Model-Based Reconstruction for High-Spatial Resolution Cone-Beam Computed Tomography

基于模型的高空间分辨率锥形束计算机断层扫描重建

• 批准号: 9259452
• 负责人: Steven Wayne Tilley
• 金额: $ 4.4万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9259452
• 关键词: Algorithms; Attention; Biological Models; Breast Cancer Detection; Breast Microcalcification; Clinical; Clinical Research; Computer Simulation; Computer software; Data; Degenerative polyarthritis; Dental; Detection; Diagnosis; Diagnostic radiologic examination; Dose; Electronics; Ensure; Equilibrium; Future; Geometry; Goals; Gold; Image; Image Analysis; Investigation; Least-Squares Analysis; Light; Limb structure; Mammography; Measurement; Measures; Methodology; Methods; Modality; Modeling; Morphology; Motion; Nature; Noise; Otolaryngology; Performance; Positron-Emission Tomography; Property; Radiation; Radiation therapy; Research; Resolution; Roentgen Rays; Rotation; Sampling; Scanning; Series; Source; Spottings; Statistical Models; Structure; System; Testing; Variant; Work; analytical method; base; bone; bone imaging; bone visualization; clinical application; clinical efficacy; clinical imaging; clinically relevant; cone-beam computed tomography; design; detector; experimental study; falls; high resolution imaging; image reconstruction; improved; in vivo imaging; mathematical model; microCT; novel; nuclear imaging; point of care; portability; reconstruction; response; simulation; single photon emission computed tomography; substantia spongiosa; success; tomography; tool

Project Summary The main goal of this work is to improve image quality, particularly resolution, of flat-panel cone-beam CT (fpCBCT) systems. This modality is promising for its high resolution, adaptability to different geometries, and portability. In particular, fpCBCT is being investigated for use in bone-morphology quantification and microcal- cification detection, which are important in the study of osteoarthritis and detection breast cancer, respectively. These tasks require high resolution to visualize trabecular bone and microcalcifications, and while fpCBCT resolu- tion is superior to that of multi-detector CT, it is often unable to resolve these structures with sufficient detail. This work aims to improve resolution by modeling different system blur and noise properties, and incorporating these models in a model-based iterative reconstruction (MBIR) algorithm. MBIR methods have been increasingly pop- ular in tomography due to their ability to generate higher quality images than traditional analytical methods. This is largely due to the fact that MBIR methods include a noise model, a feature lacking in analytical methods such as filtered backprojection. A MBIR method with an accurate flat-panel-specific mathematical model will result in higher spatial resolution reconstructions. The following sources of blur will be measured and modeled on multiple fpCBCT test benches: the extended X-Ray focal spot, the detector scintillator, detector lag, and gantry motion. Noise correlation resulting from these blurs or readout electronics will also be measured and modeled. Particular attention will be paid to the shift-variant nature of these blurs which, along with noise correlation, has been tradi- tionally overlooked in current CT and fpCBCT reconstruction methods. These models will balance accuracy with computationally efficient so they may be used in iterative methods. Novel MBIR methods will be developed to in- corporate a wide range of system models, including those developed in this work. These algorithms will be used to reconstruct data acquired in simulation and on multiple fpCBCT test benches. The importance of each blur/noise model will be evaluated with a range of system properties and acquisition settings. Data will be reconstructed using the new, more accurate methods/models and traditional methods/models for comparison. Image quality will be assessed using a variety of metrics, including resolution, spatial noise, segmentation quality, modulation transfer functions, and noise power spectra. For example, the ability to segment trabecular bone will provide a clinically relevant image quality metric. Thus, this work will result in novel MBIR methods, detailed models of blur and noise correlation applicable to many fpCBCT systems, and a detailed analysis the image quality improve- ments resulting from these methods. While this work will focus on high-resolution imaging of bone morphology, it will improve the ability of fpCBCT to accomplish other high-resolution clinical tasks by improving resolution in current systems. Additionally, this work will provide a software solution to hardware induced image quality limita- tions, providing avenues not only to extend imaging performance in currently available system, but also to provide improved trade-offs and a way to relax hardware constraints in the design of future fpCBCT systems.

项目摘要 这项工作的主要目的是提高平板锥束CT的成像质量，特别是分辨率 (FpCBCT)系统。这种模式因其高分辨率、对不同几何形状的适应性以及 可移植性。特别是，fpCBCT正在被研究用于骨形态量化和微钙化。 酸化检测，分别在骨关节炎和乳腺癌的研究中是重要的。 这些任务需要高分辨率才能显示骨小梁和微钙化，同时fpCBCT可以分辨- CT成像优于多层螺旋CT，但往往不能很好地分辨这些结构的细节。这 这项工作的目的是通过对不同的系统模糊和噪声属性进行建模并结合这些属性来提高分辨率 基于模型的迭代重建(MBIR)算法中的模型。MBIR方法越来越流行- 由于它们能够生成比传统分析方法更高质量的图像，因此它们在层析成像中具有很高的分辨率。这 这在很大程度上是因为MBIR方法包括噪声模型，这是分析方法所缺乏的一个特征 作为经过滤波的反投影。具有精确的平板特定数学模型的MBIR方法将导致 更高的空间分辨率重建。将对以下模糊源进行测量并以多个 FpCBCT测试台：扩展X射线焦点、探测器闪烁体、探测器滞后和龙门运动。 由这些模糊或读出电子产生的噪声相关性也将被测量和建模。特例 我们将注意到这些模糊的移位变化特性，以及噪声相关性。 在目前的CT和fpCBCT重建方法中往往被忽视。这些模型将在精确度和 计算效率高，因此可以在迭代方法中使用。新的MBIR方法将被开发成在- 公司广泛的系统模型，包括在这项工作中开发的那些。这些算法将用于 重建在模拟和多个fpCBCT试验台上采集的数据。每个模糊/噪声的重要性 模型将使用一系列系统属性和采购设置进行评估。数据将被重建 使用新的、更准确的方法/模型与传统方法/模型进行比较。图像质量 将使用各种指标进行评估，包括分辨率、空间噪声、分割质量、调制 传递函数和噪声功率谱。例如，分割松质骨的能力将提供 临床相关的图像质量指标。因此，这项工作将导致新的MBIR方法，模糊的详细模型 和噪声相关，适用于多种fpCBCT系统，并详细分析了图像质量的改善-- 由这些方法产生的费用。虽然这项工作将集中在骨骼形态的高分辨率成像上， 它将通过提高分辨率来提高fpCBCT完成其他高分辨率临床任务的能力 当前的系统。此外，这项工作还将为解决硬件导致的图像质量限制提供软件解决方案。 不仅提供了扩展当前可用系统中的成像性能的途径，而且还提供了 在未来的fpCBCT系统设计中，改进了权衡和放宽硬件限制的方法。