School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
Med Phys. 2024 Sep;51(9):6149-6160. doi: 10.1002/mp.17233. Epub 2024 Jun 3.
Cone-beam computed tomography (CBCT), an important medical modality for disease detection and diagnosis, is currently widely used in clinical practice. However, due to the inconsistent response of CBCT detectors, the lack of proper calibration often leads to the occurrence of ring artifacts in CBCT-reconstructed images. These artifacts may affect physicians' assessment and diagnosis. Therefore, effective elimination of ring artifacts in CBCT images without degrading image quality is important.
Given the pros and cons of existing methods for removing ring artifacts in CBCT images, this paper is devoted to designing a specific Transformer for this task, leveraging the global and local modeling ability of Transformer.
We design a loss function with dual-domain information fusion for the vanilla Transformer to correct ring artifacts in CBCT images. The method operates in image domain to predict artifact-free outputs and preserve more image details. Meanwhile, we design a tailored loss function incorporating polar domain optimization to remove ring artifacts more effectively. Specifically, an unidirectional gradient loss that constrains vertical gradients in polar domain is imposed, based on the geometric prior that in polar coordinates, ring artifacts predominately affect horizontal gradients while minimally influencing vertical gradients.
We conduct extensive ablative and comparative experiments on CBCT/CT image set to validate the performance of the proposed method. First, four ablation experiments demonstrate the feasibility of our approach. Then, we compare our method with several classical methods and the latest state-of-the-arts, and our method achieves the highest quality of corrected images as well as the best evaluation metrics. In these experiments, 5332 CT images were used for training, and 550 CT images, and 500 real CBCT images were used for testing. The source code is available at https://github.com/shasha521/CBCT.
Experimental results demonstrate that our method can significantly improve the effectiveness of ring artifact correction. By capitalizing on dual-domain information fusion and a customized loss function, the improved Transformer can not only effectively remove ring artifacts in CBCT images, but also preserve the details of original images quite well.
锥形束计算机断层扫描（CBCT）是一种用于疾病检测和诊断的重要医学模态，目前在临床实践中得到广泛应用。然而，由于 CBCT 探测器的响应不一致，缺乏适当的校准通常会导致 CBCT 重建图像中出现环形伪影。这些伪影可能会影响医生的评估和诊断。因此，在不降低图像质量的情况下，有效地消除 CBCT 图像中的环形伪影是很重要的。
鉴于现有的消除 CBCT 图像中环形伪影的方法存在优缺点，本文致力于为此任务设计一个特定的 Transformer，利用 Transformer 的全局和局部建模能力。
我们设计了一个具有双域信息融合的损失函数，用于原始 Transformer 来校正 CBCT 图像中的环形伪影。该方法在图像域中工作，以预测无伪影的输出并保留更多的图像细节。同时，我们设计了一个结合极坐标域优化的定制损失函数，以更有效地去除环形伪影。具体来说，基于在极坐标中环形伪影主要影响水平梯度而最小程度地影响垂直梯度的几何先验，施加了一个约束极坐标域中垂直梯度的单向梯度损失。
我们在 CBCT/CT 图像集上进行了广泛的消融和对比实验，以验证所提出方法的性能。首先，进行了四个消融实验，以验证方法的可行性。然后，我们将我们的方法与几种经典方法和最新的方法进行了比较，我们的方法在校正图像的质量和评估指标方面都取得了最好的结果。在这些实验中，使用了 5332 张 CT 图像进行训练，使用了 550 张 CT 图像和 500 张真实的 CBCT 图像进行测试。源代码可在 https://github.com/shasha521/CBCT 上获得。
实验结果表明，我们的方法可以显著提高环形伪影校正的效果。通过利用双域信息融合和定制的损失函数，改进后的 Transformer 不仅可以有效地去除 CBCT 图像中的环形伪影，而且还可以很好地保留原始图像的细节。
