Improved Techniques for Substitute CT Generation from MRI datasets
从 MRI 数据集生成替代 CT 的改进技术
基本信息
- 批准号:9927625
- 负责人:
- 金额:$ 45.89万
- 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
- 起止时间:2018-08-10 至 2022-05-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAbdomenAirAlgorithmsAreaBody RegionsBrainChestClinicalDataData SetDatabasesDeformityDevelopmentEvaluationFinancial compensationFutureGenerationsHeadHead and neck structureImageIonizing radiationLinear Accelerator Radiotherapy SystemsMachine LearningMagnetic Resonance ImagingMeasurementMethodologyMethodsMotionPET/CT scanPathologicPatientsPelvisPerformancePositron-Emission TomographyPsychological TransferRadialRadiation exposureRadiation therapyResidual stateResolutionSamplingTechniquesTechnologyTissuesTrainingUncertaintyWorkX-Ray Computed Tomographyattenuationbaseboneconvolutional neural networkdeep learningelectron densityimage guidedimaging capabilitiesimprovedlearning networkprospectivereal-time imagesreconstructionrespiratoryroutine imagingsimulationsoft tissuetreatment planningtumorwhole body imaging
项目摘要
This proposal will enable improved substitute CT images for use in PET/MR and MR-only radiation treatment
planning. Given the greatly improved soft-tissue contrast of MR relative to CT, which aids interpretation of PET
for PET/MR and target delineation for radiation treatment planning, a remaining limitation is the current
capability to obtain sufficiently accurate substitute CT images from only MR-data. Unfortunately, MRI has
limited capability to resolve bone and the inability of most MR acquisitions to distinguish between air and bone
makes segmentation of these tissues types challenging. This project will utilize deep learning, a new and
growing area of machine learning, to develop new methodology to create substitute CT images from rapid MR
acquisitions that can be utilized in PET/MR and radiation treatment planning workflows. In Aim 1 we will study
rapid MR acquisitions to be used with deep learning approaches for sCT generation in the head and pelvis
using 3T PET/MR images matched with PET/CT imaging to create deep learning training and evaluation
datasets. Different deep learning networks and MR inputs will be studied and adapted to determine the best
PET reconstruction performance. In Aim 2 we will investigate rapid but motion-resilient approaches to whole-
body MR imaging for subsequent deep learning-based substitute CT generation. In an exploratory subaim, we
also propose to study methods of sCT generation that only utilize PET-only data. The data acquired in Aim 2
will be used to create comprehensive whole-body, motion-resilient datasets for training and evaluation of deep
learning networks. In Aim 3 we will evaluate substitute CT approaches for MR-only radiation treatment
planning. MR-only approaches will be compared to standard CT-based treatment simulation in the brain, head
& neck, chest, abdomen, and pelvis and deep learning networks will be optimized and evaluated for region-
specific RT planning and simulation. Additionally, transfer learning approaches will be studied to extend sCT to
a 0.35T MR-Linac to demonstrate respiratory motion resolved substitute CT generation.
该提案将改进替代CT图像,用于PET/MR和仅MR放射治疗
规划鉴于MR相对于CT的软组织对比度大大提高,这有助于PET的解释
对于PET/MR和放射治疗计划的靶勾画,剩余的限制是当前
仅从MR数据获得足够准确的替代CT图像的能力。不幸的是,MRI
分辨骨的能力有限,大多数MR采集无法区分空气和骨
使得这些组织类型的分割具有挑战性。该项目将利用深度学习,一个新的和
机器学习的发展领域,开发新的方法,从快速MR创建替代CT图像
可以在PET/MR和放射治疗计划工作流程中使用的采集。在目标1中,我们将研究
快速MR采集与深度学习方法一起用于头部和骨盆中的sCT生成
使用与PET/CT成像匹配的3 T PET/MR图像来创建深度学习训练和评估
