0.055), and number of intercostal arteries (HR 0.89 per n, 95% CI: 0.80-0.98; P=0.024). A prediction model was constructed to calculate patient specific risk at 1, 2, and 5 years and to stratify patients into high-, intermediate-, and low-risk groups. The model was internally validated by bootstrapping and showed good discriminatory ability with an optimism-corrected C statistic of 70.1%.Computed tomography imaging-based morphological features combined into a prediction model may be able to identify patients at high risk for late adverse events after an initially uncomplicated type-B aortic dissection.

    View details for DOI 10.1161/CIRCIMAGING.116.005709

    View details for PubMedID 28360261