Model-Based Image Reconstruction for X-ray CT in Lung Imaging

肺部成像中基于模型的 X 射线 CT 图像重建

• 批准号: 8119605
• 负责人: JEFFREY A FESSLER
• 金额: $ 58.91万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8119605
• 关键词: Acceleration; Address; Algorithms; Architecture; Behavior; Benchmarking; Blinded; Clinical; Computer Simulation; Computers; Data; Diagnosis; Diagnostic; Disease; Dose; Drug Formulations; Electronics; Equilibrium; Evaluation; Feedback; Goals; Human; Image; Image Analysis; Lung; Lung diseases; Lung nodule; Mammary Gland Parenchyma; Maps; Measurement; Measures; Memory; Methods; Metric; Modeling; Morphologic artifacts; Motion; Nodule; Noise; Patients; Pattern; Performance; Photons; Physics; Process; Public Health; Radiation; Ramp; Relative (related person); Research; Resolution; Roentgen Rays; Scanning; Source; Spiral Computed Tomography; Statistical Models; System; Techniques; Time; Tomography, Computed, Scanners; Tube; Update; Width; X-Ray Computed Tomography; X-Ray Computed Tomography Scanners; base; design; image reconstruction; improved; lung imaging; novel; physical model; public health relevance; quantum; radiologist; reconstruction; shared memory; simulation; statistics; stem; tool

DESCRIPTION (provided by applicant): Model-Based Image Reconstruction for X-ray CT in Lung Imaging Modern X-ray computed tomography (CT) systems provide high-quality images for diagnosing numerous conditions including a variety of lung diseases. Unfortunately, technological advances in CT imaging have been accompanied by significant increases in X-ray radiation dose to patients. There is growing concern about the public health consequences of such doses. Furthermore, even with typical levels of radiation dose, current X-ray CT images have suboptimal image quality due to the limitations of the traditional image reconstruction algorithms used in clinical systems. We propose to develop, implement, analyze and evaluate model-based image reconstruction (MBIR) methods for X-ray CT to improve image quality in lung imaging and to reduce patient dose. Unlike commercially available denoising methods, the proposed MBIR methods are based on accurate models for the physics and statistics of X-ray CT systems. The methods will use edge-preserving regularization that is tailored to lung scans to control noise while improving spatial resolution. We will develop techniques for accelerating the iterative algorithms used in MBIR methods. The methods will be evaluated using computer simulations, phantom studies, and human studies. Specifically, we will focus here on lung CT applications, including morphological characterization of lung nodules and assessment of pulmonary diseases. The clinical impact of MBIR methods will be studied using automated lung image analysis tools and radiologist observer studies. PUBLIC HEALTH RELEVANCE: The relevance of this research to public health is that we will develop and evaluate sophisticated techniques for processing the raw data measured by X-ray CT scanners to dramatically reduce the X-ray radiation dose to patients and to further improve the image quality in lung CT imaging for more accurate diagnosis and treatment.

描述（由申请人提供）：肺成像中X射线CT的基于模型的图像重建现代X射线计算机断层扫描（CT）系统提供了高质量的图像，用于诊断许多疾病，包括各种肺部疾病。不幸的是，CT成像的技术进步伴随着X射线辐射剂量的显着增加。人们对这种剂量的公共卫生后果越来越关注。此外，即使在典型的辐射剂量下，由于临床系统中使用的传统图像重建算法的局限性，当前的X射线CT图像具有次优质量。 我们建议开发，实施，分析和评估X射线CT的基于模型的图像重建方法（MBIR）方法，以提高肺成像中的图像质量并减少患者剂量。与市售的非授权方法不同，所提出的MBIR方法基于X射线CT系统物理和统计的准确模型。这些方法将使用针对肺部扫描量身定制的边缘保护正则化，以控制噪声，同时改善空间分辨率。我们将开发用于加速MBIR方法中使用的迭代算法的技术。该方法将使用计算机模拟，幻影研究和人类研究来评估。具体而言，我们将在此关注肺CT应用，包括肺结节的形态表征和评估肺部疾病。 MBIR方法的临床影响将使用自动肺图像分析工具和放射科医师观察者研究研究。 公共卫生相关性：这项研究与公共卫生的相关性是，我们将开发和评估通过X射线CT扫描仪测量的原始数据的复杂技术，以大大减少X射线辐射剂量，以进一步提高肺CT成像中的图像质量，以进行更准确的诊断和治疗。