Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI
Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI
批准号:
10204956
负责人:
Jing Cai
金额:
$52.32万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-18 至 2024-06-30
关键词:
3-Dimensional4D Imaging4D MRIAbdomenAlgorithmsBiologicalBiomechanicsBreathingCancer PatientChestCommunitiesDataDevelopmentDoseFour-dimensionalFutureGoalsHumanHybridsImageImaging TechniquesImaging technologyLiverLungMagnetic Resonance ImagingMalignant NeoplasmsMeasurementMeasuresMedical ImagingMedicineMethodsModelingModernizationMorphologic artifactsMorphologyMotionNormal tissue morphologyPatientsPhasePhysiologicalPredispositionProcessProspective StudiesRadiation Dose UnitRadiation therapyResearchResolutionSamplingSchemeTechniquesTherapeuticTimeTreesWalkingbasecancer radiation therapyclinical implementationdigitalimage guidedimage registrationimprovedimproved outcomemultimodalitynovelpersonalized medicinephysiologic modelpublic health relevanceradiation-induced injuryradiomicsrespiratoryrespiratory imagingspatiotemporaltooltumor
中文摘要
点击翻译按钮获取中文摘要
英文摘要
ABSTRACT
Despite numerous advances in radiotherapy in the past decade, which have effectively enhanced local or
locoregional tumor control for many patients, there remains substantial room for improvement. A compelling
need in today's era of precision radiotherapy is to further widen the therapeutic window and improve radiation
dose conformity to the defined target volume, through technological improvements such as advanced image
guidance and motion management. Four-dimensional (4D) imaging and deformable image registration (DIR)
are two of the most important tools behind many recent radiotherapy advances, but both are facing significant
challenges as the requirement for precision increases. Major limitations of current 4D imaging technology include
low temporal/spatial resolution, long image acquisition time, suboptimal tumor contrast, and susceptibility to
artifacts caused by irregular breathing. Meanwhile, current DIR techniques focus on morphological similarity but
not on the physiological plausibility of the deformation, leading to unrealistic results in various applications.
These limitations have significantly hampered the advancement of precision radiotherapy. Our long-term goal
is to enhance precision radiotherapy through the development and clinical implementation of advanced image
guidance and motion management techniques. The overall objective of this application is to develop, cross--
fertilize, and evaluate two techniques: (a) ultra-quality 4D-MRI and (b) physiologically-based motion modeling,
for precision radiotherapy applications. Aim 1 is to develop and optimize a 4D-MRI technique for imaging
respiratory motion in the thorax and abdomen at ultra-high spatiotemporal resolution. Aim 2 is to develop a
physiologically-based motion modeling method for respiratory motion estimation. Aim 3 is to evaluate ultra-
quality 4D-MRI and physiological motion modeling in a patient study. Aim 4 is to construct physiologically realistic
4D digital phantoms for future development of precision radiotherapy applications. Successful completion of
these aims will yield powerful image guidance and motion management tools for precision radiotherapy. Such
improvements will take precision radiotherapy to a whole new level, by significantly improving radiation dose
conformity and opening doors for biological-based treatment adaptation for more effective personalized
treatment. The proposed research will have a high impact to the fields of both radiotherapy and medical imaging.
It will trigger a wave of extensive studies on a number of new and existing applications such as 4D radiotherapy,
radiomics, human digital phantom, function-based dose painting, adaptive planning, etc. Most importantly, it will
ultimately improve outcomes for cancer patients by improving our ability to precisely deliver radiation treatment
to the target and mitigate radiation-induced injury to normal tissues.
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Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI
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批准号:9980333
-
项目类别:
-
资助金额:$36.49万
-
财政年份:2019
-
负责人:Jing Cai
-
依托单位:
Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI
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批准号:10653082
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项目类别:
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资助金额:$41.67万
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财政年份:2019
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负责人:Jing Cai
-
依托单位:
Toward precision radiotherapy: Physiological modeling of respiratory motion based on ultra-quality 4D-MRI
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批准号:10413106
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项目类别:
-
资助金额:$43.5万
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财政年份:2019
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负责人:Jing Cai
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依托单位:
Image-guided Dosimetry for Injectable Brachytherapy based on Elastin-like Polypeptide Nanoparticles
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批准号:9530607
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项目类别:
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资助金额:$16.53万
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财政年份:2017
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负责人:Jing Cai
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依托单位:
Assessing deformable image registration in the lung using hyperpolarized-gas MRI
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批准号:9380237
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项目类别:
-
资助金额:$6.65万
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财政年份:2016
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负责人:Jing Cai
-
依托单位:
Assessing deformable image registration in the lung using hyperpolarized-gas MRI
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批准号:9179479
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项目类别:
-
资助金额:$20.79万
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财政年份:2016
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负责人:Jing Cai
-
依托单位:
Assessing deformable image registration in the lung using hyperpolarized-gas MRI
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批准号:9312777
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项目类别:
-
资助金额:$17.28万
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财政年份:2016
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负责人:Jing Cai
-
依托单位:
Motion Management Using 4D-MRI for Liver Cancer in Radiation Therapy
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批准号:8824888
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项目类别:
-
资助金额:$24.64万
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财政年份:2013
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负责人:Jing Cai
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依托单位:
Motion Management Using 4D-MRI for Liver Cancer in Radiation Therapy
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批准号:8443466
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项目类别:
-
资助金额:$32.58万
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财政年份:2013
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负责人:Jing Cai
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依托单位:
Motion Management Using 4D-MRI for Liver Cancer in Radiation Therapy
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批准号:8604696
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项目类别:
-
资助金额:$6.96万
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财政年份:2013
-
负责人:Jing Cai
-
依托单位:
海外基金