DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network “DeepN4” on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

T1加权（T1w）磁共振成像由于磁场不均匀性存在低频强度伪影。去除T1w磁共振成像图像中的这些偏差是确保空间上一致的图像解读的关键预处理步骤。N4ITK偏差场校正作为当前最先进的技术，其实现方式使得它难以在不同的流程和工作流之间移植，从而导致在本地、云端和边缘平台上难以重新实现和重现结果。此外，N4ITK在应用前后的优化方面不透明，这意味着方法学的发展必须围绕不均匀性校正步骤进行。鉴于偏差场校正在结构预处理和灵活实施方面的重要性，我们对N4ITK偏差场校正进行深度学习近似/重新解释，以创建一种可移植、灵活且完全可微的方法。在本文中，我们使用来自72台不同扫描仪且年龄范围各异的8个独立队列的数据，以及经过N4ITK校正的T1w磁共振成像和偏差场（在对数空间中用于监督）来训练一个深度学习网络“DeepN4”。我们发现，我们可以用简单的网络紧密近似N4ITK偏差场校正。我们根据N4ITK校正后的图像评估测试数据集中的峰值信噪比（PSNR）。N4ITK和DeepN4之间校正图像的中位PSNR为47.96 dB。此外，我们在另外8个外部数据集上评估DeepN4模型，并展示了该方法的通用性。这项研究表明，不兼容的N4ITK预处理步骤可以通过简单的深度神经网络紧密近似，从而提高灵活性。所有代码和模型都发布在https://github.com/MASILab/DeepN4。