XNAT Workshop Poster Submissions
As noted in the Workshop Agenda, we will be offering a Virtual Poster Session to close out the 2021 XNAT Workshop. We'll be using the collaborative/social platform Spatial Chat to host this session and we encourage users in the XNAT community to submit poster abstracts for display at the 2021 XNAT Workshop. This is intended to be an informal way for people to share their experiences with XNAT and the "posters" could take many forms, from a traditional scientific poster, to a video, to a photo of that napkin where you sketched out an amazing new idea for using XNAT.
When are Posters Presented?
The XNAT Poster Session will begin officially on Tuesday, Sept 14, 2021, at 11:15 am CDT, and will last for an hour and a half. Each poster will be staffed by its presenter for 30 minutes, following the schedule as defined below.
How Will This Work?
Spatial Chat allows us to create virtual "Rooms" where multiple poster presentations can be housed. We have created multiple rooms, where posters can be organized by topic area. Attendees will be able to move from poster to poster, and engage in conversations with poster authors in a free-flowing way. Additionally, we have created social breakout rooms for off-topic conversations and general catching up with colleagues from the XNAT diaspora.
See: How To Use Spatial Chat in the XNAT Workshop
Poster Topics and Rooms
Thanks to all who submitted a poster topic! We are looking forward to a lively discussion. Here are the posters that will be presented, organized by "Room" where each room represents a common theme.
Room 1: XNAT Integrations
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center | Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer’s disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets. | Greg Operto | BarcelonaBeta Brain Research Center |
11:45 - 12:15 | Development of an Integrated and Automated Image Analysis Pipeline for MR Scanner Acceptance Testing | An automated pipeline for image quality analysis and reporting was developed using Python for the MagNET protocol, used in the acceptance test of MRI scanners. The analysis pipeline was packaged as a Container using Docker and integrated with the informatics platform, XNAT. This poster includes presentation of the preliminary use of the integrated framework, demonstrating its use within the MRI community and provides a discussion for how the tool could be deployed across different MRI sites to standardise image analysis. | Emma Doran | NHS Greater Glasgow & Clyde |
12:15 - 12:45 | Design and Implementation of the DICOM Web API in XNAT | The DICOM Web RESTful API defined by the DICOM Committee is designed to support clinical workstations in the context of communicating with a PACS and single copies of DICOM study, series and instance objects. We have extended the XNAT API to support requests from third party applications and future versions of the XNAT OHIF viewer plugin. We believe that the extensions for clinical workstations will support researchers performing analysis on their existing clinical workstations in an environment that they use on a regular basis. This poster describes some of the challenges posed by XNAT's flexible data model and design choices made by the XNAT team. | Dave Maffitt | CIL Lab, WUSTL |
12:15 - 12:45 | Integrating Clara Workflow in XNAT | XNAT and MIRRIR are powerful platforms for research that organize imaging data into one central location for easy processing. Recently they have become bolstered with the tools that developers have started integrating with the platforms. One such integration is Nvidia’s CLARA application for machine learning research, which has become the basis for designing pipelines for data processing. These integrations allow users to easily build models for everything from AI assisted segmentation of organs to prediction of COVID within the CT scans of patients. Here we present once such pipeline that was developed for AI based segmentation of lungs and then subsequent Classification of that segmentation along with the corresponding CT scan with a covid classifier; however, this workflow can be adapted to most workflows using some user-built containers. | Yash Thacker | CIL Lab, WUSTL |
Room 2: Research Driven Use Cases
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | Automating Neuroimaging Workflow Using XNAT to Facilitate Translational Neuroscientific Research | Advances in neuroimaging research have allowed neuroscientists to collect large amounts of data on the human brain, greatly expanding our understanding of the its structure and functions. However, such an enormous quantity of data becomes cumbersome to work with even for seasoned neuroimaging specialists and is subject to being sequestered in data silos. Though improvements have been made in the storage of neuroimaging data via community driven efforts like the Brain Imaging Data Structure (BIDS) specification, working with neuroimaging data still retains a high barrier of entry for translational researchers without years of experience. | Alexander Bartnik | University at Buffalo, Buffalo Neuroimaging Analysis Center |
