High-Performance Computer Cluster for Image Analysis
用于图像分析的高性能计算机集群
基本信息
- 批准号:7219190
- 负责人:
- 金额:$ 22.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2007
- 资助国家:美国
- 起止时间:2007-04-01 至 2009-03-31
- 项目状态:已结题
- 来源:
- 关键词:AreaBenignBiopsyBreastComputer SimulationComputer softwareComputer-Assisted DiagnosisComputersDataData SetEarly DiagnosisEvaluation StudiesImageImage AnalysisKnowledgeLeadLung noduleMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMalignant - descriptorMalignant NeoplasmsMalignant neoplasm of prostateMedicalMethodsModelingNumbersPerformanceROC CurveReaderResearchResearch PersonnelSamplingStagingSystemTechniquesTechnology AssessmentUpdateVariantbasebreast lesioncostdesigndistributed memoryimage reconstructionimprovedpreclinical studyprospectiveradiologistreconstructionresearch clinical testingresponsesimulationsize
项目摘要
DESCRIPTION (provided by applicant): We are proposing to purchase a distributed memory computation cluster to enhance our research in image analysis, image reconstruction, and technology assessment, The long-term objectives of our research is to advance our knowledge in computer-aided diagnosis (CAD), image reconstruction, MRI/MRS for the breast and prostate cancers, and ROC analysis. Specifically, the proposed system will allow for advances in four areas. First, we plan to conduct clinical evaluation of CAD where online calculations are needed. Because of the complexity of the calculations, these need to conducted on high performance computers to achieve 1 s response of the CAD system. Second, we will develop image analysis techniques for analyzing MRI and MRS datasets for improving the early detection and staging of breast and prostate cancers. The datasets for MRI and MRS are large - over a gigabyte in size. To maximize the extraction of information from these data, preprocesswill develop advanced image reconstruction techniques using a combination of computer simulation to model image acquisition systems and optimizing reconstruction methods using the simulations. Both the simulation and reconstruction techniques are compute intensive. Fourth, we will develop publicly available software for ROC analysis that incorporates recent advances in the field. These include statistical analyses of differences between ROC curves based on variations that due to case-sample variation and image-reader variation. The relevance of the research, which will employ the computation cluster, is four fold. First, we will demonstrate in prospective pre-clinical studies that computers can help radiologists determine whether a breast lesion or a lung nodule is benign or malignant. This can reduce the number of unnecessary biopsies and reduce the chances that a cancer is missed. Second, by extracting more information from MRI studies, we can improve the early detection, diagnosisctive. The impact of our research using the poposed system is large. For example, the old version of ROC software is used by more than 10,000 researchers worldwide. The updated software will undoubtedly benefit many researchers.
ology (ROC analysis) can lead to faster and cheaper implementation of the most promising of new medical advances, because evaluation studies can be designed that are more efficient and cost effe
描述(由申请人提供):我们建议购买一个分布式内存计算集群,以加强我们在图像分析,图像重建和技术评估方面的研究,我们研究的长期目标是提高我们在计算机辅助诊断(CAD),图像重建,乳腺癌和前列腺癌MRI/MRS和ROC分析方面的知识。具体来说,拟议的制度将允许在四个领域取得进展。首先,我们计划对需要在线计算的CAD进行临床评估。由于计算的复杂性,这些需要在高性能计算机上进行,以达到CAD系统的1s响应。其次,我们将开发用于分析MRI和MRS数据集的图像分析技术,以提高乳腺癌和前列腺癌的早期发现和分期。核磁共振和核磁共振的数据集很大——超过1 gb。为了最大限度地从这些数据中提取信息,预处理将开发先进的图像重建技术,使用计算机模拟来模拟图像采集系统,并使用模拟优化重建方法。模拟和重建技术都是计算密集型的。第四,我们将开发包含该领域最新进展的ROC分析的公开可用软件。其中包括基于病例-样本差异和图像阅读器差异的ROC曲线差异的统计分析。该研究将使用计算集群,其相关性为四倍。首先,我们将在前瞻性临床前研究中证明,计算机可以帮助放射科医生确定乳房病变或肺结节是良性还是恶性。这可以减少不必要的活组织检查的次数,减少癌症漏诊的机会。其次,通过从MRI研究中提取更多的信息,我们可以提高早期发现,诊断。我们的研究使用所提出的系统的影响是巨大的。例如,旧版本的ROC软件被全球超过10,000名研究人员使用。更新后的软件无疑将使许多研究人员受益。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Imaging in pleural mesothelioma: A review of the 15th International Conference of the International Mesothelioma Interest Group.
胸膜间皮瘤影像学:国际间皮瘤兴趣小组第 15 届国际会议回顾。
- DOI:10.1016/j.lungcan.2021.12.008
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Armato,SamuelG;Nowak,AnnaK;Francis,RoslynJ;Katz,SharynI;Kholmatov,Manizha;Blyth,KevinG;Gudmundsson,Eyjolfur;Kidd,AndrewC;Gill,RituR
- 通讯作者:Gill,RituR
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ROBERT M NISHIKAWA其他文献
ROBERT M NISHIKAWA的其他文献
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{{ truncateString('ROBERT M NISHIKAWA', 18)}}的其他基金
Detecting Mammographically-Occult Cancer in Women with Dense Breasts
检测乳腺致密女性的乳房X线隐匿性癌症
- 批准号:
9296809 - 财政年份:2017
- 资助金额:
$ 22.96万 - 项目类别:
A new approach to optimizing and evaluating computer-aided detection schemes
优化和评估计算机辅助检测方案的新方法
- 批准号:
8725661 - 财政年份:2013
- 资助金额:
$ 22.96万 - 项目类别:
A new approach to optimizing and evaluating computer-aided detection schemes
优化和评估计算机辅助检测方案的新方法
- 批准号:
8913965 - 财政年份:2013
- 资助金额:
$ 22.96万 - 项目类别:
A new approach to optimizing and evaluating computer-aided detection schemes
优化和评估计算机辅助检测方案的新方法
- 批准号:
9326987 - 财政年份:2013
- 资助金额:
$ 22.96万 - 项目类别:
A new approach to optimizing and evaluating computer-aided detection schemes
优化和评估计算机辅助检测方案的新方法
- 批准号:
9134748 - 财政年份:2013
- 资助金额:
$ 22.96万 - 项目类别:
A new approach to optimizing and evaluating computer-aided detection schemes
优化和评估计算机辅助检测方案的新方法
- 批准号:
8439396 - 财政年份:2013
- 资助金额:
$ 22.96万 - 项目类别:
Quantitative Evaluation of Reconstruction Algorithms - Resubmission 01
重建算法的定量评估-补交01
- 批准号:
8384340 - 财政年份:2012
- 资助金额:
$ 22.96万 - 项目类别:
Quantitative Evaluation of Reconstruction Algorithms - Resubmission 01
重建算法的定量评估-补交01
- 批准号:
8517718 - 财政年份:2012
- 资助金额:
$ 22.96万 - 项目类别:
Computerized Lesion Detection in Breast Tomosynthesis
乳腺断层合成中的计算机化病变检测
- 批准号:
7290123 - 财政年份:2006
- 资助金额:
$ 22.96万 - 项目类别:
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