Park Hyoung Suk, Seo Jin Keun, Jeon Kiwan
National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea.
Med Phys. 2025 Apr;52(4):2201-2211. doi: 10.1002/mp.17649. Epub 2025 Jan 29.
In X-ray computed tomography (CT), metal-induced beam hardening artifacts arise from the complex interactions between polychromatic X-ray beams and metallic objects, leading to degraded image quality and impeding accurate diagnosis. A previously proposed metal-induced beam hardening correction (MBHC) method provides a theoretical framework for addressing nonlinear artifacts through mathematical analysis, with its effectiveness demonstrated by numerical simulations and phantom experiments. However, in practical applications, this method relies on precise segmentation of highly attenuating materials and parameter estimations, which limit its ability to fully correct artifacts caused by the intricate interactions between metals and other dense materials, such as bone or teeth.
This study aims to develop a parameter-free MBHC method that eliminates the need for accurate segmentation and parameter estimations, thereby addressing the limitations of the original MBHC approach.
The proposed method employs implicit neural representations (INR) to generate two tomographic images: one representing the monochromatic attenuation distribution at a specific energy level, and another capturing the nonlinear beam hardening effects caused by the polychromatic nature of X-ray beams. A loss function drives the generation of these images, where the predicted projection data is nonlinearly modeled by the combination of the two images. This approach eliminates the need for geometric and parameter estimation of metals, providing a more generalized solution.
Numerical and phantom experiments demonstrates that the proposed method effectively reduces beam hardening artifacts caused by interactions between highly attenuating materials such as metals, bone, and teeth. Additionally, the proposed INR-based method demonstrates potential in addressing challenges related to data insufficiencies, such as photon starvation and truncated fields of view in CT imaging.
The proposed generalized MBHC method provides high-quality image reconstructions without requiring parameter estimations and segmentations, offering a robust solution for reducing metal-induced beam hardening artifacts in CT imaging.
在X射线计算机断层扫描（CT）中，金属诱导的束硬化伪影源于多色X射线束与金属物体之间的复杂相互作用，导致图像质量下降并妨碍准确诊断。先前提出的金属诱导束硬化校正（MBHC）方法通过数学分析为解决非线性伪影提供了一个理论框架，其有效性已通过数值模拟和体模实验得到证明。然而，在实际应用中，该方法依赖于高衰减材料的精确分割和参数估计，这限制了其完全校正由金属与其他致密材料（如骨骼或牙齿）之间复杂相互作用引起的伪影的能力。
本研究旨在开发一种无参数的MBHC方法，该方法无需精确分割和参数估计，从而解决原始MBHC方法的局限性。
所提出的方法采用隐式神经表示（INR）来生成两张断层图像：一张表示特定能量水平下的单色衰减分布，另一张捕捉由X射线束的多色性质引起的非线性束硬化效应。一个损失函数驱动这些图像的生成，其中预测的投影数据由这两张图像的组合进行非线性建模。这种方法无需对金属进行几何和参数估计，提供了一个更通用的解决方案。
数值和体模实验表明，所提出的方法有效地减少了由金属、骨骼和牙齿等高衰减材料之间的相互作用引起的束硬化伪影。此外，所提出的基于INR的方法在解决与数据不足相关的挑战方面显示出潜力，如CT成像中的光子饥饿和截断视野。
所提出的通用MBHC方法无需参数估计和分割即可提供高质量的图像重建，为减少CT成像中金属诱导的束硬化伪影提供了一个强大的解决方案。
