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

基本信息

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
项目类别：
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

项目摘要

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）：肺部成像中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）系统提供用于诊断包括各种肺部疾病的多种病症的高质量图像。不幸的是，随着 CT 成像技术的进步，患者的 X 射线辐射剂量显着增加。人们越来越担心此类剂量对公共健康的影响。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，当前的 X 射线 CT 图像的图像质量也不是最佳的。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并减少患者剂量。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。这些方法将使用针对肺部扫描定制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、模型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 应用，包括肺结节的形态特征和肺部疾病的评估。 MBIR 方法的临床影响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。公共健康相关性：这项研究与公共健康的相关性在于，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，以显着减少患者的 X 射线辐射剂量并进一步改善图像肺部 CT 成像质量，以实现更准确的诊断和治疗。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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
影响因子：
0
作者：
Kim JK;Fessler JA;Zhang Z
通讯作者：
Zhang Z