In computed tomography (CT), the presence of metallic implants in patients
often leads to disruptive artifacts in the reconstructed images, hindering
accurate diagnosis. Recently, a large amount of supervised deep learning-based
approaches have been proposed for metal artifact reduction (MAR). However,
these methods neglect the influence of initial training weights. In this paper,
we have discovered that the uncertainty image computed from the restoration
result of initial training weights can effectively highlight high-frequency
regions, including metal artifacts. This observation can be leveraged to assist
the MAR network in removing metal artifacts. Therefore, we propose an
uncertainty constraint (UC) loss that utilizes the uncertainty image as an
adaptive weight to guide the MAR network to focus on the metal artifact region,
leading to improved restoration. The proposed UC loss is designed to be a
plug-and-play method, compatible with any MAR framework, and easily adoptable.
To validate the effectiveness of the UC loss, we conduct extensive experiments
on the public available Deeplesion and CLINIC-metal dataset. Experimental
results demonstrate that the UC loss further optimizes the network training
process and significantly improves the removal of metal artifacts.

通过计算机断层扫描（CT），本研究发现利用初始训练权重所计算的不确定性图像可以有效地突显高频区域（包括金属伪影），从而帮助去除金属伪影，实验结果显示不确定性约束损失进一步优化了网络训练过程并显著提高了去除金属伪影的效果。