11:45 - 12:15 | MIRRIR: An XNAT Use Case | The MIR Research Imaging Repository exists to make massive quantities of clinical imaging data available to imaging research workflows. Its first chartered goal was to import 500,000 anonymized mammogram studies from a connected clinical PACS, and support searchability across this massive dataset. This has forced a reckoning on both the front end and back end of XNAT to support such a large collection of data, as well as intensive coordination with PACS administrators and on-campus Dev Ops engineers to support the transport and protection of this amount of data. This poster will present an overview of the MIRRIR project including implementation, cohort identification, data ingestion and storage, key plugins, key projects, and future plans. | Michael Hileman | CIL Lab, WUSTL |
12:15 - 12:45 | TIP: the Translational Imaging Portal | TIP is a heavily customized version of XNAT that was created to facilitate the translation of imaging research into clinical practice. The implementation of TIP has focused on the translation of resting-state fMRI research and image processing workflows into patient diagnoses and neurosurgical planning. This XNAT implementation was the first to interface directly with a PACS system for the purpose of querying and pulling patient records, and featured the first initial build of the DICOM Query-Retrieve plugin. It is also a heavily secured system that is considered part of the institutional network of patient data. | Pamela LaMontagne | CIL Lab, WUSTL |
12:15 - 12:45 | PIXI: Preclinical Imaging XNAT-enabled Informatics | In this session we report on efforts to develop an open-source preclinical imaging informatics platform, PIXI, to manage the workflows of preclinical image data acquisition, capture imaging-associated experiments including metadata and annotations, and to implement analytic pipelines in a unified environment. The XNAT system at the core of PIXI is being extended to track animals as subjects, track PDX acquisitions and correlated tumors, and import hotel-based imaging sessions from preclinical scanners and split those sessions into multiple data objects for longitudinal tracking of individual subjects. XNAT’s Restful API will also be extended with PIXI-specific API endpoints, which will then enable the development of an ecosystem of PIXI applications for tablet devices, as well as for Python scripting environments such as Jupyter Notebooks. PIXI will also come equipped with containerized preclinical analytic pipelines built around XNAT Container Service enabling a portable, sharable, and fully reproducible data processing environment. | Andrew Lassiter | CIL Lab, WUSTL |
Room 3: XNAT Deployments
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | WU Infrastructure for Supporting Production XNATs | This poster will describe some of the dev ops challenges to be met when deploying and managing large scale XNAT installations in a production environment. We will describe approaches and solutions used by the XNAT team at Washington University. | Chip Schweiss | CIL Lab, WUSTL |
11:45 - 12:15 | Centralising XNAT provision at UCL | UCL has had a long history of using XNAT to support a variety of initiatives across the institution - however until recently these have all been deployed and supported by individual project teams. We will describe a collaborative effort between the Centre for Medical Image Computing and the Centre for Advanced Research Computing at UCL to implement centrally maintained research medical imaging infrastructure with XNAT at its core. | Haroon Chughtai | University College London |
(Withdrawn) | XNAT requires a fairly complex architecture to run out of the box. This poster will show the architecture and Infrastructure as Code deployment package required to run XNAT on AWS | Gang Fu | Amazon | |
12:15 - 12:45 | DASHER: Data Anonymisation and Synchronisation in HEalthcare Research | A major challenge for large multi-site clinical projects is to ensure that staff at local sites are able to efficiently and consistently manage and transfer medical data to remote central repositories while ensuring the data does not contain identifiable private health information (PHI). DASHER is an XNAT-based system for sites to pseudonymise imaging data for both clinical trials and general research and ensuring that this data conforms to clinical trial protocols and is fully pseudonymised before transferring off-site. DASHER consists of two XNATs running within the same Docker service, one storing non-anonymised data, the other pseudonymised data. DASHER has recently been updated to XNAT 1.8, improving the anonymisation and security and simplifying the installation process considerably. | Daniel Beasley | King's College London |
Room 4: Findable, Accessible, Interoperable, Reusable (FAIR)
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | Australian Imaging Service | Building a secure, nationally federated imaging platform on kubernetes with XNAT and related technologies. | Ryan Sullivan | The University of Sydney |
