Intra‐MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework
Abstract
Despite numerous magnetic resonance imaging (MRI) head motion mitigation strategies, the lack of rigorous evaluation limits their optimization and clinical adoption. We propose an in vivo framework combining a visual instruction system for reproducible head motion with reference standard interpose displacement estimation to assess intra‐MRI tracking accuracy and precision. Its utility is demonstrated by comparing a markerless optical system (MOS) and a fat‐signal navigator (FatNav). Six participants underwent 3T T1‐weighted brain MRI with a FatNav module, performing visually guided 2° and 4° head rotations around the X ‐ and Z ‐axes using MOS feedback. T1‐weighted images were acquired at seven distinct head poses. MOS and FatNav motion estimates were compared against rigid registration of the T1‐weighted images, which served as the reference standard. MOS‐ and FatNav‐corrected images for the three successive head rotations were also compared using the structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), and a focus measure. FatNav accuracy was inferior for translations ( p < 0.001) and 2°–4° rotations but improved to match MOS for subtle pitch + and yaw + , even surpassing it for subtle yaw − . Meanwhile, MOS precision was higher for yaw + than yaw − ( p < 0.001) but inferior to FatNav for pitch + ( p = 0.041). MOS better restored T1‐weighted image fidelity, yielding higher SSIM, PSNR, and focus ( p < 0.01). Notably, the framework detected a subtle improvement in FatNav performance with neck masking, an effect uncaptured by conventional image quality metrics. In conclusion, while image quality metrics suggested superior overall correction with MOS, our framework provided a more detailed characterization of in vivo performance differences.
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BibTeX
@article{zariry2026intramri,
title = {{Intra‐MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework}},
author = {Zakaria Zariry and Franck Lamberton and Robert Frost and Thomas Gaass and Thomas Troalen and Holly Rayson and Jakob Slipsager and Nathalie Richard and Andre van der Kouwe and James Bonaiuto and Bassem Hiba},
journal = {NMR in Biomedicine},
year = {2026},
month = aug,
doi = {10.1002/nbm.70368},
url = {https://hal.science/hal-05804614v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Intra‐MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework
AU - Zakaria Zariry
AU - Franck Lamberton
AU - Robert Frost
AU - Thomas Gaass
AU - Thomas Troalen
AU - Holly Rayson
AU - Jakob Slipsager
AU - Nathalie Richard
AU - Andre van der Kouwe
AU - James Bonaiuto
AU - Bassem Hiba
PY - 2026
DA - 2026/08/03
JO - NMR in Biomedicine
DO - 10.1002/nbm.70368
UR - https://hal.science/hal-05804614v1
AB - Despite numerous magnetic resonance imaging (MRI) head motion mitigation strategies, the lack of rigorous evaluation limits their optimization and clinical adoption. We propose an in vivo framework combining a visual instruction system for reproducible head motion with reference standard interpose displacement estimation to assess intra‐MRI tracking accuracy and precision. Its utility is demonstrated by comparing a markerless optical system (MOS) and a fat‐signal navigator (FatNav). Six participants underwent 3T T1‐weighted brain MRI with a FatNav module, performing visually guided 2° and 4° head rotations around the X ‐ and Z ‐axes using MOS feedback. T1‐weighted images were acquired at seven distinct head poses. MOS and FatNav motion estimates were compared against rigid registration of the T1‐weighted images, which served as the reference standard. MOS‐ and FatNav‐corrected images for the three successive head rotations were also compared using the structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), and a focus measure. FatNav accuracy was inferior for translations ( p < 0.001) and 2°–4° rotations but improved to match MOS for subtle pitch + and yaw + , even surpassing it for subtle yaw − . Meanwhile, MOS precision was higher for yaw + than yaw − ( p < 0.001) but inferior to FatNav for pitch + ( p = 0.041). MOS better restored T1‐weighted image fidelity, yielding higher SSIM, PSNR, and focus ( p < 0.01). Notably, the framework detected a subtle improvement in FatNav performance with neck masking, an effect uncaptured by conventional image quality metrics. In conclusion, while image quality metrics suggested superior overall correction with MOS, our framework provided a more detailed characterization of in vivo performance differences.
SN - 0952-3480
SN - 1099-1492
ER -