Spectral CT metal artifact correction

项目总结 放射治疗计划会受到金属物体的严重影响，例如 植入物和矫形五金。金属物体会导致计算机断层扫描(CT)图像中的伪影 模糊解剖结构和改变CT数字，这两个都是准确估计 计划放射治疗的目的。这些不确定性可能导致肿瘤剂量不足和剂量过大 健康的组织。现有的金属伪影减少技术不能完全消除由金属产生的所有伪影 对象，并且已知会引入新的构件。该项目将开发一种光谱CT成像方法来 减少金属伪影，同时保持CT数字的准确性和软组织对比度。我们建议减少 CT成像中的金属伪影，使用最先进的采集技术，结合优化- 基于框架的重建。我们提出了一种有约束的一步谱CT图像重建方法 (COSSCIR)算法的前期工作和初步研究证明了该算法的可行性 将金属伪影减少到&lt；8HU误差。将物理效果合并到数据模型中是一种方法 通过该算法减少了金属伪影。我们团队开发的优化框架是独一无二的 结合了一些约束，可缓解由于通过金属的不可靠测量而导致的欠采样 并且还实现了将减少不可靠测量的数量的采集方法。这些方法 旨在广泛和自动地纠正金属伪影，而不需要了解植入物 材料。该项目的目标是在保持软组织对比度和CT数的同时减少金属伪影 将通过进一步开发cOSSCIR算法并研究其在这两个领域的应用来实现精度 使用模拟、模体实验和临床的双千伏和光子计数光谱采集方法 光子计数CT数据集。该算法还将针对放射治疗计划的任务进行评估 使用模拟和体模实验对存在髋关节假体的前列腺癌进行研究。这个 将开发的光谱CT金属伪影校正方法与金标准图像和 建立了金属伪影减少技术。成功完成项目目标将产生一种方法 为了减少CT图像中的金属伪影，同时保持软组织对比度和CT数值准确性 在模拟和实验体模数据上进行了验证。

PROJECT SUMMARY Radiation therapy treatment planning can be severely impacted by the presence of metal objects such as implants and orthopaedic hardware. Metal objects cause artifacts in computed tomography (CT) images that obscure anatomical structures and alter the CT numbers, both of which are critical to estimate accurately for the purpose of planning radiation therapy. These uncertainties can cause underdosing of tumors and overdosing of healthy tissue. Existing metal artifact reduction techniques do not fully mitigate all artifacts created by the metal objects and are known to introduce new artifacts. This project will develop a spectral CT imaging method to reduce metal artifacts while maintaining CT number accuracy and soft tissue contrast. We propose to reduce metal artifacts in CT imaging by using state-of-the-art acquisition techniques, combined with an optimization- based reconstruction framework. We developed a constrained `one-step' spectral CT image reconstruction (cOSSCIR) algorithm in previous work and preliminary studies demonstrate feasibility of the proposed algorithm to reduce metal artifacts to <8 HU error. The incorporation of physical effects into the data model is one method by which the algorithm reduces metal artifacts. The optimization framework developed by our group uniquely incorporates constraints that mitigate undersampling due to unreliable measurements that pass through metal and also enable acquisition approaches that will reduce the number of unreliable measurements. The methods are designed to correct metal artifacts broadly and automatically without requiring knowledge of the implant material. The project objective to reduce metal artifacts while maintaining soft tissue contrast and CT number accuracy will be achieved by further developing the cOSSCIR algorithm and investigating its application to both dual-kV and photon-counting spectral acquisition methods using simulations, phantom experiments, and clinical photon-counting CT datasets. The algorithm will also be evaluated relative to task of radiation therapy planning for prostate cancer in the presence of hip prostheses using simulations and phantom experiments. The developed spectral CT metal artifact correction method will be compared to gold-standard images and an established metal artifact reduction technique. Successful completion of the project aims will result in a method to reduce metal artifacts in CT images while maintaining soft tissue contrast and CT number accuracy that has been validated on simulated and experimental phantom data.