An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data

AbstractLarge-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition.

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An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data

Frontiers in Neuroinformatics ◽

10.3389/fninf.2020.00005 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 2

Author(s):

Horea-Ioan Ioanas ◽

Markus Marks ◽

Clément M. Garin ◽

Marc Dhenain ◽

Mehmet Fatih Yanik ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Open Source ◽

Small Animal ◽

Imaging Data ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Data

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

10.1101/2021.10.22.21265382 ◽

2021 ◽

Author(s):

Agah Karakuzu ◽

Stefan Appelhoff ◽

Tibor Auer ◽

Mathieu Boudreau ◽

Franklin Feingold ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Data Structure ◽

Magnetic Resonance ◽

Brain Imaging ◽

Common Ground ◽

Quantitative Mri ◽

Imaging Data ◽

Resonance Imaging ◽

Brain Imaging Data ◽

The Brain

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 which makes multicenter dissemination of interoperable data possible. As a result, 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. Finally, our BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the practical barriers to standardization that is currently lacking in the field of neuroimaging.

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A device for the controlled cooling and freezing of excised plant specimens during magnetic resonance imaging

Plant Methods ◽

10.1186/s13007-021-00743-4 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Camilo Villouta ◽

Benjamin L. Cox ◽

Beth Rauch ◽

Beth Ann A. Workmaster ◽

Kevin W. Eliceiri ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Open Source ◽

Cold Hardiness ◽

Plant Tissues ◽

Imaging Data ◽

Resonance Imaging ◽

Controlled Cooling ◽

Wide Range ◽

Freezing Temperatures

Abstract Background Investigating plant mechanisms to tolerate freezing temperatures is critical to developing crops with superior cold hardiness. However, the lack of imaging methods that allow the visualization of freezing events in complex plant tissues remains a key limitation. Magnetic resonance imaging (MRI) has been successfully used to study many different plant models, including the study of in vivo changes during freezing. However, despite its benefits and past successes, the use of MRI in plant sciences remains low, likely due to limited access, high costs, and associated engineering challenges, such as keeping samples frozen for cold hardiness studies. To address this latter need, a novel device for keeping plant specimens at freezing temperatures during MRI is described. Results The device consists of commercial and custom parts. All custom parts were 3D printed and made available as open source to increase accessibility to research groups who wish to reproduce or iterate on this work. Calibration tests documented that, upon temperature equilibration for a given experimental temperature, conditions between the circulating coolant bath and inside the device seated within the bore of the magnet varied by less than 0.1 °C. The device was tested on plant material by imaging buds from Vaccinium macrocarpon in a small animal MRI system, at four temperatures, 20 °C, − 7 °C, − 14 °C, and − 21 °C. Results were compared to those obtained by independent controlled freezing test (CFT) evaluations. Non-damaging freezing events in inner bud structures were detected from the imaging data collected using this device, phenomena that are undetectable using CFT. Conclusions The use of this novel cooling and freezing device in conjunction with MRI facilitated the detection of freezing events in intact plant tissues through the observation of the presence and absence of water in liquid state. The device represents an important addition to plant imaging tools currently available to researchers. Furthermore, its open-source and customizable design ensures that it will be accessible to a wide range of researchers and applications.

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