Quantitative Evaluation of Reconstruction Algorithms - Resubmission 01

重建算法的定量评估-补交01

• 批准号: 8384340
• 负责人: ROBERT M NISHIKAWA
• 金额: $ 23.7万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8384340
• 关键词: Algorithms; Anatomy; Appearance; Benign; Breast; Characteristics; Clinical; Colon; Complex; Computer-Assisted Diagnosis; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Dose; Funding; Goals; Image; Image Analysis; Lesion; Liver; Lung; Magnetic Resonance Imaging; Malignant - descriptor; Mammography; Measures; Medical Imaging; Methods; Metric; Modality; Noise; Pathology; Performance; Play; Positron-Emission Tomography; Property; Qualitative Methods; Quantitative Evaluations; Radiation; Reading; Research; Resolution; Role; Scheme; Simulate; System; Techniques; Testing; Three-Dimensional Imaging; Training; breast lesion; detector; experience; image processing; image reconstruction; imaging modality; radiologist; reconstruction; single photon emission computed tomography; skills

DESCRIPTION (provided by applicant): The long-term goal of this research is to develop a method to quantify the diagnostic image quality of CT images. Image reconstruction plays a pivotal role in any 3D imaging modality, such as CT, MRI, PET, SPECT, etc. As a result, there is ongoing research into developing and optimizing reconstruction algorithms. One limitation of such research is the lack of a quantitative evaluation paradigm that is related/correlated to the diagnostic quality of the image. The goal of this project is to determine the feasibility of using quantitative feature analysis of the reconstructed image as a surrogate for actually measuring radiologists' diagnostic performance on the reconstructed images. Currently methods are qualitative, ad hoc, or quantitative, but not necessarily related to diagnostic quality. When interpreting an image, radiologists use features of lesions in an image to distinguish actual disease from normal anatomy and also to distinguish between different types of pathology. This skill is developed over years of training and experience. We propose to extract quantitative features of lesions to assess the diagnostic quality of a reconstructed image. We will build on over 20 years of experience in extracting and analyzing image features to develop computer-aided diagnosis schemes. We propose to use the feature analysis techniques as a measure of the quality of a reconstructed image. Our hypothesis is that quantitative feature analysis is correlated to diagnostic performance of radiologists. If this is true, then we will have shown that it is feasible to use quantitative feature analysis to evaluate reconstruction algorithms. Specifically in this project, we will develop two databases one containing clinical breast CT images and the other simulated breast CT images with simulated lesions. We will perform an observer study using the clinical images to measure radiologists ability to characterize benign from malignant lesions in images reconstructed using different algorithms. We will use these databases and the observer study to develop a set of quantitative image features that correlate with radiologists' performance in classifying breast lesions. If we are successful, then our method can, with further development, be used to optimize reconstruction algorithms and evaluate dose reduction techniques. PUBLIC HEALTH RELEVANCE: CT reconstruction algorithms are the ultimate factor in image quality and they dictate the final appearance of image that the radiologist will interpret. Unfortunately, no method exists to quantitatively compare reconstruction algorithms in terms of diagnostic image quality (i.e., the ability of radiologists to make the correct diagnosis). This project will determine the feasibility of using quantitative features extracted from reconstructed images to evaluate the diagnostic quality produced from different reconstruction algorithms.

描述（由申请人提供）：本研究的长期目标是开发一种量化 CT 图像诊断图像质量的方法。图像重建在任何 3D 成像模式（例如 CT、MRI、PET、SPECT 等）中都发挥着关键作用。因此，开发和优化重建算法的研究正在进行中。此类研究的一个局限性是缺乏与图像的诊断质量相关的定量评估范式。该项目的目标是确定使用重建图像的定量特征分析作为实际测量放射科医生对重建图像的诊断性能的替代方法的可行性。目前的方法是定性的、临时的或定量的，但不一定与诊断质量相关。在解释图像时，放射科医生使用图像中病变的特征来区分实际疾病与正常解剖结构，并区分不同类型的病理。这项技能是通过多年的培训和经验而发展起来的。我们建议提取病变的定量特征来评估重建图像的诊断质量。我们将利用 20 多年提取和分析图像特征的经验来开发计算机辅助诊断方案。我们建议使用特征分析技术作为重建图像质量的衡量标准。我们的假设是定量特征分析与放射科医生的诊断表现相关。如果这是真的，那么我们将证明 利用定量特征分析来评估重建算法是可行的。具体来说，在这个项目中，我们将开发两个数据库，一个包含临床乳腺 CT 图像，另一个包含模拟病变的模拟乳腺 CT 图像。我们将使用临床图像进行观察者研究，以测量放射科医生在使用不同算法重建的图像中区分良性和恶性病变的能力。我们将使用这些数据库和观察者研究来开发一组定量图像特征，这些特征与放射科医生对乳腺病变分类的表现相关。如果我们成功了，那么我们的方法经过进一步发展，可以用于优化重建算法和评估剂量减少技术。 公共健康相关性：CT 重建算法是图像质量的最终因素，它们决定了放射科医生将解释的图像的最终外观。不幸的是，没有方法可以在诊断图像质量（即放射科医生做出正确诊断的能力）方面定量比较重建算法。该项目将确定使用从重建图像中提取的定量特征来评估不同重建算法产生的诊断质量的可行性。