A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability
- 1Goethe University Frankfurt and University Hospital
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 array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting 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.
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BibTeX
@misc{jimenez2026harmonized,
title = {{A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability}},
author = {B. Yague Jimenez and A. Kalantari and D. Bolster and E. Botto and L. Carnevale and S. Doblas and F. Gammaraccio and M. Gottschalk and K. Hekmatyar and J. Klohs and N. Kurniawan and X. Lopez-Gil and B. Maus and E. Micotti and F. Moro and S. Mueller and L. Perez Nieto and C. Po and K. Swanberg and A. Trotier and J. Van Audekerke and A. Van der Toorn and I. Van Spilbeeck and P. Wenk and T. Basse-Luesebrink and D. Bertoglio and P. Boehm-Sturm and E. Budinger and J. M. das Neves Duarte and R. Dijkhuizen and C. Faber and G. Forloni and P. Garteiser and W. Holmes and D. Longo and M. Meier and E. Munoz-Moreno and E. Ribot and R. Vasco Simoes and G. Ielacqua and M. Aswendt},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.29.754185},
url = {https://www.biorxiv.org/content/10.64898/2026.09.29.754185v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A harmonized phantom MRI quality control framework identifies sources of longitudinal and multi-site variability
AU - B. Yague Jimenez
AU - A. Kalantari
AU - D. Bolster
AU - E. Botto
AU - L. Carnevale
AU - S. Doblas
AU - F. Gammaraccio
AU - M. Gottschalk
AU - K. Hekmatyar
AU - J. Klohs
AU - N. Kurniawan
AU - X. Lopez-Gil
AU - B. Maus
AU - E. Micotti
AU - F. Moro
AU - S. Mueller
AU - L. Perez Nieto
AU - C. Po
AU - K. Swanberg
AU - A. Trotier
AU - J. Van Audekerke
AU - A. Van der Toorn
AU - I. Van Spilbeeck
AU - P. Wenk
AU - T. Basse-Luesebrink
AU - D. Bertoglio
AU - P. Boehm-Sturm
AU - E. Budinger
AU - J. M. das Neves Duarte
AU - R. Dijkhuizen
AU - C. Faber
AU - G. Forloni
AU - P. Garteiser
AU - W. Holmes
AU - D. Longo
AU - M. Meier
AU - E. Munoz-Moreno
AU - E. Ribot
AU - R. Vasco Simoes
AU - G. Ielacqua
AU - M. Aswendt
PY - 2026
DA - 2026/09/29
PB - bioRxiv
DO - 10.64898/2026.09.29.754185
UR - https://www.biorxiv.org/content/10.64898/2026.09.29.754185v1
AB - 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 array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting 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.
ER -