These metals are supposed as Titanium. Detailed parameters of the acquisition geometry can be found in Table 1. This sample is 3D cone-beam data. The estimated spectrum is illustrated in Figure 1 ( Right). 2 2 Additional Details of Baselines In our experiments, we compare our proposed method against eight baseline MAR approaches. Specifically, it learns the prior distribution of metal-free CT images with a generative model in order to infer the lost sinogram in the metal-affected regions.

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.

These metals are supposed as Titanium. Detailed parameters of the acquisition geometry can be found in Table 1. This sample is 3D cone-beam data. The estimated spectrum is illustrated in Figure 1 ( Right). 2 2 Additional Details of Baselines In our experiments, we compare our proposed method against eight baseline MAR approaches. Specifically, it learns the prior distribution of metal-free CT images with a generative model in order to infer the lost sinogram in the metal-affected regions.

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. Specifically, we first derive a polychromatic forward model to accurately simulate the nonlinear CT acquisition process.