Perceptual sensitivity to anatomical background statistics in mammography

乳房X线照相术中对解剖背景统计的感知敏感性

• 批准号: 9804780
• 负责人: Jonathan D Victor
• 金额: $ 19.14万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9804780
• 关键词: Affect; Algorithms; Anatomy; Appearance; Basic Science; Breast; Breast Cancer Detection; Cancerous; Categories; Clinical; Communities; Complex; Data; Databases; Descriptor; Detection; Development; Devices; Diagnosis; Diagnostic radiologic examination; Digital Mammography; Disease; Dose; Elements; Engineering; Frequencies; Gaussian model; Goals; Hand; Human; Image; Individual; Infrastructure; Knowledge; Lead; Lesion; Magnetic Resonance Imaging; Mammographic screening; Mammography; Masks; Medical Imaging; Mind; Modeling; Motivation; Noise; Perception; Phase; Physiological; Probability; Process; Production; Property; Psychophysics; Reading; Research; Resolution; Roentgen Rays; Scientist; Stimulus; Structure; Sum; Testing; Texture; Translating; Variant; Vision; Vision research; Visual; Visual Perception; Visual system structure; Weight; Work; base; contrast imaging; density; design; diagnostic accuracy; digital; improved; instrumentation; malignant breast neoplasm; public health relevance; screening; signal processing; statistics; tool; vision science

Project Summary/Abstract It is well known that normal anatomy in a medical image can mask the presence of disease. However this process is not well understood. Part of the problem is that we lack knowledge of the relevant statistical descriptors that characterize perceptual effects of image statistics. While image acquisition noise is largely characterized by its second moments (power-spectrum or covariance matrix), background anatomy has a complex structure that requires higher-order statistics – and an understanding of their perceptual relevance –to characterize fully. This is an important limitation because reading “through” this background is a critical component of many clinical tasks. In a statistical sense, reading through the background means exploiting redundancies in the presentation of normal anatomical structures for the purpose of isolating disease processes. The need for background characterization is well recognized in screening mammography, our focus, as screening mammography typically includes an assessment of the background via the BIRADS density score. However, this score has limited utility as a statistical descriptor. The basis for this project is to translate a successful approach from basic vision science to medical imaging, in order to identify the relevant high-order statistical properties of medical images and their perceptual impact. In this approach, a set of local image statistics (co-occurrence probabilities) are used to build an ”alphabet” for the statistical structure of synthetic visual textures and their local features (such as edges). Perceptual sensitivities to local features can be concisely characterized and modeled via this alphabet, and it has been shown that sensitivity to these elements is matched to their informativeness in natural scenes. This motivates our general approach, and many specifics of our research plan. Our plan is to develop algorithms in Aim 1 that selectively alter (either increase or decrease) the co-occurrence statistics of mammograms, while retaining their general background appearance. The sub-aims explore four strategies, building on a Fourier domain approach for which a proof-of-principle is in hand. Then, Aim 2 will use these images to assess perceptual sensitivity. Aim 2A will develop the psychophysical paradigm. Aims 2B-D will determine whether the principles identified in previous studies of synthetic visual textures (sign-invariance, approximate scale-invariance, and quadratic combination) extend to medical images, as this will enable a comprehensive yet concise description of perceptual sensitivity. We will pursue these aims using a database of full-field digital mammograms. The project is expected to yield a validated approach for modulating high-order statistical properties of mammograms and baseline data of perceptual sensitivity to these modulations. These findings will improve our understanding how normal anatomy impacts the statistical properties of screening mammograms, and give us valuable baseline data on how the statistics of normal anatomy affect perception.

项目总结/摘要 众所周知，医学图像中的正常解剖结构可以掩盖疾病的存在。然而 这一过程还没有得到很好的理解。部分问题在于我们缺乏相关统计数据的知识， 描述符表征图像统计的感知效果。虽然图像采集噪声在很大程度上 其特征在于其二阶矩（功率谱或协方差矩阵），背景解剖具有 需要高阶统计的复杂结构-以及对它们的感知相关性的理解 - 充分描述。这是一个重要的限制，因为阅读“通过”这个背景是一个关键的 许多临床任务的组成部分。从统计学意义上讲，通过背景阅读意味着利用 为了隔离疾病，在正常解剖结构的表现上存在冗余 流程.背景表征的需要在筛查性乳腺X线摄影中得到了很好的认识，我们的 焦点，因为筛查性乳腺X线摄影通常包括通过BIRADS评估背景 密度分数然而，该分数作为统计描述符的效用有限。 这个项目的基础是将一种成功的方法从基础视觉科学转化为医学 成像，以便识别医学图像的相关高阶统计特性及其 知觉冲击在该方法中，使用一组局部图像统计（共现概率）来 为合成视觉纹理的统计结构及其局部特征（如 边缘）。对局部特征的感知敏感度可以通过这个来简明地表征和建模。 字母表，并且已经表明，对这些元素的敏感性与它们的信息量相匹配， 自然场景。这激发了我们的总体方法，以及我们研究计划的许多细节。 我们的计划是在目标1中开发算法，选择性地改变（增加或减少）乳房X线照片的共现统计数据，同时保留其一般背景外观。次级目标 探索四种策略，建立在傅立叶域方法的基础上，该方法的原理证明正在进行中。 然后，目标2将使用这些图像来评估感知灵敏度。目标2A将发展心理物理学 范例目的2B-D将确定在先前的合成药物研究中确定的原则是否适用。 视觉纹理（符号不变性，近似尺度不变性和二次组合）扩展到 医学图像，因为这将使得能够对感知灵敏度进行全面而简洁的描述。我们将 使用全视野数字乳房X线照片数据库来实现这些目标。 该项目预计将产生一个有效的方法来调制高阶统计特性 以及对这些调制的感知敏感度的基线数据。这些发现将 提高我们对正常解剖结构如何影响筛查统计特性的理解 乳房X光片，并为我们提供了关于正常解剖结构的统计数据如何影响感知的宝贵基线数据。