论文标题
结合用于减少金属人工制品的多模式信息：无监督的深度学习框架
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
论文作者
论文摘要
金属伪像还原（MAR）技术旨在消除金属诱导的临床图像噪声。在计算机断层扫描（CT）中，监督的深度学习方法已显示出有效，但在普遍性方面有限，因为它们主要依赖于合成数据。相反，在磁共振成像（MRI）中，尚未引入任何方法来纠正易感性伪像，即使是在MAR特定的采集中仍然存在。在这项工作中，我们假设MAR的多模式方法将改善CT和MRI。鉴于它们的外观不同，他们的互补信息可以以两种方式弥补损坏的信号。因此，我们为多模式MAR提出了一种无监督的深度学习方法。我们介绍了将局部归一化的跨相关性用作损失项的使用，以鼓励融合多模式信息。实验表明，我们的方法有利于CT中更平稳的校正，同时促进MRI中的信号恢复。
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown effective but limited in generalisability, as they mostly rely on synthetic data. In Magnetic Resonance Imaging (MRI) instead, no method has yet been introduced to correct the susceptibility artefact, still present even in MAR-specific acquisitions. In this work, we hypothesise that a multimodal approach to MAR would improve both CT and MRI. Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. Experiments show that our approach favours a smoother correction in the CT, while promoting signal recovery in the MRI.
