论文标题
接头重建和偏置场的校正，以采样不足的MR成像
Joint reconstruction and bias field correction for undersampled MR imaging
论文作者
论文摘要
在MRI中采样k空间可以节省宝贵的收购时间，但导致了不良的反转问题。最近，已经开发了许多深度学习技术，解决了从不足采样数据中恢复完全采样的MR图像的问题。但是，这些基于学习的方案容易受到训练数据与要在测试时重建的图像之间的差异。这样的差异可以归因于MR图像中存在的偏置场，这是由场不均匀性和线圈敏感性引起的。在这项工作中，我们解决了重建问题对偏置领域的敏感性，并建议在重建中明确对其进行建模，以降低这种敏感性。为此，我们在联合优化方案中使用基于学习的重建算法作为我们的基础，并将其与基于N4的偏置域估计方法结合使用。我们使用HCP数据集以及内部测量图像进行评估。我们表明，所提出的方法在视觉上和RMSE方面都提高了重建质量。
Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Recently, many deep learning techniques have been developed, addressing this issue of recovering the fully sampled MR image from the undersampled data. However, these learning based schemes are susceptible to differences between the training data and the image to be reconstructed at test time. One such difference can be attributed to the bias field present in MR images, caused by field inhomogeneities and coil sensitivities. In this work, we address the sensitivity of the reconstruction problem to the bias field and propose to model it explicitly in the reconstruction, in order to decrease this sensitivity. To this end, we use an unsupervised learning based reconstruction algorithm as our basis and combine it with a N4-based bias field estimation method, in a joint optimization scheme. We use the HCP dataset as well as in-house measured images for the evaluations. We show that the proposed method improves the reconstruction quality, both visually and in terms of RMSE.
