QSM Reconstruction Challenge 2.0: Design and Report of Results


Journal article


B. Bilgiç, C. Langkammer, J. Marques, J. Meineke, C. Milovic, F. Schweser
bioRxiv, 2020

Semantic Scholar DOI PubMed
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APA
Bilgiç, B., Langkammer, C., Marques, J., Meineke, J., Milovic, C., & Schweser, F. (2020). QSM Reconstruction Challenge 2.0: Design and Report of Results. BioRxiv.

Chicago/Turabian
Bilgiç, B., C. Langkammer, J. Marques, J. Meineke, C. Milovic, and F. Schweser. “QSM Reconstruction Challenge 2.0: Design and Report of Results.” bioRxiv (2020).

MLA
Bilgiç, B., et al. “QSM Reconstruction Challenge 2.0: Design and Report of Results.” BioRxiv, 2020.


Abstract

Purpose The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. Methods A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground-truths available to mimic the clinical setting. At the second stage, ground-truths were provided for parameter optimization.Submissions were evaluated using eight numerical metrics and visual ratings. Results A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep-learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and Total Variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. Conclusion The synthetic data provides a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithmsproduced the best results along all metrics. Future QSM challenges should asses if this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.