Quantitative Evaluation of Reconstruction Algorithms - Resubmission 01

描述(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CT、MRI、PET、SPECT等三维成像技术中起着举足轻重的作用，因此，对重建算法的研究也在不断深入。这类研究的一个局限性是缺乏与图像诊断质量相关的定量评估范例。该项目的目标是确定使用重建图像的定量特征分析作为替代来实际测量放射科医生对重建图像的诊断性能的可行性。目前的方法是定性的，特别的，或定量的，但不一定与诊断质量有关。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和正常解剖结构，并区分不同类型的病理。这项技能是在多年的训练和经验中发展起来的。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。我们将在提取和分析图像特征方面积累20多年的经验，以开发计算机辅助诊断方案。我们建议使用特征分析技术来衡量重建图像的质量。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，那么我们就会证明 利用定量特征分析对重建算法进行评价是可行的。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，以衡量放射科医生在使用不同算法重建的图像中区分良恶性病变的能力。我们将使用这些数据库和观察者研究来开发一套与放射科医生在对乳腺病变进行分类时的表现相关的定量图像特征。如果我们成功了，那么随着进一步的发展，我们的方法可以用于优化重建算法和评估剂量减少技术。

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.