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

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

José P. Marques, Jakob Meineke, Carlos Milovic, Berkin Bilgic, Kwok‐Shing Chan, Renaud Hedouin, Wietske van der Zwaag, Christian Langkammer, Ferdinand Schweser

Abstract

Purpose

To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM.

Methods

We created a digital whole‐head tissue property phantom by segmenting and postprocessing high‐resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady‐state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier‐based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion‐weighted imaging phantom data are provided, allowing future investigations of tissue‐microstructure effects in phase and QSM algorithms.

Results

The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom’s properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial‐volume effects or complex distributions of frequency shifts within the voxel.

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 susceptibility reconstruction algorithms.