Citations 2
Characterization of Portable Ultra‐Low Field MRI Scanners for Multi‐Center Structural Neuroimaging
Ljungberg, Emil , Padormo, Francesco , Poorman, Megan , Clemensson, Petter , Bourke, Niall , Evans, John , Gholam, James , Vavasour, Irene M. , Kolind, Shannon , Lafayette, Samson Lecurieux, Bennallick, Carly , Donald, Kirsten A. , Bradford, Layla E. , Lena, Beatrice , Vokhiwa, Maclean , Shama, Talat , Sekoli, Lydia, Rensburg, Janse van , Madhwani, Akber, Banda, Frank A., Mwila, Mwila L., Cassidy, Adam R. , Moabi, Kebaiphe, Sephi, Dolly, Boakye, Richard A. , Hollander, W. den, Karaulanov, Todor , Williams, Steven , Deoni, Sean , Evans, John C , Vavasour, Irene , Kollind, Shannon H , Pepper, Michael S , Cassidy, Adam R , Boakye, Richard A , Ae-Ngibise, Kenneth A , Asante, Kwaku P , Ljungberg, Emil , Bradford, Layla E. , Siew, Jasmine , Khan, Amna , Banda, Frank A., Hollander, William J.
Human Brain Mapping | May 23, 2025 | Volume: 46 | Issue: 8 | Pages: No page available
ABSTRACT The lower infrastructure requirements of portable ultra‐low field MRI (ULF‐MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF‐MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF‐MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open‐source analysis tools, we quantify signal‐to‐noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF‐MRI systems can be deployed in a variety of environments for multi‐center neuroimaging studies and produce robust results.
The past, present, and future of the brain imaging data structure (BIDS)
Markiewicz, Christopher J. , Bansal, Shashank , Bénar, Christian , Bertazzoli, Giacomo , Bhogawar, Suyash , Blair, Ross , Brooks, Teon L , Calhoun, Vince D. , Castelli, Filippo Maria , D’Ambrosio, Sasha , Hollander, Gilles de , Vega, Alejandro de la , Delorme, Arnaud , Earl, Eric , Feingold, Franklin , Flandin, Guillaume, Galassi, Anthony , Gallitto, Giuseppe , Ganz, Melanie , Gholam, James , Ghosh, Satrajit , Guidali, Giacomo , Handwerker, Daniel A. , Hardcastle, Nell , Innis, Robert B. , Ioanas, Horea-Ioan , Jahn, Andrew , Karakuzu, Agâh , Kincses, Bálint , Lau, Jonathan C. , Li, Xiangrui , Meisler, Steven L. , Mikkelsen, Mark E. , Nichols, Thomas E. , Ort, Eduard , Park, Patrick J., Pawlik, Mateusz , Pestilli, Franco , Pollonini, Luca , Rizzo, Gaia , Robbins, Kay A. , Rockhill, Alexander P. , Rogers, Christine , Rokem, Ariel , Rorden, Chris , Routier, Alexandre , Saborit-Torres, Jose Manuel , Schirner, Michael , Smith, Robert E. , Sprenger, Julia , Szinte, Martin , Thomas, Adam G. , Torabian, Sajjad, Voytek, Bradley , Calhoun, Vince D. , Poldrack, Russell A. , Appelhoff, Stefan , Ashar, Yoni K. , Auer, Tibor , Baillet, Sylvain , Beltrachini, Leandro , Bortoletto, Marta , Boudreau, Mathieu , Clement, Patricia , Cohen, Alexander L. , Cohen-Adad, Julien , Iglesia-Vayá, María de la , Devinsky, Orrin , Draschkow, Dejan , Duff, Eugene Paul , DuPré, Elizabeth , Estéban, Oscar , Gau, Rémi , Ghosh, Satrajit S , Giacomel, Alessio , Gillman, Ashley , Gleeson, Padraig , Gramfort, Alexandre , Guay, Samuel , Halchenko, Yaroslav O. , Herholz, Peer , Hermes, Dora , Honey, Christopher John , Keator, David B. , Kiar, Gregory , Laird, Angela R. , Lazari, Alberto , Legarreta, Jon Haitz , Li, Adam , Love, Bradley C. , Lu, Hanzhang , Marcantoni, Eleonora , Maumet, Camille , Mazzamuto, Giacomo , Mutsaerts, Henk , Nikolaidis, Aki , Nilsonne, Gustav , Niso, Guiomar , Nørgaard, Martin Agge , Okell, Thomas W. , Oostenveld, Robert , Pernet, Cyril R , Petr, Jan , Phillips, Christophe L. M. , Poline, Jean‐Baptiste , Raamana, Pradeep Reddy , Ritter, Petra , Salo, Taylor , Spisák, Tamás , Swann, Nicole C. , Takerkart, Sylvain , Thirion, Bertrand , Varoquaux, Gaël , Welzel, Julius , Wilson, Martin , Yarkoni, Tal , Gorgolewski, Krzysztof J.
Imaging Neuroscience | February 20, 2024 | Volume: 2 | Issue: No issue available | Pages: No page available
Abstract The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.