Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction Techniques

通过图像重建减少临床X射线CT中的金属伪影 技术 摘要： 金属植入物，如牙齿填充物、手术夹子、线圈、钢丝和矫形五金 在患者体内是非常有帮助的，为患者提供更好的健康益处。论 另一方面，它们可能会在许多医学成像方式中造成伪影，例如在x射线CT中 还有核磁共振。尽管在硬件和软件方面都取得了重大进步 多年来，X射线CT扫描中的金属伪影仍然很麻烦。金属器物 显示为暗阴影和亮条纹。这些人工制品，如果不纠正，可能会严重 降低图像质量，降低临床检查的诊断价值。 在X射线管中产生X射线的效率非常低；效率远远不到1%。一个 高光子计数要求导致接受广泛的能谱，这是 由韧致辐射和特征辐射共同贡献。内部的金属物体 患者的身体会导致光子饥饿和光束硬化。这些影响是非线性的，而且 很难为它们建立一个准确的数学模型。最先进的治疗方法之一 是使用双能量CT测量两组投影，然后通过使用 数学模型，并结合这两个数据集来估计一组综合的 单能X射线测量。另一种补救方法是使用金属艺术品。 简化(MAR)算法，用于替换探测器中损坏的投影测量值 利用来自相邻未被破坏的投影的内插。在数据匮乏的情况下， 双能源方法并不有效。目前的MAR算法还不成熟，它们 可能会在试图抑制金属伪像的同时创建新的伪像。 这是我们之前的R15奖助金的续订申请，该奖助金的标题是“快速和强大的低剂量 X射线CT图像重建，其中我们已经成功地显影图像 用于对抗噪声的重建算法。我们的一些想法是在过去三年中形成的 可进一步提出减少X射线CT中金属伪影的新思路。在此R15中 更新后，我们将专注于基于新的金属伪影减少(MAR)算法的开发 常规的单能量数据采集。创新是构建社会主义和谐社会的新途径 优化的目标函数。我们目标函数的独特性在于它们不 有一个数据保真度术语；它们只包含贝叶斯术语。形成了贝叶斯项 从金属制品的特征来看。采用梯度下降法对目标进行优化 函数和一组新的未被破坏的测量被估计。最终的图像是 采用滤波反投影(FBP)算法进行重建。 提出的算法具有较高的性价比。我们假设所提出的方法将 比商业CT扫描仪可用的最先进的MAR方法更有效。 这一假设将在更新的R15中仔细评估。我们的临床合作者位于 犹他大学医疗保健公司将与我们密切合作，为我们提供临床数据和 专业的评估建议。 此R15项目为犹他州山谷大学(UVU)的学生提供实践 在医疗保健领域进行真实世界研究的机会和经验。会的 激发学生的兴趣，使他们考虑从事生物医学和 生物工程领域/行业。

Title: Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction Techniques Abstract: Metallic implants such as dental fillings, surgical clips, coils, wires, and orthopedic hardware inside the patient body are very helpful in providing patients with better health benefits. On the other hand, they may cause artifacts in many medical imaging modalities such as in x-ray CT and MRI. Even though significant advances in both hardware and software have been made over the years, metal artifacts in x-ray CT scans are still troublesome. The metal artifacts appear as dark shadows and bright streaks. These artifacts, if not corrected, can severely degrade the image quality and decrease the diagnostic value of the clinical examination. X-ray generation in an x-ray tube is very inefficient; the efficiency is much less than 1%. A high photon count requirement results in the acceptance of a wide energy spectrum, which is contributed by both Bremsstrahlung and characteristic radiation. Metallic objects inside the patient body can cause photon starvation and beam hardening. These effects are nonlinear and difficult to establish an exact mathematical model for them. One of the state-of-the-art remedies is to use the dual-energy CT to measure two sets of projections, and then by using a mathematical model and combining these two data sets to estimate a set of synthetic monoenergetic x-ray measurements. Another one of the remedies is the use of a metal artifact reduction (MAR) algorithm that replaces the corrupted projection measurements in the detector with interpolation from neighboring uncorrupted projections. In data starvation situations, the dual-energy methods are not effective. The current MAR algorithms are still immature, and they may create new artifacts while trying to suppress the metal artifacts. This is a renewal application of our previous R15 grant entitled “Fast and Robust Low-Dose X-Ray CT Image Reconstruction,” in which we have successfully developed image reconstruction algorithms to combat noise. Some of our ideas formed during the last three years can be further advanced into new ideas for metal artifact reduction in x-ray CT. In this R15 renewal, we will focus on new metal artifact reduction (MAR) algorithm development based on conventional single-energy data acquisition. The innovation is the new way to set up the objective functions for optimization. The uniqueness of our objective functions is that they do not have a data fidelity term; they only contain the Bayesian terms. The Bayesian terms are formed from the metal artifact features. A gradient descent algorithm is used to optimize the objective functions and a new set of un-corrupted measurements are estimated. The final image is reconstructed by the filtered backprojection (FBP) algorithm. The proposed algorithms are cost-effective. We hypothesize that the proposed methods will be more effective than the state-of-the-art MAR methods available for commercial CT scanners. This hypothesis will be carefully evaluated in this renewed R15. Our clinical collaborators at University of Utah HealthCare will work with us closely by providing us clinical data and professional evaluation advice. This R15 project provides Utah Valley University (UVU) students with hands-on opportunities and experiences of performing real-world research in the field of healthcare. It will stimulate the interests of students so that they consider a career in biomedical and bioengineering field/industry.