论文标题
基于仿真的胎儿大脑MRI超分辨率重建的参数优化
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
论文作者
论文摘要
在反问题中调整正规化超参数$α$是一个长期存在的问题。在胎儿脑磁共振成像的情况下，尤其如此，其中各向同性高分辨率的体积是通过运动腐败的低分辨率二维厚切片重建的。确实，缺乏地面真相图像使$α$以定量方式挑战了给定的兴趣环境。在这项工作中，我们提出了一种基于模拟的方法，用于调整给定的收购设置。我们专注于磁场强度和输入低分辨率图像的可用性对问题不良的影响。我们的结果表明，最佳$α$被选为最大化与模拟参考图像的相似性的最大相似性，它与一般采用的默认正则化值相比，显着提高了超分辨率重建精度，而不是所选管道。对临床数据的定性验证证实了将此参数调整为目标临床图像设置的重要性。
Tuning the regularization hyperparameter $α$ in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of $α$ to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune $α$ for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal $α$, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting.
