A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability
Year: 2026
Type: preprint
Abstract: The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 ± 3/48 ± 1 dB for mouse surface-coil datasets and 49 ± 3/44 ± 4 dB for rat 2 × 2 array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas GSR was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.
Authors: Balbino Yagüe Jiménez, Aref Kalantari, Declan Bolster, Elena Botto, Lorenzo Carnevale, Sabrina Doblas, Francesco Gammaraccio, Michael Gottschalk, Khan Hekmatyar, Jan Klohs, Nyoman D. Kurniawan, Xavier López-Gil, Bastian Maus, Edoardo Micotti, Federico Moro, Susanne Müeller, Lucia Perez Nieto, Chrystelle Po, Kelley M. Swanberg, Aurélien J. Trotier, Johan Van Audekerke, Annette Van der Toorn, Ignace Van Spilbeeck, Patricia Wenk, Thomas Basse-Lüsebrink, Daniele Bertoglio, Philipp Boehm-Sturm, Eike Budinger, João Miguel das Neves Duarte, Rick M. Dijkhuizen, Cornelius Faber, Gianluigi Forloni, Philippe Garteiser, William M. Holmes, Dario Livio Longo, Martin Meier, Emma Muñoz-Moreno, Emeline J. Ribot, Rui Vasco Simoes, Giovanna D. Ielacqua, Markus Aswendt
Institutions: Universidad de Granada, University of Freiburg, University of Cologne, University Hospital Cologne, University of Glasgow, National Research Council, Istituto Neurologico Mediterraneo, Inserm, Université Paris Cité, Centre de Recherche sur l'Inflammation, Lund University, Georgia State University, The University of Queensland, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, University of Münster, University Hospital Münster, Mario Negri Institute for Pharmacological Research, Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Freie Universität Berlin, Charité – Universitätsmedizin Berlin, Centre National de la Recherche Scientifique, Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie, Université de Strasbourg, Centre de Résonance Magnétique des Systèmes Biologiques, University of Antwerp, University Medical Center Utrecht, Medizinische Hochschule Hannover, Universidade do Porto, i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Max Delbrück Center, Goethe University Frankfurt, University Hospital Frankfurt
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Award: France Life Imaging
OpenAlex ID: W7214953248