Spectral CT metal artifact correction

能谱CT金属伪影校正

• 批准号: 10372913
• 负责人: Taly Gilat Schmidt
• 金额: $ 27.75万
• 依托单位: MARQUETTE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372913
• 关键词: Algorithms; Anatomy; Calibration; Clinical; Data; Data Set; Dental; Development; Diagnostic; Diagnostic Imaging; Dose; Gold; Hip Prosthesis; Image; Image Analysis; Implant; Knowledge; Malignant neoplasm of prostate; Maps; Measurement; Metals; Methods; Morphologic artifacts; Noise; Normal tissue morphology; Organ; Orthopedic Procedures; Orthopedics; Overdose; Pathology; Pelvis; Performance; Photons; Prevalence; Prostate Cancer therapy; Radiation Dose Unit; Radiation therapy; Risk; Roentgen Rays; Source; Starvation; System; Techniques; Technology; Tissues; Uncertainty; Variant; Work; X-Ray Computed Tomography; base; data modeling; design; experimental study; image reconstruction; imaging modality; implant material; improved; in vivo; irradiation; novel; photon-counting detector; physical model; preservation; prototype; public health relevance; reconstruction; research clinical testing; simulation; soft tissue; treatment planning; tumor

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.

项目概要 放射治疗的治疗计划可能会因金属物体的存在而受到严重影响，例如 植入物和矫形硬件。金属物体会导致计算机断层扫描 (CT) 图像出现伪影 模糊解剖结构并改变 CT 数字，这两者对于准确估计 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肿瘤剂量过量 健康的组织。现有的金属伪影减少技术并不能完全减轻金属产生的所有伪影 对象并已知会引入新的工件。该项目将开发一种能谱 CT 成像方法 减少金属伪影，同时保持 CT 数字准确性和软组织对比度。我们建议减少 通过使用最先进的采集技术并结合优化，消除 CT 成像中的金属伪影 为基础的重建框架。我们开发了一种受限的“一步式”能谱 CT 图像重建 （cOSSCIR）算法在之前的工作和初步研究中证明了所提出算法的可行性 将金属伪影减少到 <8 HU 误差。将物理效应纳入数据模型是一种方法 该算法通过该算法减少了金属伪影。我组独有研发的优化框架 包含了一些约束，可以减轻由于穿过金属的不可靠测量而导致的采样不足 还可以采用采集方法来减少不可靠测量的数量。方法 旨在广泛且自动地纠正金属伪影，无需了解植入物 材料。该项目的目标是减少金属伪影，同时保持软组织对比度和 CT 数量 通过进一步开发 coSSCIR 算法并研究其在两者中的应用，可以实现准确性 使用模拟、模型实验和临床的双 kV 和光子计数光谱采集方法 光子计数 CT 数据集。该算法还将根据放射治疗计划的任务进行评估 使用模拟和模型实验在髋关节假体存在的情况下治疗前列腺癌。这 开发的光谱 CT 金属伪影校正方法将与金标准图像和 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 减少 CT 图像中的金属伪影，同时保持软组织对比度和 CT 数字精度 已在模拟和实验模型数据上得到验证。

项目成果

Experimental study of photon-counting CT neural network material decomposition under conditions of pulse pileup.

• DOI: 10.1117/1.jmi.8.1.013502
• 发表时间: 2021-01
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Jenkins PJB;Schmidt TG
• 通讯作者: Schmidt TG

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.

来自硅光子计数原型的头部 CT 数据的约束一步材料分解重建。

• DOI: 10.1002/mp.16649
• 发表时间: 2023
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Schmidt,TalyGilat;Sidky,EmilY;Pan,Xiaochuan;Barber,RinaFoygel;Grönberg,Fredrik;Sjölin,Martin;Danielsson,Mats
• 通讯作者: Danielsson,Mats