MR Biophysics Lab

Buffalo Neuroimaging Analysis Center

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures


Journal article


J. Marques, J. Meineke, C. Milovic, B. Bilgiç, Kwok-Shing Chan, Renaud Hédouin, W. van der Zwaag, C. Langkammer, F. Schweser
bioRxiv, 2020

Semantic Scholar DOI PubMedCentral PubMed
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APA   Click to copy
Marques, J., Meineke, J., Milovic, C., Bilgiç, B., Chan, K.-S., Hédouin, R., … Schweser, F. (2020). QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures. BioRxiv.


Chicago/Turabian   Click to copy
Marques, J., J. Meineke, C. Milovic, B. Bilgiç, Kwok-Shing Chan, Renaud Hédouin, W. van der Zwaag, C. Langkammer, and F. Schweser. “QSM Reconstruction Challenge 2.0: A Realistic in Silico Head Phantom for MRI Data Simulation and Evaluation of Susceptibility Mapping Procedures.” bioRxiv (2020).


MLA   Click to copy
Marques, J., et al. “QSM Reconstruction Challenge 2.0: A Realistic in Silico Head Phantom for MRI Data Simulation and Evaluation of Susceptibility Mapping Procedures.” BioRxiv, 2020.


BibTeX   Click to copy

@article{j2020a,
  title = {QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures},
  year = {2020},
  journal = {bioRxiv},
  author = {Marques, J. and Meineke, J. and Milovic, C. and Bilgiç, B. and Chan, Kwok-Shing and Hédouin, Renaud and van der Zwaag, W. and Langkammer, C. and Schweser, F.}
}

Abstract

Purpose To create a realistic in-silico head phantom for the second QSM Reconstruction Challenge and for future evaluations of processing algorithms for Quantitative Susceptibility Mapping (QSM). Methods We created a whole-head tissue property model by segmenting and post-processing high-resolution, multi-parametric MRI data acquired from a healthy volunteer. We simulated the steady-state magnetization using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based post-processing. We demonstrated some of the phantom’s properties, including the possibility of generating phase data that do not evolve linearly with echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the model, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Results The brain-part of the phantom features realistic morphology combined with realistic spatial variations in relaxation and susceptibility values. Simulation code allows adjusting the following parameters and effects: repetition time and echo time, voxel size, background fields, and RF phase biases. Additionally, diffusion weighted imaging data of the phantom is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. Conclusion The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative MRI reconstruction algorithms.