Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering

使用半球阵列和逆散射对乳房进行超声成像

基本信息

  • 批准号:
    8545500
  • 负责人:
  • 金额:
    $ 4.34万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-30 至 2014-07-31
  • 项目状态:
    已结题

项目摘要

Project Overview and Rationale for an Administrative Supplement The hemispheric array ultrasound breast imaging system currently being developed at the University of Rochester with the support of NIH grant R01 EB009692 is a unique facility comprised of a data-acquisition apparatus and a hybrid data-acquisition and high-performance computer network. The system leverages significant advances in ultrasound transducer arrays, front-end electronics, digital technology, and theoretical breakthroughs in inverse scattering to provide speckle-free, high- resolution, quantitative images of intrinsic tissue characteristics, i.e., sound speed and attenuation slope. The system is designed to acquire data during a two -second interval by using 10,240 parallel channels for transmission and reception and to image the entire breast volume with isotropic point resolution as good as the lateral resolution of x-ray mammography by use of a novel reconstruction algorithm. Using non-ionizing ultrasound, this system permits risk-free examination of the breast for cancer detection and overcomes limitations of x-ray mammography such as low resolution of contrast in dense breast, distortion and discomfort resulting from compression-induced deformation of anatomy, and poor imaging of breasts with implants. Demonstration of success would ultimately change the way screening for breast cancer is performed and significantly improve detection, diagnosis, and monitoring of response to treatment of breast cancer. An important theoretical development ¿ a major breakthrough ¿ has occurred since the original grant was awarded. This breakthrough allows our unique reconstruction algorithm to reconstruct different subvolumes independently, i.e., in parallel. At the same time that our algorithm was being extended, graphical processing units (GPUs) were evolving into powerful parallel computational engines. These GPUs allow small high-performance computer systems (HPCs) to perform massively parallel computations that are ideally suited for implementation of our parallelized reconstruction algorithm. Incorporation of GPUs in the computing nodes of our system endows the hybrid computer network with a computational capability comparable to that of the large, federally-supported national supercomputer facilities that were originally intended to be used for reconstructions. A GPU-based HPC network coupled to our data-acquisition system will be able to produce breast images in 10 ¿15 minutes rather than the days that would be required on a supercomputer using our original single-volume method. Reconstruction times measured in minutes rather than days would have an enormous impact on the clinical utility of our imaging instrument because data could be acquired and the reconstructed volumes of the breast viewed in the course of a single visit. An ultrasound imaging system capable of producing images with 100-micron resolution in minutes would provide an improved and efficient way to screen for breast cancer and also to diagnose other breast disease.
项目概述和行政补充的理由半球阵列超声乳腺成像系统目前正在开发的罗切斯特大学与美国国立卫生研究院资助R 01 EB 009692是一个独特的设施,包括数据采集设备和混合数据采集和高性能计算机网络。该系统利用超声换能器阵列、前端电子器件、数字技术的显著进步以及逆散射的理论突破来提供内在组织特性的无斑点、高分辨率、定量图像,即,声速和衰减斜率。该系统被设计为通过使用10,240个并行通道进行发送和接收来在两秒间隔期间采集数据,并且通过使用新的重建算法以与X射线乳房摄影术的横向分辨率一样好的各向同性点分辨率对整个乳房体积进行成像。该系统使用非电离超声波,可对乳房进行无风险检查以检测癌症,并克服了X射线乳房摄影术的局限性,例如致密乳房的对比度分辨率低、压缩引起的解剖结构变形导致的变形和不适以及植入乳房的成像差。成功的证明将最终改变乳腺癌筛查的方式,并显着提高检测,诊断和监测乳腺癌治疗的反应。自从最初的赠款获得以来,一个重要的理论发展--一个重大的突破--已经发生。这一突破使我们独特的重建算法能够独立地重建不同的子体积,即,并联在我们的算法被扩展的同时,图形处理单元(GPU)正在演变成强大的并行计算引擎。这些GPU允许小型高性能计算机系统(HPC)执行大规模并行计算,非常适合实现我们的并行重建算法。在我们系统的计算节点中加入GPU,使混合计算机网络的计算能力与联邦政府支持的大型国家超级计算机设施相当,这些设施最初旨在用于重建。基于GPU的HPC网络与我们的数据采集系统相耦合,将能够在10 - 15分钟内生成乳房图像,而不是使用我们原始的单卷方法在超级计算机上需要的几天。以分钟而不是以天为单位测量的重建时间将对我们的成像仪器的临床实用性产生巨大影响,因为可以在单次访问的过程中获取数据并查看乳房的重建体积。一个能够在几分钟内产生100微米分辨率图像的超声成像系统将提供一种改进和有效的方法来筛查乳腺癌,并诊断其他乳腺疾病。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Comparison of temporal and spectral scattering methods using acoustically large breast models derived from magnetic resonance images.
使用从磁共振图像导出的声学大乳房模型来比较时间和光谱散射方法。
Reduced-Rank Approximations to the Far-Field Transform in the Gridded Fast Multipole Method.
  • DOI:
    10.1016/j.jcp.2011.02.016
  • 发表时间:
    2011-05-10
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    Hesford, Andrew J.;Waag, Robert C.
  • 通讯作者:
    Waag, Robert C.
Aberration compensation of an ultrasound imaging instrument with a reduced number of channels.
A singular-value method for reconstruction of nonradial and lossy objects.
用于重建非径向和有损物体的奇异值方法。
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