数据集。不同的深度学习网络和MR输入将被研究和调整,以确定最佳的
PET重建性能。在目标2中,我们将研究快速但运动弹性的方法,
身体MR成像,用于后续基于深度学习的替代CT生成。在一个探索性的子目标中,我们
还建议研究仅利用仅PET数据的sCT生成方法。目标2中获得的数据
将用于创建全面的全身,运动弹性数据集,用于训练和评估深度
学习网络。在目标3中,我们将评估仅MR放射治疗的替代CT方法
规划将仅MR方法与基于CT的标准治疗模拟进行比较,
和颈部,胸部,腹部和骨盆,深度学习网络将针对区域进行优化和评估-
具体的RT计划和模拟。此外,还将研究迁移学习方法,以将sCT扩展到
0.35T MR-Linac,用于演示呼吸运动分辨替代CT生成。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
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 }}
Alan Blair McMillan其他文献
Alan Blair McMillan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('Alan Blair McMillan', 18)}}的其他基金
PET/MR Correlates of Accelerated Aging in Chronic Epilepsy
慢性癫痫加速衰老的 PET/MR 相关性
- 批准号:
10388246 - 财政年份:2021
- 资助金额:
$ 45.89万 - 项目类别:
PET/MR Correlates of Accelerated Aging in Chronic Epilepsy
慢性癫痫加速衰老的 PET/MR 相关性
- 批准号:
10580787 - 财政年份:2021
- 资助金额:
$ 45.89万 - 项目类别:
PET/MR Correlates of Accelerated Aging in Chronic Epilepsy
慢性癫痫加速衰老的 PET/MR 相关性
- 批准号:
10210072 - 财政年份:2021
- 资助金额:
$ 45.89万 - 项目类别:
Improved Techniques for Substitute CT Generation from MRI datasets
从 MRI 数据集生成替代 CT 的改进技术
- 批准号:
10179376 - 财政年份:2018
- 资助金额:
$ 45.89万 - 项目类别:
Improved Techniques for Substitute CT Generation from MRI datasets
从 MRI 数据集生成替代 CT 的改进技术
- 批准号:
9762102 - 财政年份:2018
- 资助金额:
$ 45.89万 - 项目类别:
Accelerated Electron Paramagnetic Resonance Imaging
加速电子顺磁共振成像
- 批准号:
8385868 - 财政年份:2012
- 资助金额:
$ 45.89万 - 项目类别:
Accelerated Electron Paramagnetic Resonance Imaging
加速电子顺磁共振成像
- 批准号:
8528585 - 财政年份:2012
- 资助金额:
$ 45.89万 - 项目类别:
相似海外基金
Contributions of cell behaviours to dorsal closure in Drosophila abdomen
细胞行为对果蝇腹部背侧闭合的贡献
- 批准号:
2745747 - 财政年份:2022
- 资助金额:
$ 45.89万 - 项目类别:
Studentship
Using the GI Tract as a Window to the Autonomic Nervous System in the Thorax and in the Abdomen
使用胃肠道作为胸部和腹部自主神经系统的窗口
- 批准号:
10008166 - 财政年份:2018
- 资助金额:
$ 45.89万 - 项目类别:
Development of a free-breathing dynamic contrast-enhanced (DCE)-MRI technique for the abdomen using a machine learning approach
使用机器学习方法开发腹部自由呼吸动态对比增强 (DCE)-MRI 技术
- 批准号:
18K18364 - 财政年份:2018
- 资助金额:
$ 45.89万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Combined motion-compensated and super-resolution image reconstruction to improve magnetic resonance imaging of the upper abdomen
结合运动补偿和超分辨率图像重建来改善上腹部的磁共振成像
- 批准号:
1922800 - 财政年份:2017
- 资助金额:
$ 45.89万 - 项目类别:
Studentship
Optimising patient specific treatment plans for ultrasound ablative therapies in the abdomen (OptimUS)
优化腹部超声消融治疗的患者特定治疗计划 (OptimUS)
- 批准号:
EP/P013309/1 - 财政年份:2017
- 资助金额:
$ 45.89万 - 项目类别:
Research Grant
Optimising patient specific treatment plans for ultrasound ablative therapies in the abdomen (OptimUS)
优化腹部超声消融治疗的患者特定治疗计划 (OptimUS)
- 批准号:
EP/P012434/1 - 财政年份:2017
- 资助金额:
$ 45.89万 - 项目类别:
Research Grant
Relationship between touching the fetus via the abdomen of pregnant women and fetal attachment based on changes in oxytocin levels
基于催产素水平变化的孕妇腹部触摸胎儿与胎儿附着的关系
- 批准号:
16K12096 - 财政年份:2016
- 资助金额:
$ 45.89万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Design Research of Healthcare System based on the Suppleness of Upper Abdomen
基于上腹部柔软度的保健系统设计研究
- 批准号:
16K00715 - 财政年份:2016
- 资助金额:
$ 45.89万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Technical Development of Diffusion Tensor Magnetic Resonance Imaging in the Human Abdomen
人体腹部弥散张量磁共振成像技术进展
- 批准号:
453832-2014 - 财政年份:2015
- 资助金额:
$ 45.89万 - 项目类别:
Postdoctoral Fellowships
Technical Development of Diffusion Tensor Magnetic Resonance Imaging in the Human Abdomen
人体腹部弥散张量磁共振成像技术进展
- 批准号:
453832-2014 - 财政年份:2014
- 资助金额:
$ 45.89万 - 项目类别:
Postdoctoral Fellowships