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Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality. 

This paper presents Ombro, a low-level virtual instruction set architecture (vISA) which enforces compiler-based security policies on real-world commodity hypervisors. We extend the Secure Virtual Architecture (which itself extends the LLVM compiler’s Intermediate Representation) to support the full set of hardware operations needed to run an x86 commodity hypervisor used in some of the world’s largest public clouds, namely, the Xen 4.12 hypervisor, running in full hardware-accelerated mode using Intel’s Virtual Machine Extensions (VMX). We have ported Xen 4.12 to the Ombro vISA and demonstrated that it can run unmodified guest VMs of real-world relevance (namely, Linux guests under Xen’s HVM and PVH modes). Furthermore, to demonstrate Ombro’s ability to harden hypervisors from attack, Ombro implements control flow integrity and the first protected shadow (split) stack for x86 hypervisors. Our performance results show that Ombro achieves this protection without imposing measurable overheads on most application benchmarks.