Non-contrast 3D T1p Mapping for Myocardial Fibrosis Quantification of Pediatric Cardiomyopathy Patients
用于小儿心肌病患者心肌纤维化定量的非对比 3D T1p 映射
基本信息
- 批准号:10351919
- 负责人:
- 金额:$ 9.87万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-03-01 至 2024-02-29
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAddressAdultAffectArchitectureArrhythmiaBrainBreathingCardiacCardiomyopathiesChildChildhoodClinicalClinical MedicineContrast MediaDataData AnalysesDevelopmentDiffuseDiseaseFibrosisFinancial costFunctional disorderGadoliniumGeneral AnesthesiaGoldGrantHandHealthHeartHeart DiseasesHypertrophic CardiomyopathyImageImage EnhancementImaging TechniquesLeft ventricular structureManualsMarfan SyndromeMeasurementMeasuresMyocarditisNaturePatient CarePatientsPediatric CardiomyopathyPhysiologic pulsePopulationPreparationRenal functionResearchResolutionRight ventricular structureRiskSamplingScanningScreening procedureSex DifferencesSpeedTNFSF15 geneTachycardiaTechniquesTechnologyTestingThickTimeTrainingTraining ActivityWorkage differenceallograft rejectionautomated image analysisautomated segmentationbasecardiac magnetic resonance imagingcareerclinical translationcongenital heart disordercoronary fibrosisdeep learningextracellularheart allograftimage reconstructioninnovationolder patientpatient populationpediatric patientsprototyperacial differenceradio frequencyreconstructionresearch clinical testingstudy populationsudden cardiac deathyoung adult
项目摘要
PROJECT SUMMARY
The development of myocardial fibrosis is associated with nearly all forms of pediatric
heart disease including hypertrophic cardiomyopathy, congenital heart disease, diastolic
dysfunction, arrhythmia, myocarditis, and sudden cardiac death. Despite the pervasive nature of
myocardial fibrosis, the current technology available to detect fibrosis is suboptimal for studying
pediatric cardiomyopathy. Cardiac MRI (CMR) is the gold standard noninvasive screening tool to
detect both diffuse and focal fibrosis, through extracellular volume (ECV) and late gadolinium
enhancement (LGE) imaging, respectively. Unfortunately, both ECV and LGE CMR require the
administration of a gadolinium-based contrast agent (GBCA), which accumulates in the brain
even when renal function is normal, including in children. In addition, traditional CMR requires
subjects to hold their breath for accurate imaging. However, many pediatric patients cannot
adequately hold their breath and so are put under general anesthesia (GA), which is not ideal as
GA poses an additional health risk and significant financial cost. Furthermore, the current 2D
techniques for fibrosis imaging have insufficient spatial resolution, and thus are only able to
acquire data in sections of the left ventricle (6-10 mm thick) of the heart, completely missing
fibrosis information in the right ventricle (3-5 mm thick), which is known to be the substrate for
some tachycardia arrhythmias. Therefore,
breathing, T1ρ mapping is a
promising non-contrast CMR technique that can be used to detect both focal and diffuse
myocardial fibrosis. Despite its enormous potential for assessment of myocardial fibrosis in
pediatric patients, cardiac T1ρ mapping suffers from several technical limitations: (a) poor spatial
resolution, (b) long scan time (up to 18 min), and (c) undeveloped pipeline for clinical integration.
Additionally, the volumetric cardiac T1ρ mapping sequences that have been developed have only
been tested on adult patients, and in very few subjects (n < 15). Therefore, in this study, I seek to
address these limitations of 3D cardiac T1ρ mapping by (1) using innovative k-space sampling
with deep learning for achieving unprecedented image quality with acceptable scan and
reconstruction time, (2) implementing deep learning to automate image analysis and fibrosis
quantification to make the information readily accessible for patient care, and (3) scanning a large
population of pediatric patients to make this the most comprehensive T1ρ mapping study to date.
there is a strong need to develop a non-contrast, free-
volumetric imaging test for detecting fibrosis in pediatric patients.
项目总结
心肌纤维化的发展与几乎所有形式的儿科
心脏病包括肥厚型心肌病、先天性心脏病、舒张期
功能障碍、心律失常、心肌炎和心脏性猝死。尽管无处不在的
心肌纤维化,目前可用于检测纤维化的技术对于研究来说是次要的
儿科心肌病。心脏磁共振(CMR)是金标准的无创筛查工具
通过细胞外体积(ECV)和晚期Gd检测弥漫性和局灶性纤维化
分别行增强(LGE)成像。遗憾的是,ECV和LGE CMR都需要
注射一种在大脑中积聚的格拉基造影剂(GBCA)
即使肾功能正常,包括儿童。此外,传统的CMR需要
受试者屏住呼吸以获得准确的成像。然而,许多儿科患者不能
充分屏住呼吸,因此被置于全身麻醉(GA)下,这不是理想的,因为
GA会带来额外的健康风险和巨大的财务成本。此外,目前的2D
纤维化成像技术的空间分辨率不足,因此只能
获取心脏左心室(6-10 mm厚)的断层数据,完全缺失
右心室(3-5 mm厚)的纤维化信息,这是已知的
一些心动过速的心律失常。因此,
呼吸,T1ρ映射是一种
前景看好的非对比度CMR技术,可用于检测焦点和漫反射
心肌纤维化。尽管它在评估心肌纤维化方面具有巨大的潜力
儿科患者,心脏T1ρ标测存在几个技术限制:(A)空间差
(B)扫描时间长(长达18分钟),以及(C)临床集成流水线未开发。
此外,已经开发的容量心脏T1ρ作图序列仅具有
已在成人患者和极少数受试者上进行了测试(n<;15)。因此,在这项研究中,我试图
通过以下方式解决3D心脏T1ρ标测的这些局限性:(1)使用创新的k空间采样
具有深度学习功能,可通过可接受的扫描和
重建时间,(2)实施深度学习以自动化图像分析和纤维化
量化以便于患者护理访问信息,以及(3)扫描大型
这是迄今为止最全面的T1ρ图谱研究。
迫切需要开发一种无对比度、免费的
容积显像法检测儿科患者肝纤维化
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 }}
Suvai Gunasekaran其他文献
Suvai Gunasekaran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('Suvai Gunasekaran', 18)}}的其他基金
Non-contrast 3D T1p Mapping for Myocardial Fibrosis Quantification of Pediatric Cardiomyopathy Patients
用于小儿心肌病患者心肌纤维化定量的非对比 3D T1p 映射
- 批准号:
10579868 - 财政年份:2022
- 资助金额:
$ 9.87万 - 项目类别:
相似海外基金
Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
- 批准号:
MR/S03398X/2 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
- 批准号:
EP/Y001486/1 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
- 批准号:
2338423 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
- 批准号:
MR/X03657X/1 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
- 批准号:
2348066 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Standard Grant
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
- 批准号:
2341402 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
- 批准号:
AH/Z505481/1 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10107647 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
EU-Funded
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10106221 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
- 批准号:
AH/Z505341/1 - 财政年份:2024
- 资助金额:
$ 9.87万 - 项目类别:
Research Grant