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

描述（由申请人提供）：基于模型的肺成像x射线CT图像重建现代x射线计算机断层扫描（CT）系统为诊断多种疾病提供高质量图像，包括各种肺部疾病。不幸的是，CT成像技术的进步伴随着患者x射线辐射剂量的显著增加。人们越来越关注这类剂量对公众健康造成的后果。此外，即使在典型的辐射剂量水平下，由于临床系统中使用的传统图像重建算法的局限性，目前的x射线CT图像的图像质量也不理想。我们建议开发、实施、分析和评估基于模型的x射线CT图像重建（MBIR）方法，以提高肺部成像的图像质量，减少患者的剂量。与商业上可用的去噪方法不同，所提出的MBIR方法是基于x射线CT系统的物理和统计的精确模型。该方法将使用适合肺部扫描的边缘保留正则化来控制噪声，同时提高空间分辨率。我们将开发加速MBIR方法中使用的迭代算法的技术。这些方法将通过计算机模拟、模拟研究和人体研究进行评估。具体来说，我们将重点关注肺部CT的应用，包括肺结节的形态学特征和肺部疾病的评估。将使用自动肺图像分析工具和放射科医师观察研究来研究MBIR方法的临床影响。

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.