Chronic hypertension (HTN) affects more than 1 billion people worldwide, and is associated with an increased risk of cardiovascular disease. Despite decades of promising research, effective treatment of HTN remains challenging. This work investigates vagus nerve stimulation (VNS) as a novel, device-based therapy for HTN treatment, and specifically evaluates its effects on long-term survival and HTN-associated adverse effects. HTN was induced in Dahl salt-sensitive rats using a high-salt diet, and the rats were randomly divided into two groups: VNS (n = 9) and Sham (n = 8), which were implanted with functional or non-functional VNS stimulators, respectively. Acute and chronic effects of VNS therapy were evaluated through continuous monitoring of blood pressure (BP) and ECG via telemetry devices. Autonomic tone was quantified using heart rate (HR), HR variability (HRV) and baroreflex sensitivity (BRS) analysis. Structural cardiac changes were quantified through gross morphology and histology studies. VNS significantly improved the long-term survival of hypertensive rats, increasing median event-free survival by 78% in comparison to Sham rats. Acutely, VNS improved autonomic balance by significantly increasing HRV during stimulation, which may lead to beneficial chronic effects of VNS therapy. Chronic VNS therapy slowed the progression of HTN through an attenuation of SBP and by preserving HRV. Finally, VNS significantly altered cardiac structure, increasing heart weight, but did not alter the amount of fibrosis in the hypertensive hearts. These results suggest that VNS has the potential to improve outcomes in subjects with severe HTN.

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Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limited number of measurements, but these methods typically rely on the existence of a large, centralized database of fully sampled scans for training. In this work, we investigate federated learning for MRI reconstruction using end-to-end unrolled deep learning models as a means of training global models across multiple clients (data sites), while keeping individual scans local. We empirically identify a low-data regime across a large number of heterogeneous scans, where a small number of training samples per client are available and non-collaborative models lead to performance drops. In this regime, we investigate the performance of adaptive federated optimization algorithms as a function of client data distribution and communication budget. Experimental results show that adaptive optimization algorithms are well suited for the federated learning of unrolled models, even in a limited-data regime (50 slices per data site), and that client-sided personalization can improve reconstruction quality for clients that did not participate in training.

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using score-based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion.

PROPELLER based acquisitions have the unique ability to give low resolution images for each echo train acquired and are often used for motion correction. However, motion correction with PROPELLER can often be hindered due to the low resolution nature of each shot. We propose a technique which leverages recent advancements in super resolution neural networks to enhance low resolution PROPELLER shots for better inter-shot motion estimation.