Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CT
Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CT
批准号:
10674275
负责人:
Xun Jia
金额:
$34.72万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-03-15 至 2024-02-29
关键词:
AdoptionAlgorithmsAnatomyBreathingCaliberCancer EtiologyCessation of lifeClinicalClinical TrialsContrast MediaDataDevelopmentDictionaryDoseEffectivenessElementsEnsureGoalsHepaticImageImplantIncidenceInferiorInjectionsIntravenousIodineLiverLiver DysfunctionLiver diseasesMagnetic Resonance ImagingMalignant NeoplasmsMalignant neoplasm of liverMethodsMorphologic artifactsMotionNatureNormal tissue morphologyPatientsPositioning AttributeProceduresRadiationRadiation therapyScanningSystemSystems DevelopmentTechniquesTestingToxic effectTrainingTumor VolumeUncertaintyUnresectableValidationX-Ray Computed Tomographybaseclinically significantclinically translatablecone-beam computed tomographycontrast enhancedcostexperienceimage guidedimage reconstructionimaging approachimaging capabilitiesimaging studyimaging systemimprovedinnovationmortalitynovelpatient populationpatient safetyreconstructionrespiratorysafety and feasibilitysafety testingsimulationspectrographstandard caresuccesstreatment planningtumor
中文摘要
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英文摘要
Project Summary
Liver cancers, both primary and metastatic, are increasing in incidence and are associated with significant
mortality. Stereotactic Body Radiotherapy (SBRT) has been established as an effective, safe, and feasible first-
line option in the local control of unresectable hepatic malignancies. Nonetheless, a large margin (typically
~1cm) has to be used in current liver SBRT to accommodate tumor positioning uncertainty under cone-beam
CT (CBCT) image guidance. Because of respiratory motion and vanishing tumor contrast, the tumor target
cannot be visualized in CBCT. Inferior tumor positioning approach based on anatomical or implanted
surrogates are clinical standard, leading to substantial tumor position uncertainty and a typical margin size of
~1cm. Consequently, high dose to a large volume of normal tissue is delivered, causing a toxicity concern,
especially in patients with liver dysfunction caused by cancer and/or treatments is more substantial. In addition,
normal tissue toxicity limits further dose escalation to improve clinical benefits. This issue is expected to
become more severe, when extending SBRT to a wider patient population, e.g. those with a large tumor size.
Several emerging imaging approaches have showed potential to improve image guidance accuracy but also
encountered challenges. To date, there is no approach that can provide accurate, reliable, and clinically
translatable image guidance for liver SBRT. Recently, our group has made a breakthrough towards
reconstructing elemental composition image using a standard CBCT platform. Employing a kVp-switching
technique, a novel image reconstruction method with spatial and spectral image regularization, as well as a
sparse-dictionary based element decomposition method, we achieved ~3% accuracy in elemental composition
as tested in phantom studies. We have also accumulated extensive experience in reconstructing high-quality
CBCT images under respiratory motion. Armed with these successes, the overall goal of this study is to
develop a novel element-resolved and motion-compensated (ERMC-) CBCT to image iodine contrast agent
using only 20% contrast injection in a standard treatment planning CT scan for precise (uncertainty <2mm)
image guidance in liver SBRT. We will pursue three specific aims (SAs): SA1. Develop the overall ERMC-
CBCT system. SA2. Optimize scan parameters via phantom studies. SA3. Perform studies in 10 patient cases
to test safety, feasibility, and tumor positioning accuracy of ERMC-CBCT based image guidance. The
innovation of this project is a novel ERMC-CBCT system and its application for a clinically significant problem
of tumor localization in liver SBRT. Besides the significance of substantially improved localization accuracy and
therefore clinical potential of normal tissue sparing and dose escalation, our project also holds the significance
of utilizing CBCT to its maximal potential for many other advanced image guidance tasks and quantitative
applications. The ERMC-CBCT system is developed on a conventional CBCT platform, the most widely
available image-guidance platform in radiotherapy, ensuring its translatability.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1016/j.media.2020.101896
发表时间:
2021-03
期刊:
Medical image analysis
影响因子:
10.9
作者:
[Gonzalez Y, Shen C, Jung H, Nguyen D, Jiang SB, Albuquerque K, Jia X]
通讯作者:
Jia X
Next generation small animal radiation research platform
-
批准号:10680056
-
项目类别:
-
资助金额:$15.88万
-
财政年份:2022
-
负责人:Xun Jia
-
依托单位:
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
-
批准号:10592427
-
项目类别:
-
资助金额:$61.56万
-
财政年份:2022
-
负责人:Xun Jia
-
依托单位:
Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging
-
批准号:10391652
-
项目类别:
-
资助金额:$65.83万
-
财政年份:2022
-
负责人:Xun Jia
-
依托单位:
Human-like automated radiotherapy treatment planning via imitation learning
-
批准号:10610971
-
项目类别:
-
资助金额:$60.6万
-
财政年份:2021
-
负责人:Xun Jia
-
依托单位:
Human-like automated radiotherapy treatment planning via imitation learning
-
批准号:10406863
-
项目类别:
-
资助金额:$59.28万
-
财政年份:2021
-
负责人:Xun Jia
-
依托单位:
Intelligent treatment planning for cancer radiotherapy
-
批准号:10363727
-
项目类别:
-
资助金额:$48.03万
-
财政年份:2019
-
负责人:Xun Jia
-
依托单位:
Intelligent treatment planning for cancer radiotherapy
-
批准号:10190850
-
项目类别:
-
资助金额:$49.01万
-
财政年份:2019
-
负责人:Xun Jia
-
依托单位:
Intelligent treatment planning for cancer radiotherapy
-
批准号:10593946
-
项目类别:
-
资助金额:$48.03万
-
财政年份:2019
-
负责人:Xun Jia
-
依托单位:
Next generation small animal radiation research platform
-
批准号:10895120
-
项目类别:
-
资助金额:$47.11万
-
财政年份:2018
-
负责人:Xun Jia
-
依托单位:
Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CT
-
批准号:10112840
-
项目类别:
-
资助金额:$39.27万
-
财政年份:2018
-
负责人:Xun Jia
-
依托单位:
Next generation small animal radiation research platform
-
批准号:10331746
-
项目类别:
-
资助金额:$26.62万
-
财政年份:2018
-
负责人:Xun Jia
-
依托单位:
Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CT
-
批准号:10348153
-
项目类别:
-
资助金额:$6.68万
-
财政年份:2018
-
负责人:Xun Jia
-
依托单位:
Explore random sampling for dose reduction and scatter removal in cone beam CT
-
批准号:9282422
-
项目类别:
-
资助金额:$24.04万
-
财政年份:2016
-
负责人:Xun Jia
-
依托单位:
4D cone beam CT reconstruction for radiotherapy via motion vector optimization
-
批准号:8824420
-
项目类别:
-
资助金额:$20.19万
-
财政年份:2014
-
负责人:Xun Jia
-
依托单位:
4D cone beam CT reconstruction for radiotherapy via motion vector optimization
-
批准号:8935775
-
项目类别:
-
资助金额:$24.23万
-
财政年份:2014
-
负责人:Xun Jia
-
依托单位:
A progressive cone-beam CT dose control scheme for image-guided radiation therapy
-
批准号:8882347
-
项目类别:
-
资助金额:$20.75万
-
财政年份:2014
-
负责人:Xun Jia
-
依托单位:
A progressive cone-beam CT dose control scheme for image-guided radiation therapy
-
批准号:8691950
-
项目类别:
-
资助金额:$17.29万
-
财政年份:2014
-
负责人:Xun Jia
-
依托单位:
海外基金