Laboratory of Image Science and Technology (LIST), Southeast University, Nanjing, Jiangsu 210096, China.
Med Phys. 2013 Aug;40(8):081903. doi: 10.1118/1.4812424.
In computed tomography, metallic objects in the scanning field create the so-called metal artifacts in the reconstructed images. Interpolation-based methods for metal artifact reduction (MAR) replace the metal-corrupted projection data with surrogate data obtained from interpolation using the surrounding uncorrupted sinogram information. Prior-based MAR methods further improve interpolation-based methods by better estimating the surrogate data using forward projections from a prior image. However, the prior images in most existing prior-based methods are obtained from segmented images and misclassification in segmentation often leads to residual artifacts and tissue structure loss in the final corrected images. To overcome these drawbacks, the authors propose a fusion scheme, named fusion prior-based MAR (FP-MAR).
The FP-MAR method consists of (i) precorrect the image by means of an interpolation-based MAR method and an edge-preserving blur filter; (ii) generate a prior image from the fusion of this precorrected image and the originally reconstructed image with metal parts removed; (iii) forward project this prior image to guide the estimation of the surrogate data using well-developed replacement techniques.
Both simulations and clinical image tests are carried out to show that the proposed FP-MAR method can effectively reduce metal artifacts. A comparison with other MAR methods demonstrates that the FP-MAR method performs better in artifact suppression and tissue feature preservation.
From a wide range of clinical cases to which FP-MAR has been tested (single or multiple pieces of metal, various shapes, and sizes), it can be concluded that the proposed fusion based prior image preserves more tissue information than other segmentation-based prior approaches and can provide better estimates of the surrogate data in prior-based MAR methods.
在计算机断层扫描中，扫描场中的金属物体在重建图像中会产生所谓的金属伪影。基于插值的金属伪影减少（MAR）方法用通过使用周围未损坏的正弦图信息进行插值获得的替代数据来替换损坏的投影数据。基于先验的 MAR 方法通过使用先验图像的正向投影更好地估计替代数据，从而进一步改进基于插值的方法。然而，大多数现有基于先验的方法中的先验图像是从分割图像中获得的，分割中的错误分类常常导致最终校正图像中的残留伪影和组织结构丢失。为了克服这些缺点，作者提出了一种融合方案，称为融合基于先验的 MAR（FP-MAR）。
FP-MAR 方法包括：（i）通过基于插值的 MAR 方法和边缘保持模糊滤波器对图像进行预校正；（ii）融合经过预处理的图像和去除金属部分的原始重建图像，生成先验图像；（iii）对该先验图像进行正向投影，以使用成熟的替换技术指导替代数据的估计。
通过模拟和临床图像测试均表明，所提出的 FP-MAR 方法可以有效减少金属伪影。与其他 MAR 方法的比较表明，FP-MAR 方法在抑制伪影和保留组织特征方面表现更好。
从广泛的临床案例（单块或多块金属，各种形状和大小）来看，所提出的基于融合的先验图像比其他基于分割的先验方法保留了更多的组织信息，并且可以在基于先验的 MAR 方法中提供更好的替代数据估计。
