Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body. CT metal artifacts arise from the drastic variation of metal's attenuation coefficients at various energy levels of the X-ray spectrum, leading to a nonlinear metal effect in CT measurements. Recovering CT images from metal-affected measurements hence poses a complicated nonlinear inverse problem where empirical models adopted in previous metal artifact reduction (MAR) approaches lead to signal loss and strongly aliased reconstructions. Polyner instead models the MAR problem from a nonlinear inverse problem perspective. Specifically, we first derive a polychromatic forward model to accurately simulate the nonlinear CT acquisition process. Then, we incorporate our forward model into the implicit neural representation to accomplish reconstruction. Lastly, we adopt a regularizer to preserve the physical properties of the CT images across different energy levels while effectively constraining the solution space. Our Polyner is an unsupervised method and does not require any external training data. Experimenting with multiple datasets shows that our Polyner achieves comparable or better performance than supervised methods on in-domain datasets while demonstrating significant performance improvements on out-of-domain datasets. To the best of our knowledge, our Polyner is the first unsupervised MAR method that outperforms its supervised counterparts. The code for this work is available at: https://github.com/iwuqing/Polyner.

翻译：新兴的基于断层成像的神经重建技术（例如NeRF、NeAT和NeRP）已在医学成像中展现出独特能力。本文提出一种新型多色神经表示（Polyner），以解决人体内存在金属植入物时CT成像的挑战性问题。CT金属伪影源于金属在不同X射线能谱能量水平下衰减系数的剧烈变化，导致CT测量中的非线性金属效应。从受金属影响的测量中恢复CT图像因此构成复杂的非线性逆问题，以往金属伪影减少（MAR）方法采用的经验模型会导致信号损失和严重混叠重建。Polyner从非线性逆问题视角对MAR问题进行建模。具体而言，我们首先推导出一个多色前向模型以精确模拟非线性CT采集过程。然后，我们将该前向模型整合到隐式神经表示中以完成重建。最后，我们采用正则化项来保留CT图像在不同能量水平下的物理特性，同时有效约束解空间。我们的Polyner是一种无监督方法，无需任何外部训练数据。在多个数据集上的实验表明，Polyner在域内数据集上达到与监督方法相当或更优的性能，同时在域外数据集上展现出显著的性能提升。据我们所知，Polyner是首个性能超越监督对应方法且无监督的MAR方法。本工作的代码可在https://github.com/iwuqing/Polyner获取。