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Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a “smart CT” paintbrush tool; the integration of NVIDIA’s Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.

An external driver-free MRI method for assessment of liver fibrosis offers a promising noninvasive tool for diagnosis and monitoring of liver disease. Lately, the heart's intrinsic motion and MR tagging have been utilized for the quantification of liver strain. However, MR tagging requires multiple breath-hold acquisitions and substantial postprocessing. In this study, we propose the use of a fast strain-encoded (FSENC) MRI method to measure the peak strain (SPhantom and in vivo experiments (11 healthy subjects and 11 patients with liver fibrosis) were conducted. Reproducibility experiments were performed in seven healthy subjects.Peak liver strain, SAccelerated external driver-free noninvasive assessment of left liver lobe strain and SWV is feasible using strain-encoded MRI. The two measures significantly separate healthy subjects from patients with fibrotic liver. Magn Reson Med 74:106-114, 2015. © 2014 Wiley Periodicals, Inc.

ObjectivesWe sought to define age-related geometric changes of the aortic arch and determine their relationship to central aortic stiffness and left ventricular (LV) remodeling.BackgroundThe proximal aorta has been shown to thicken, enlarge in diameter, and lengthen with aging in humans. However, no systematic study has described age-related longitudinal and transversal remodeling of the aortic arch and their relationship with LV mass and remodeling.MethodsWe studied 100 subjects (55 women, 45 men, average age 46±16 years) free of overt cardiovascular disease using magnetic resonance imaging to determine aortic arch geometry (length, diameters, height, width, and curvature), aortic arch function (local aortic distensibility and arch pulse wave velocity [PWV]), and LV volumes and mass. Radial tonometry was used to calculate central blood pressure.ResultsAortic diameters and arch length increased significantly with age. The ascending aorta length increased most, with age leading to aortic arch widening and decreased curvature. These geometric changes of the aortic arch were significantly related to decreased ascending aortic distensibility, increased aortic arch PWV (p < 0.001), and increased central blood pressures (p < 0.001). Increased ascending aortic diameter, lengthening, and decreased curvature of the aortic arch (unfolding) were all significantly associated with increased LV mass and concentric remodeling independently of age, sex, body size, and central blood pressure (p < 0.01).ConclusionsAge-related unfolding of the aortic arch is related to increased proximal aortic stiffness in individuals without cardiovascular disease and associated with increased LV mass and mass-to-volume ratio independent of age, body size, central pressure, and cardiovascular risk factors.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.