qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

Abstract

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

Vendor‐neutral sequences and fully transparent workflows improve inter‐vendor reproducibility of quantitative MRI

“…In doing so, we adapted a system that is primarily geared toward real‐time imaging (RTHawk) to perform quantitative MRI measurements. For example, absolute gradient limits have been allowed to achieve higher spoiling gradient moments and string‐valued customized metadata injection has been enabled to follow community data standards 41–43 …”

“…This key improvement addresses problem 1, as stated in the Introduction, with open-source pulse sequence descriptions. The developed sequence can be run on most GE and Siemens scanners through RTHawk software and an additional UUI that allows users to prescribe customized file naming entities for exporting reconstructed images in the BIDS 42,43 and k-space data in the ISMRM-RD format. 41 Conforming with community data standards, providing a user-friendly solution with a simplified vendor-neutral deployment, this work offers a complete solution for problem 2, and shows a way forward for the standardization of qMRI.…”

“…All the metadata annotations, accumulation logic of the collected data and naming of the exported images were designed according to the community data standards: ISMRM-RD 41 for the k-space data and the Brain Imaging Data Structure (BIDS) for the reconstructed images. 42,43…”

“…For example, absolute gradient limits have been allowed to achieve higher spoiling gradient moments and string-valued customized metadata injection has been enabled to follow community data standards. [41][42][43] Third, we created a fully transparent, container-mediated, and data-driven workflow 51,52 that automates the processing and reduces variability introduced by the operators. By design, the workflow operates according to the BIDS qMRI standard 43 for picking up all the necessary data and metadata, and generates outputs following a consistent derivative hierarchy.…”

“…[41][42][43] Third, we created a fully transparent, container-mediated, and data-driven workflow 51,52 that automates the processing and reduces variability introduced by the operators. By design, the workflow operates according to the BIDS qMRI standard 43 for picking up all the necessary data and metadata, and generates outputs following a consistent derivative hierarchy. Moreover, the raw data are exported in the ISMRM-RD format by our vendor-neutral sequence, allowing the use of community developed reconstruction tools by simply adding another container at the beginning of our modular workflow.…”