Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA.
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Neuroinformatics. 2024 Apr;22(2):193-205. doi: 10.1007/s12021-024-09655-9. Epub 2024 Mar 25.
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）MRI 由于磁场不均匀会产生低频强度伪影。去除 T1w MRI 图像中的这些偏差是确保空间一致的图像解释的关键预处理步骤。N4ITK 偏置场校正作为当前的最先进方法，其实现方式使得在不同的管道和工作流程之间移植变得困难，从而难以在本地、云、边缘平台上重新实现和复制结果。此外，N4ITK 在应用前后对优化是不透明的，这意味着方法学的发展必须围绕不均匀性校正步骤进行。鉴于偏置场校正在结构预处理中的重要性和灵活的实现方式，我们追求对 N4ITK 偏置场校正进行深度学习近似/重新解释，以创建一种可移植、灵活且完全可微分的方法。在本文中，我们在八个独立的队列上训练了一个深度学习网络“DeepN4”，这些队列来自 72 个不同的扫描仪和年龄范围，使用 N4ITK 校正的 T1w MRI 和偏置场进行对数空间的监督。我们发现我们可以用朴素网络来很好地近似 N4ITK 偏置场校正。我们在测试数据集上评估了与 N4ITK 校正图像的峰值信噪比（PSNR）。N4ITK 和 DeepN4 校正图像之间的平均 PSNR 为 47.96 dB。此外，我们还在另外八个外部数据集上评估了 DeepN4 模型，并展示了该方法的通用性。本研究表明，不兼容的 N4ITK 预处理步骤可以用朴素的深度神经网络来很好地近似，从而提高了灵活性。所有代码和模型都发布在 https://github.com/MASILab/DeepN4 。
