Quantitative Evaluation of Reconstruction Algorithms - Resubmission 01

重建算法的定量评估-补交01

• 批准号: 8517718
• 负责人: ROBERT M NISHIKAWA
• 金额: $ 17.99万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8517718
• 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.

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。在任何三维成像方式中，如CT、MRI、PET、SPECT等，图像重建都起着至关重要的作用。因此，开发和优化重建算法的研究正在进行中。这种研究的一个局限是缺乏与图像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替代的可行性，以实际测量放射科医生对重建图像的诊断性能。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和正常解剖结构，并区分不同类型的病理。这项技能是经过多年的训练和经验发展起来的。我们建议提取病灶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，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，那么我们就已经证明了

项目成果

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification.

• DOI: 10.1002/mp.13054
• 发表时间: 2018-06-19
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Lee J;Nishikawa RM;Reiser I;Boone JM
• 通讯作者: Boone JM