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: 8293142
• 负责人: JEFFREY A FESSLER
• 金额: $ 59.23万
• 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 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8293142
• 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）：用于肺部成像中的X射线CT的基于模型的图像重建现代X射线计算机断层摄影（CT）系统提供用于诊断包括各种肺部疾病的多种状况的高质量图像。不幸的是，CT成像的技术进步伴随着对患者的X射线辐射剂量的显著增加。人们越来越担心这种剂量的公共健康后果。此外，即使具有典型的辐射剂量水平，由于临床系统中使用的传统图像重建算法的限制，当前的X射线CT图像也具有次优的图像质量。 我们建议开发，实施，分析和评估基于模型的图像重建（MBIR）方法的X射线CT，以提高图像质量在肺部成像，并减少病人的剂量。与商业上可用的去噪方法不同，所提出的MBIR方法基于X射线CT系统的物理和统计的精确模型。该方法将使用针对肺部扫描定制的边缘保持正则化来控制噪声，同时提高空间分辨率。我们将开发用于加速MBIR方法中使用的迭代算法的技术。将使用计算机模拟、体模研究和人体研究对这些方法进行评价。具体来说，我们将集中在肺部CT应用，包括肺结节的形态特征和肺部疾病的评估。将使用自动肺部图像分析工具和放射科医生观察员研究来研究MBIR方法的临床影响。 公共卫生关系：这项研究与公共卫生的相关性在于，我们将开发和评估用于处理X射线CT扫描仪测量的原始数据的复杂技术，以大幅降低患者的X射线辐射剂量，并进一步提高肺部CT成像的图像质量，以实现更准确的诊断和治疗。

项目成果

A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction.

• DOI: 10.1109/tmi.2011.2175233
• 发表时间: 2012-03
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Ramani S;Fessler JA
• 通讯作者: Fessler JA

Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.

• DOI: 10.1109/tmi.2013.2266898
• 发表时间: 2013-11
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Kim D;Pal D;Thibault JB;Fessler JA
• 通讯作者: Fessler JA

ALGORITHMS FOR AREA PRESERVING FLOWS.

• DOI: 10.1137/100815542
• 发表时间: 2011
• 期刊: SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics
• 作者: Kublik C;Esedoḡlu S;Fessler JA
• 通讯作者: Fessler JA

Forward-Projection Architecture for Fast Iterative Image Reconstruction in X-ray CT.

• DOI: 10.1109/tsp.2012.2208636
• 发表时间: 2012-10
• 期刊: IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society
• 作者: Kim JK;Fessler JA;Zhang Z
• 通讯作者: Zhang Z