11:45 - 12:15 | XNAT in the NeuroBridge Project | Replication, mega analysis, and meta analysis are critical to the advancement of neuroimaging research. Harnessing already-collected data for replication purposes efficiently and rigorously will enable and support the reuse of critical neuroscience data sets. While large amounts of data exist through many different neuroimaging databases, these databases usually do not communicate with each other. Moreover, larger amounts of neuroimaging data are collected in hundreds of laboratories each year but not archived in databases, though many of these datasets are described in journal publications. Researchers need the ability to select the most useful datasets from among those available, since too much data can be as problematic as too little. A critical gap therefore exists in finding and getting enough data of the right kind to the scientist. Our proposed NeuroBridge will address this gap by performing text mining of open source journal databases and searches of collaborating XNAT instances for archived data sets. This poster describes the planned extensions to the XNAT platform to support these summary queries intended to allow researchers to identify appropriate data sets without requiring access to low level and possibly sensitive detailed information. Once data sets are identified, a user can make a formal request to the owner of the data set with appropriate safeguards and data use agreements in place. | Stephen Moore | CIL Lab, WUSTL |
12:15 - 12:45 | A Platform for Cohort Discovery for Integrating and Curating Clinical and Imaging Data for Precision Health | We developed a platform at UCLA for cohort discovery that uses XNAT for curating imaging data, automating executions of cancer detection software via pipelines, and storing computational results with extended data model(s). | Bing Zhu | University of California: Los Angeles |
12:15 - 12:45 | Federated XNATs in NCITA | The National Cancer Imaging Translational Accelerator (NCITA) is a UK collaboration of nine academic centres in the UK that all specialise in advanced imaging in cancer. NCITA is based around three "Units" (Repository, QA/QC and Clinical Trials). The NCITA Repository Unit is developing a federated XNAT framework in which data can be curated within institutions (which retain their own XNAT administrators and security procedures) but will allow data to be shared and cohorts to be built across institutions in a way that is individualised for each user, depending on the the permissions granted to them by these XNAT admins. A user pool has been created using AWS Cognito and we are creating an XNAT plugin that will allow single sign-on to all participating XNAT instances, without any central application requiring individual password details from those systems. | Chris Rookyard | Institute for Cancer Research, London |
Room 5: Container Services
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | Incremental extraction of metadata from XNAT stored DICOM header to a FHIR Database in nationwide MIRACUM-project | XNAT is acting as central image storage for scientists. We deployed a freely configurable service in a docker container that is connecting to the XNAT API. The service consumes the information about the projects, subjects, experiments, and scans and retrieves the belonging DICOM dumps. These well-formatted DICOM metadata are mapped to the FHIR imaging study resource following an implementation guide developed for the nation-wide German MIRACUM project. The service processes the target resources incrementally and stores them in a central FHIR server. Within the FHIR server, the information about the imaging data is linked to already available patient information: demographics, available diagnosis, applied procedures, and general encounter data. | Jan Jörg Maluche | Otto-von-Guericke University Magdeburg |
11:45 - 12:15 | Predicting Mortality of COVID-19 Positive Patients in the ICU After 30 Days Using a Convolutional Neural Network with UNetArchitecture | Machine learning (ML) has become a very valuable tool in research, and with the rise of COVID-19 it has become very popular and powerful tool that is used to make predictions about a patient's health. However, a lot of current research focuses on using CT or MR scans as the basis of prediction were as much of the early data, we have is in the form of X-Ray radiographs of the chest. This is due to CT scans not being the standard practice for treating patients in the early months of the pandemic. This project has two goals, we wanted to see if we could design a model that would predict the mortality of a patient admitted to the ICU after 30 days and additionally, we wanted to examine the heatmaps of the model to see where it would focus in on within radiographs. This model could be useful when triaging of patients are needed if resources are low, and additionally our research gives us deeper insight into how ML models work. There are certain pre-existing conditions such as obesity, and asthma that put patients in the higher risk category, which can be gathered from clinical data, and we wanted to examine if there was any relationship between clinical variables and what can be observed from the chest radiograph of a patient. | Yash Thacker | CIL Lab, WUSTL |
12:15 - 12:45 | Data Processing with XNAT Container Service | Data processing with XNAT and Docker. Topics include: "Dockerizing" your process, preparing your container for working with the XNAT data model, process workflow using the XNAT UI. | Matt Kelsey | CIL Lab, WUSTL |
Room 6: Plugins and Frameworks
