Axon Connected, LLC, Earlysville, VA, 22936, USA.
Department of Mathematics, Union College, Schenectady, NY, 12308, USA.
Med Phys. 2021 Jul;48(7):3790-3803. doi: 10.1002/mp.14932. Epub 2021 Jun 28.
In x-ray radiography, the commonly used antiscatter grid for enhancing image quality causes artifacts in the form of periodic noises, such as shadows, cutoff, and Moiré fringes. Software degridding is traditionally performed via linear or homomorphic filtering in the spectral domain. These methods inevitably result in image blurring, information loss, and distortion, thus hindering detection and assessment of diseases. We seek effective and practical solutions for grid artifact correction based on spatial-domain analysis toward high-quality imaging.
By analyzing the physical process of grid artifact formation, we track down the root of the problem associated with spectral filtering. We propose the grid regression demodulation (GRD). The degridding cost is forged as a functional of the latent x-ray photon image and parametric grid model characterizing grid transmission property. Regularization on the grid spectra is incorporated. We devise optimization algorithms for artifact correction and grid pattern estimation. GRD decouples the partially overlapped spectra of the grid and anatomy, and removes the artifacts independently, thus restoring the underlying clinically relevant data.
Method efficacy is demonstrated using simulated and real data. GRD effectively preserves image edges, textures, and patterns while removing grid artifacts. For the known ground truth setting, GRD gives a near-perfect correction. For real data, GRD is capable of correcting not only the primary grid artifacts, but also the higher grid harmonic artifacts while keeping image content unaltered, which is unachievable by the other methods. Our method has low residual errors and exhibits a successful demodulation effect without introducing additional artifacts, while ringing or cilia artifacts are present in the others.
The proposed method outperforms the prevalent transform techniques for correcting grid artifacts in digital radiography. It is self-sustained and self-adaptive to a range of targets and beam quality. Our approach is advantageous in restoring the latent image while suppressing grid noises. It retrieves the true scale factor of the degridded data, which is unattainable via any spectral filtering techniques. This work unlocks a promising venue to improve and upgrade low-dose medical radiographic imaging technology.
在 X 射线射线照相中，常用的散射消除栅格用于增强图像质量，会导致周期性噪声的伪影，例如阴影、截止和莫尔条纹。传统上，软件去栅格化是通过光谱域中的线性或同态滤波来完成的。这些方法不可避免地导致图像模糊、信息丢失和失真，从而阻碍疾病的检测和评估。我们寻求基于空域分析的有效实用的解决方案，以实现高质量成像。
通过分析栅格伪影形成的物理过程，我们追踪与光谱滤波相关的问题根源。我们提出了栅格回归解调（GRD）。去栅格化成本被伪造为潜在 X 射线光子图像和参数栅格模型的函数，该模型描述了栅格传输特性。对栅格光谱进行正则化。我们设计了用于伪影校正和栅格图案估计的优化算法。GRD 分离了栅格和解剖结构的部分重叠光谱，并独立去除伪影，从而恢复潜在的临床相关数据。
使用模拟和真实数据证明了该方法的有效性。GRD 有效地保留了图像的边缘、纹理和图案，同时去除了栅格伪影。对于已知的真实设置，GRD 提供了近乎完美的校正。对于真实数据，GRD 不仅能够校正主要的栅格伪影，而且能够在保持图像内容不变的情况下校正更高的栅格谐波伪影，而其他方法则无法实现。我们的方法具有较低的残余误差，并表现出成功的解调效果，而不会引入额外的伪影，而其他方法则存在振铃或纤毛伪影。
与数字射线照相中的普遍变换技术相比，所提出的方法在纠正栅格伪影方面表现出色。它是自我维持和自适应的，可以适应各种目标和射线质量。我们的方法在抑制栅格噪声的同时，有利于恢复潜在图像。它恢复了去栅格化数据的真实比例因子，这是任何光谱滤波技术都无法实现的。这项工作为改进和升级低剂量医学射线照相成像技术开辟了一个有前途的途径。
