Artificial intelligence and machine learning in aortic disease.
Current opinion in cardiology
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
On the impact of vessel wall stiffness on quantitative flow dynamics in a synthetic model of the thoracic aorta.
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 Flow-Net for EPI Distortion Estimation.
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
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
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