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.

项目摘要 放射治疗治疗计划可能会受到金属物体的存在（例如 植入物和骨科硬件。金属物体在计算机断层扫描（CT）图像中引起伪影 掩盖解剖结构并改变CT数，这两个对于准确估算的至关重要 计划辐射疗法的目的。这些不确定性会导致肿瘤的剂量不足和过量服用 健康的组织。现有的金属伪影技术并不能完全减轻金属创建的所有工件 物体并众所周知会引入新的文物。该项目将开发一种光谱CT成像方法 减少金属伪像，同时保持CT数量的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，将CT成像中的金属伪影与优化结合在一起 基于重建框架。我们开发了受约束的“一步”光谱CT图像重建 （Cosscir）先前工作和初步研究的算法证明了所提出的算法的可行性 将金属伪像降低到<8 HU误差。将物理效应纳入数据模型是一种方法 通过该算法减少金属伪像。我们小组独特开发的优化框架 结合了由于不可靠的测量而导致的限制，这些限制可以减轻不可靠的测量 还可以实现采集方法，以减少不可靠的测量数量。方法 旨在在不需要植入物知识的情况下广泛，自动纠正金属伪像 材料。该项目的目标是减少金属伪像，同时保持软组织对比度和CT数字 通过进一步开发Cosscir算法并调查其对两者的应用，将实现准确性 使用模拟，幻影实验和临床的双KV和光子计数频谱采集方法 光子计数CT数据集。该算法还将相对于放射治疗计划的任务进行评估 使用模拟和幻影实验在髋关节假体的存在下进行前列腺癌。这 将将开发的光谱CT金属伪像校正方法与金标准图像进行比较 已建立的金属伪像还原技术。成功完成项目目标将导致一种方法 减少CT图像中的金属伪影，同时保持软组织对比度和CT数量精度 已在模拟和实验幻影数据上进行了验证。

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