Bayesian image analysis in Fourier space

傅立叶空间中的贝叶斯图像分析

• 批准号: 9465461
• 负责人: John Kornak
• 金额: $ 35.66万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9465461
• 关键词: Algorithms; Bayesian Modeling; Big Data; Blinded; Brain; Breast Cancer Detection; Characteristics; Classification; Clinical; Clinical Research; Code; Communities; Computer software; Computer-Assisted Image Analysis; Computers; Coupled; Data Set; Databases; Detection; Development; Diagnosis; Diagnostic; Dimensions; Disease; Engineering; Equilibrium; Evaluation; Family; Fracture; Frequencies; Frontotemporal Dementia; Gold; Grant; Image; Image Analysis; Image Enhancement; Imaging Phantoms; Imaging problem; Journals; Knowledge; Libraries; Location; Magnetic Resonance Imaging; Mammary Neoplasms; Measures; Medical Imaging; Methods; Modeling; Noise; Patients; Pattern; Perfusion; Plant Roots; Process; Pythons; Radiology Specialty; Research; Research Personnel; Scientist; Series; Signal Transduction; Societies; Source; Space Models; Specific qualifier value; Speed; Structure; To specify; Treatment Efficacy; Variant; Weight; base; clinical imaging; computer science; data mining; design; diagnosis standard; diagnostic accuracy; expectation; high dimensionality; image archival system; image processing; imaging study; improved; innovation; novel; novel strategies; online resource; open source; radiological imaging; radiologist; repository; screening; simulation; tumor; web site

Project Summary/Abstract: “Bayesian image analysis in Fourier space (BIFS)” The objective of this grant is to develop a family of new medical imaging processing methods that will advance the state-of-the art in radiological research and diagnostic evaluation. We will improve on commonly used spatial filtering methods, Fourier/wavelet shrinkage/sparsity methods, and conventional Bayesian image analysis methods by reformulating Bayesian Image analysis in Fourier Space (BIFS), i.e., by modeling in terms of spatial frequencies. Spatially correlated prior distributions that are difficult to model and compute in conventional image space can be more efficiently modeled as a set of independent processes across Fourier space. The originally inter-correlated and high-dimensional problem in image space is thereby broken down into a series of independent one-dimensional problems in Fourier space, where the independent distributions are tied together by our development of a parameter function over Fourier space. This independence over Fourier space structure coupled with the concept of the parameter function will enable us to develop new families of powerful and easy to specify BIFS models with fast algorithms to compute posterior image estimates. We will disseminate these methods to imaging researchers and clinical practitioners by developing a BIFS software package designed to solve a wide range of medical image processing problems. The specific aims of the proposal are: 1) to develop novel BIFS models targeted to provide improved solutions to medical image processing problems; 2) to validate benefits of BIFS models compared with conventional processing methods based on careful simulation and phantom imaging studies; 3) to clinically validate improvements generated by BIFS modeling in two ongoing clinical studies: a) quantification of brain perfusion patterns associated with frontotemporal lobar dementia, and b) detection of breast cancer in MRIs; and 4) to disseminate the methods to the wider clinical and scientific communities by generating and releasing an R package for the implementation of BIFS. Our innovative BIFS modeling approach has the potential to make a big difference in the field of medical image processing problems and image analysis in general. The impact on radiological application through the development of new, readily implementable and fast-to-compute BIFS models will lead to increased accuracy for the detection of findings in radiological screening and diagnosis; improved assessment of treatment efficacy; increased accuracy for measuring disease effects in imaging-based clinical studies; and expanded potential for practical image data-mining.

项目摘要/摘要：“傅立叶空间中的贝叶斯图像分析（BIFS）” 这项资助的目的是开发一系列新的医学成像处理方法， 推进放射学研究和诊断评估的最新发展。我们将共同改进 使用空间滤波方法、傅立叶/小波收缩/稀疏方法和常规贝叶斯图像 分析方法通过重新制定贝叶斯图像分析在傅立叶空间（BIFS），即，通过建模 空间频率。空间相关的先验分布，难以建模和计算， 传统的图像空间可以更有效地建模为一组独立的过程， 空间原来图像空间中的相互关联和高维问题由此被分解 转化为傅立叶空间中一系列独立的一维问题，其中独立分布 通过我们在傅立叶空间上的参数函数的发展联系在一起。这种独立性 傅立叶空间结构与参数函数的概念相结合，将使我们能够开发新的 功能强大且易于指定的BIFS模型系列，具有快速算法来计算后验图像 估算我们将传播这些方法，以成像研究人员和临床从业人员， 一个BIFS软件包，旨在解决广泛的医学图像处理问题。 该提案的具体目标是：1）开发新的BIFS模型，旨在提供改进的 医学图像处理问题的解决方案; 2）验证BIFS模型与 基于仔细模拟和体模成像研究的常规处理方法; 3）临床上 在两项正在进行的临床研究中验证BIFS建模所产生的改进：a）大脑的定量 与额颞叶痴呆相关的灌注模式，和B）MRI中乳腺癌的检测; 以及4）通过生成和发布这些方法， 一个用于实现BIFS的R包。 我们创新的BIFS建模方法有可能在医疗领域产生重大影响 图像处理问题和图像分析。对放射性应用的影响 开发新的、易于实现的和快速计算的BIFS模型将提高精度 用于检测放射筛查和诊断中的发现;改善治疗评估 有效性;提高基于成像的临床研究中测量疾病影响的准确性;并扩大 实际图像数据挖掘的潜力。