[论文解读] Neural Networks-based Regularization of Large-Scale Inverse Problems in Medical Imaging.

Andreas Kofler, Markus Haltmeier+5 more|arXiv (Cornell University)|Dec 19, 2019

Medical Imaging Techniques and Applications被引用 1

一句话总结

该论文提出了一种基于图像块的深度学习正则化方法，用于大规模医学图像重建，通过使用预训练神经网络作为Tikhonov先验，将数据一致性与基于先验的正则化解耦。该方法将计算速度提升了数个数量级，并在3D低剂量CT和2D径向电影MRI的PSNR、NRMSE和SSIM指标上优于当前最先进方法。

ABSTRACT

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks and cascaded neural networks have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by decoupling the regularization of the solution from ensuring consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained neural network which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative networks. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude.

研究动机与目标

• 解决在迭代深度学习网络中处理完整3D医学图像或体积时因高内存和处理需求导致的计算不可行性问题。
• 将正则化步骤与数据一致性强制分离，以允许使用复杂且高容量的神经网络进行先验学习。
• 通过将图像作为图像块或切片处理，实现在大规模医学影像数据中高效且可扩展的重建。
• 在低剂量CT和欠采样MRI等病态逆问题中，利用Tikhonov框架下的学习先验，提升图像质量。
• 在定量性能上优于总变差（total variation）和字典基正则化方法。

提出的方法

• 通过使用预训练的深度神经网络生成先验图像，将正则化与数据一致性解耦，并在Tikhonov正则化框架中使用该先验。
• 通过一种Tikhonov型正则化项应用先验，惩罚与深度网络输出的偏差，从而促进合理图像解的生成。
• 将大图像或体积作为图像块或切片处理，以降低计算负载，使复杂架构能够在大规模问题中应用。
• 利用学习到的先验，比标准迭代网络或总变差方法更有效地抑制噪声和伪影。
• 将先验整合到变分重建框架中，使最终解在数据保真度与基于先验的正则化之间实现平衡。
• 在重建流程中使用前，先在大规模数据集上独立训练先验网络，以确保泛化性和稳定性。

实验结果

研究问题

• RQ1 预训练的深度神经网络能否作为Tikhonov正则化中有效且可扩展的先验，用于大规模医学图像重建？
• RQ2 将先验学习与数据一致性解耦，是否能提升3D和大规模2D医学影像问题的计算可行性？
• RQ3 通过图像或体积的图像块处理策略，是否能在避免计算成本过高的前提下，使复杂神经网络得以应用？
• RQ4 在PSNR、NRMSE和SSIM指标上，该方法与总变差和学习字典基正则化方法相比，定量表现如何？
• RQ5 与端到端迭代深度学习方法相比，该方法在正则化步骤上加速了多少？

主要发现

• 在3D锥形束低剂量CT中，该方法在所有定量指标上均优于总变差最小化和学习字典基正则化方法。
• 在2D径向电影MRI重建中，该方法取得了更优结果，PSNR、NRMSE和SSIM指标均优于基线方法。
• 使用预训练深度网络作为先验，相比标准迭代网络，能更有效地抑制噪声和伪影。
• 图像块处理策略降低了计算复杂度，使比传统迭代架构中更深层、更复杂的网络成为可能。
• 与端到端迭代深度学习方法相比，正则化步骤的计算速度提升了数个数量级。
• 该方法在不同成像模态（包括低剂量CT和欠采样MRI）中均表现出强大的泛化能力。