Articles

By Pablo Velasco, Senior Scientific Solutions Engineer at Flywheel

In neurology, medical imaging data provides invaluable insights into brain structure and function. But neuroimaging researchers face challenges in discovering, aggregating, standardizing and analyzing that data efficiently. Here, we detail some of the top challenges in neurology research regarding imaging data management—and how researchers can solve them to enable new breakthroughs in brain sciences. 

Scalability and Resource Demands 

One of the most pressing challenges in neuroimaging is the ability to process and analyze large volumes of data efficiently. The complexity of brain tissue segmentation means the work is labor-intensive, time-consuming and requires high computational power. This scalability issue can overwhelm existing infrastructure, leading to delayed research timelines and increased costs. 

Modern medical data management platforms can offer a solution by leveraging cloud-based resources that can scale as needed. By seamlessly integrating cloud infrastructure, a cloud-based platform can easily accommodate fluctuating computational and storage needs without the need for costly servers that may end up becoming outdated and may not be fully utilized when your needs decrease. 

Data Standardization and Organization 

Neuroimaging data often originates from a variety of scanners and institutions, resulting in disparate file formats and organizational structures. For researchers to effectively utilize applications for image processing and analysis, data must be standardized.  

The Brain Imaging Data Structure (BIDS) provides a framework for organizing and describing MRI datasets. But conversion can introduce challenges around file organization and metadata extraction. 

Data management platforms can automate the conversion process, transforming heterogeneously organized files into standardized formats like BIDS. This automatic transformation enhances interoperability and ensures that researchers can focus on analysis rather than data wrangling. 

Running Algorithms at Scale 

Another challenge lies in training and deploying complex algorithms across vast data sets. Effectively training algorithms requires a large number of images, while algorithms for complex tasks such as tissue segmentation may require substantial processing power. 

A scalable platform that has machine learning algorithms built into it allows researchers to deploy exiting algorithms across extensive datasets smoothly and gather the necessary data to train new algorithms. Using and building containerized algorithms can help accelerate analytical processes, freeing up time for deeper investigations. 

Data Reuse and Ethical Management 

Even when data is gathered, cleaned and otherwise ready to use, ethical considerations remain. For instance, data acquired during routine hospital visits often cannot be used for research due to protections around protected health information (PHI). Yet, these datasets are invaluable for developing insights into conditions such as brain tumors, Alzheimer's disease, stroke, epilepsy and multiple sclerosis. 

Data management platforms cater to this need by implementing de-identification workflows and secure data containers, ensuring privacy is maintained while enabling data reuse. Researchers can ethically harness these datasets, broadening the scope of their studies without compromising patient confidentiality. 

Ensuring Data Provenance and Compliance 

Ensuring data compliance and traceability is crucial in neuroimaging research. Organizations need to track every access and modification made to data in order to maintain accountability, transparency and reproducibility of research, but this often imposes manual, error-prone or disconnected solutions. 

Using a data management platform with built-in security features, such as access controls, as well as audit logs, can help you protect data integrity, confidentiality and auditability. These measures help researchers stay compliant with regulatory requirements such as HIPAA, GDPR and 21 CFR Part 11. 

How Flywheel Addresses These Challenges 

The Flywheel medical imaging data management platform provides the solutions neuroimaging professionals need to overcome these challenges effectively. 

Automated Data Conversion 

Flywheel has more than 50 built-in Gears specific to neurology, helping researchers automate tasks such as data conversion into BIDS. From ingestion to conversion via DICOM to NIfTI Gears, Flywheel ensures data is well-organized and ready for advanced analysis, saving valuable time and resources. 

Cloud-Based Scalability 

Leveraging cloud infrastructure, Flywheel helps research institutions scale computational resources as needed. Neuroimaging professionals can seamlessly transition their extensive data sets onto cloud or hybrid environments, allowing for scalable storage and efficient processing.  

De-identification Workflows 

Flywheel’s de-identification workflows enable hospitals to anonymize PHI contained in images, rendering them usable for research purposes. The platform not only removes header information but also addresses PHI within images themselves, making data ethically permissible for further study.  

Data Security Features 

Flywheel offers secure data management with built-in role-based permissions, access-controlled projects and the option to build audit trails. 

Learn More 

You can find out more about our neurology-specific features here. Have questions? Reach and out and schedule a demo to learn how your organization can maximize its imaging data with Flywheel.