DASSIM-RT and Compressed Sensing-Based Inverse Planning
DASSIM-RT and Compressed Sensing-Based Inverse Planning
批准号:
8643085
负责人:
Lei Xing
金额:
$57.01万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-06-01 至 2019-05-31
关键词:
AccountingAddressAlgorithmsCancer PatientClinicalCollimatorComplexDevelopmentDisciplineDoseDose-RateEquilibriumFoundationsFutureGenerationsGoalsIntensity-Modulated RadiotherapyInvestigationKnowledgeLeadMalignant NeoplasmsMapsMotionMusPatient CarePatientsPatternPerformancePhysiciansPlanning TechniquesPrincipal InvestigatorProblem SolvingProcessProtocols documentationRadiation OncologyRadiation Therapy Oncology GroupResearchResearch PersonnelSamplingSchemeSeriesShapesSolutionsSystemSystems AnalysisTechniquesTechnologyTestingTherapeuticTimeTimeLineTissuesTranslationsTreatment outcomeVariantbasecancer therapycase controlcostdesigndigitalempoweredimprovedinnovationmeetingsnovelprogramspublic health relevancetooltreatment planningtumor
中文摘要
NIH R 01(PA 11-260):DASSIM-RT和基于压缩感知的逆向规划
项目摘要
该项目的目标是建立一个密集角度采样和稀疏强度的新范例-
调制式放射治疗(DASSIM-RT)。在该方案中,
通过使用压缩感测(CS)技术有效地去除入射强度调制光束。的
以这种方式节省的输送时间被用于增加角度采样以改善剂量一致性。通过
平衡角度采样和强度调制,DASSIM-RT使我们能够充分利用技术
现代数字直线加速器产生高度共形剂量分布的能力,
有效地具体而言,我们将1)建立一个基于压缩感知(CS)的逆处理框架
计划; 2)研究一种称为DASSIM-RT的新型治疗方案; 3)显示
DASSIM-RT通过一系列体模病例和既往接受过治疗的患者。DASSIM-RT代表了一个真正的
最佳RT方案与不妥协的角度采样(包括非共面光束),光束强度
调制以及可能的场特定能量和准直器角度。如果成功,该项目将使我们能够
克服了现有治疗方案的许多局限性,以满足对高剂量的未满足的临床需求。
放射肿瘤学中的适形剂量分布。
英文摘要
NIH R01 (PA 11-260): DASSIM-RT and Compressed Sensing-Based Inverse Planning
Project Summary
The goal of this project is to establish a novel paradigm of dense angularly sampled and sparse intensity-
modulated radiation therapy (DASSIM-RT). In this scheme, the redundant or dispensable modulation of the
incident intensity-modulated beams is removed effectively by using a compressed sensing (CS) technique. The
delivery time saved in this way is used to increase the angular sampling for improved dose conformality. By
balancing the angular sampling and intensity modulation, DASSIM-RT enables us to fully utilize the technical
capabilities of modern digital linacs to produce highly conformal dose distributions that can be delivered
efficiently. Specifically, we will 1) set up a compressed sensing (CS)-based framework for inverse treatment
planning; 2) investigate a new type of treatment scheme termed DASSIM-RT; and 3) show the advantage of
DASSIM-RT through a series of phantom cases and previously treated patients. DASSIM-RT represents a truly
optimal RT scheme with uncompromised angular sampling (including non-coplanar beams), beam intensity
modulation, and possible field-specific energy and collimator angle. If successful, the project will allow us to
overcome many of the limitations of existing treatment schemes to meet the unmet clinical demand for highly
conformal dose distributions in radiation oncology.
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