ROBERT C WAAG其他文献

ROBERT C WAAG的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('ROBERT C WAAG', 18)}}的其他基金

Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering
使用半球阵列和逆散射对乳房进行超声成像
  • 批准号:
    8977104
  • 财政年份:
    2015
  • 资助金额:
    $ 4.34万
  • 项目类别:
Estimation and Correction of Ultrasound Beam Aberration Caused by Breast
乳腺引起的超声束像差的估计与校正
  • 批准号:
    7985875
  • 财政年份:
    2010
  • 资助金额:
    $ 4.34万
  • 项目类别:
Estimation and Correction of Ultrasound Beam Aberration Caused by Breast
乳腺引起的超声束像差的估计与校正
  • 批准号:
    8279211
  • 财政年份:
    2010
  • 资助金额:
    $ 4.34万
  • 项目类别:
Estimation and Correction of Ultrasound Beam Aberration Caused by Breast
乳腺引起的超声束像差的估计与校正
  • 批准号:
    8470094
  • 财政年份:
    2010
  • 资助金额:
    $ 4.34万
  • 项目类别:
Estimation and Correction of Ultrasound Beam Aberration Caused by Breast
乳腺引起的超声束像差的估计与校正
  • 批准号:
    8115955
  • 财政年份:
    2010
  • 资助金额:
    $ 4.34万
  • 项目类别:
ULTRASOUND SCATTERING FROM A DISTRIBUTION OF SPHERES IN A TISSUE-MIMICKING PHAN
组织模拟 PHAN 中球体分布的超声散射
  • 批准号:
    7956143
  • 财政年份:
    2009
  • 资助金额:
    $ 4.34万
  • 项目类别:
Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering
使用半球阵列和逆散射对乳房进行超声成像
  • 批准号:
    8307744
  • 财政年份:
    2009
  • 资助金额:
    $ 4.34万
  • 项目类别:
Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering
使用半球阵列和逆散射对乳房进行超声成像
  • 批准号:
    7698526
  • 财政年份:
    2009
  • 资助金额:
    $ 4.34万
  • 项目类别:
Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering
使用半球阵列和逆散射对乳房进行超声成像
  • 批准号:
    8111970
  • 财政年份:
    2009
  • 资助金额:
    $ 4.34万
  • 项目类别:
Ultrasound Imaging of Breast by Use of a Hemispheric Array and Inverse Scattering
使用半球阵列和逆散射对乳房进行超声成像
  • 批准号:
    7901359
  • 财政年份:
    2009
  • 资助金额:
    $ 4.34万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 4.34万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了