QSM reconstruction challenge 2.0: Design and report of results
QSM reconstruction challenge 2.0: Design and report of results
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DOI：
10.1002/mrm.28754
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发表时间：
2021-03-30
影响因子：
3.3
通讯作者：
Schweser, Ferdinand
中科院分区：
文献类型：
--
作者：
Bilgic, Berkin;Langkammer, Christian;Schweser, Ferdinand
目的：第二届定量磁化率图(QSM)重建挑战赛(2019年10月，韩国首尔)的目的是在模拟脑数据中检验QSM偶极子倒置算法的准确性。参与者被提供了使用MR模拟器从两个真实的硅头模型合成的多回波梯度回波图像的数据集。在第一阶段，参与者在没有可用来模拟临床环境的基本事实数据的情况下优化QSM重建。在第二阶段，为参数优化提供了地面实况数据。结果：第一阶段共提交了98个重建项目，第二阶段共提交了47个项目。迭代方法的量化指标得分最高，其次是深度学习方法和直接倒置方法。根据震级数据得出的先验数据提高了衡量标准的分数。基于迭代方法和全变差(及其导数)的算法产生了最好的整体结果。已报道的结果和分析管道已公开，以便研究人员将新方法与当前技术状态进行比较。结论：合成数据提供了一个一致的框架，以测试存在噪声、钙化和微小体素去相效应时QSM算法的准确性和稳健性。基于全变差的算法在所有指标中产生了最好的结果。未来的QSM挑战应该评估合成数据集的这种良好表现是否转化为更现实的场景，其中包括背景场和偶极不相容的相位贡献。
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 truth data available to mimic the clinical setting. At the second stage, ground truth data 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 provide 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 algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.