Early clinical outcomes and molecular smooth muscle cell phenotyping using a prophylactic aortic arch replacement strategy in Loeys-Dietz syndrome.

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

Artificial intelligence and machine learning in aortic disease.

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.

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 â€‹= â€‹22 healthy subjects to train our network. A separate dataset of Ntest â€‹= â€‹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.