How Modern Imaging Data Management Can Help Overcome Challenges in Academic Research
From oncology to ophthalmology, imaging is fueling breakthroughs in modern medical research. But advances in resolution and modality have generated enormous, complex datasets that are difficult to manage. In addition, many academic institutions remain constrained by fragmented infrastructure, where data is separated along department lines and scattered amongst personal drives, servers and PACS systems that may or may not be up to date.
Keeping imaging data siloed may seem preferrable in some ways, but ultimately, it can slow collaboration and discoveries while inflating storage costs, without necessarily offering better cybersecurity. Ideally, academic medical institutions could get the best of all worlds: Increasing data access and usability without sacrificing security or ownership.
Modern imaging data management platforms can help alleviate these issues by integrating imaging data from multiple sources and increasing access. A successful platform should be able to accomplish this while maintaining (and even improving) the security necessary to protect personal health information (PHI). Below, we explore some of the challenges of imaging data management and solutions that a modern imaging data management platform can help provide.
3 Challenges to Efficient Imaging Data Management in Academia
Data Size and Complexity
As imaging datasets balloon in size and complexity, researchers face mounting logistical hurdles. Each instrument may output data in different formats — DICOM, TIFF and NIfTI, to name a few — making integration across labs and sites difficult.
Storage infrastructure often lags behind storage needs, as high-resolution imaging projects can generate dozens of terabytes per year — data that needs to be preserved for years to come, per national regulations. This can cause storage costs to grow exponentially, especially with on-premises servers.
Data Silos
When imaging data is isolated within departments, collaboration suffers. Cross-institutional projects are hampered by the absence of standardized metadata and harmonized pipelines. And without clear provenance tracking, reproducing published analyses becomes nearly impossible.
The National Institutes of Health and UNESCO have both emphasized that scientific progress depends on the FAIR data principles: making data Findable, Accessible, Interoperable and Reusable. But implementing those principles requires centralized infrastructure and well-defined governance — something many academic medical centers still struggle with. In fact, only 20% of healthcare leaders surveyed fully trust their data.
Data Security and Provenance
Researchers must protect sensitive patient data by implementing strong administrative and audit safeguards. It’s also critical to capture full data provenance, or a complete record of how imaging datasets were ingested, processed, manipulated and shared. Provenance enables reproducible medical research and builds a reliable foundation for downstream workflows (including AI).
However, many legacy systems like PACS lack adequate security features. And implementing standardized data provenance protocols so data history is fully captured at time of ingest is challenging due to the sheer volume of data and the time it takes to manually enter metadata.
3 Ways a Modern Imaging Data Management Platform Can Help
Enabling Secure Cross-Disciplinary Collaboration
Centralized imaging repositories open the door to collaboration across disciplines and dispersed locations as well as to multimodal research. For example, in neuroscience, discoveries across diverse patient populations across continents have helped with crucial work in pediatric neurodevelopment. These efforts wouldn't be able to be achieved as cleanly or as efficiently without a central imaging data platform.
However, due to strict regulations around patient data like HIPAA and GDPR, this central platform needs to have built-in security features. For example, encryption of data both at-rest and in-transit ensures that even if storage or network systems are compromised, unauthorized parties can't get to PHI. Role-based access controls and multi-factor authentication (MFA) further round out a secure system.
Standardization and Automation
An imaging data management platform that includes automated workflows and containerized algorithms can help organizations easily accomplish crucial but time-consuming tasks such as standardization to formats like BIDS and DICOM and de-identifying PHI. It can also help ensure research is sound and reproducible by capturing complete provenance at ingestion, enforcing consistent metadata standards and integrating automated quality control.
Future-Proofing Research for AI and Beyond
As machine learning and AI become central to scientific discovery, imaging data infrastructure must evolve from passive storage to active enablement. Scalable, cloud-based systems capable of storing large amounts of diverse data, orchestrating containerized workflows, tracking data versions and integrating multimodal pipelines are essential to AI and ML development. Without that foundation, organizations likely won’t have enough data to develop and test models effectively or efficiently.
How Flywheel Helps
This is where an advanced imaging data management platform like Flywheel enters the picture. Designed specifically for imaging-based research, Flywheel provides academic medical centers a unified, cloud-native platform with tools to securely aggregate, annotate, curate and analyze large amounts of imaging data, either within your organization or with collaborators across the world.
It automatically captures provenance and metadata, supports multiple imaging modalities and integrates containerized workflows for reproducible analysis. By aligning with FAIR principles and offering built-in quality control, access management and audit trail capabilities, Flywheel helps academic institutions move beyond fragmented systems toward truly scalable research.
Across leading universities, from the University of Wisconsin to Stanford, Flywheel has been used to standardize imaging processing pipelines, harmonize multisite datasets, and prepare data for AI development and testing. It helps these institutions make the most of shared resources and reduce administrative overhead without burdening IT or stripping researchers of necessary tools. With the ability to connect to PACS and other systems for hybrid storage and compliance features aligned to HIPAA and GDPR standards, Flywheel is helping academic researchers and institutions break down data silos, future-proof their work for AI and maximize the value of their imaging data.
To see a demo and learn more about how Flywheel supports academic research, get in touch.