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | DICOM Query / Retrieve Plugin | The Dicom Query-Retrieve (DQR) plugin allows XNAT users to connect directly to a PACS or other DICOM Application Entity, send queries to find studies, and import them to their XNAT with custom relabeling applied en route. Users can also send image data from XNAT to the PACS. With these new capabilities, the DQR Plugin can significantly impact your ability to interact with clinical PACS data. It also offers fine-tuned administrative controls, to help XNAT administrators and PACS administrators set and enforce policies on things such as data transfer rate and availability. This poster will walk through the technical capabilities of the DQR plugin as well as the business rules and restrictions put in place to govern its usage. | Will Horton | CIL Lab, WUSTL |
11:45 - 12:15 | XNAT Platform and Plugin Framework Upgrades | Like any mature software system, the XNAT platform has legacy features that are used but are difficult to extend and maintain. This might be due to underlying libraries or other design choices. The XNAT core team is in the process of updating underlying libraries and our own software implementation to provide modern mechanisms for developers who want to build on the XNAT system. This poster describes updates and changes that will be useful for developers who are writing XNAT plugins as well as for users who invoke the XNAT RESTful API. | Rick Herrick | CIL Lab, WUSTL |
12:15 - 12:45 | Working Through Challenges of XNAT-Integrated App Development | UAR has been developing the XNAT Desktop Client, which is a deeply integrated application built in Electron that has involved the development of new core code, new protocols, imported DICOM anonymization libraries, and complementary plugins for managing site settings. This poster/presentation would highlight challenges and the means we took to overcome them. | Darko Ljubic | Up and Running Software |
12:15 - 12:45 | LDAP integration UI plugin | We'll describe the features of a UI plugin that we've developed to make it easier for our users to use LDAP authentication in our XNATs. | Matthew South | Oxford University |
Room 7: New Tools in the XNAT Ecosystem
| Staffing | Title | Description | Presenter | Institution |
|---|---|---|---|---|
11:45 - 12:15 | XNAT Rapid Reader Program | The XNAT Rapid Reader program is a new application that is designed to support radiologists and other imaging experts in the task of reading and scoring a set of common sessions for a research project. The application integrates the XNAT platform, OHIF viewer plugin and new software to provide an efficient, worklist driven environment for the reading task. When working in this mode, the imaging experts can focus on the task at hand with reduced user interface options to optimize their work. | Woonchan Cho | CIL Lab, WUSTL |
11:45 - 12:15 | XTOLM: an XNAT-aware Linux shell batch analysis environment | Many imaging data analysis scripts are developed in Linux shell. These scripts are often developed ad-hoc and are hard to reuse. Over the years, we isolated a set of best practices and standard routine tasks relevant to developing such analyses against XNAT-hosted data. We implemented those in an experimental bash wrapper environment called XTOLM. XTOLM offers developers several syntactical structures for iteration over spreadsheets, nested metadata management, and generation of results spreadsheets. XTOLM also wraps XNAT REST API serialization, allows working on spreadsheet row subranges and supports grid execution. | Mikhail Milchenko | CIL Lab, WUSTL |
12:15 - 12:45 | Pixel Anonymization and Bulk Uploading in the XNAT Desktop Client | The XNAT Desktop Client (XDC) is an Electron based, desktop application that was first release in January 2019. Version 2 was released in August 2020 and introduced pixel anonymization and a number of operational improvements. Version 3 of the XDC will be released in the fall of 2021. It includes two large feature changes:
This poster will walk through the usage of the XNAT Desktop Client, as well as shine a spotlight on changes in XNAT that it prompted. | Will Horton | CIL Lab, WUSTL |
Presenting and Data Format Guidelines
Posters should be educational in nature and not commercial, but if you represent a company that uses or is interested in XNAT, then we have the potential to create virtual "vendor booths" (see below). Posters should not contain proprietary information nor contain any sensitive data such as protected health information (PHI).
Posters represent "fixed" content that we will upload prior to the workshop, but the platform will also give you the opportunity to share "live" content directly with the people you are chatting to - see the presenting and data format guidelines here for more details.
During the meeting participants can share content with those around them in two main ways:
- static uploads: image, video (via YouTube, Vimeo or Twitch link), text/clickable link
- screen sharing of your entire screen, a window on your computer or a chrome tab
Guidelines on poster creation are being created and will be uploaded soon.
Screen sharing can only be done in an "Auditorium" setting, which requires a dedicated room in Spatial Chat. These will be reserved for only a few presenters. We strongly encourage attendees to create static posters or presentations if possible.
Submission Deadline
All poster submissions are due September 7, 2021 to give us time to build the virtual environment. Before placement in the poster area, your poster will be reviewed to ensure it meets the guidelines and you may be asked to revise your submission.