Improved Motion Robust MRI of Children
改进儿童运动鲁棒性 MRI
基本信息
- 批准号:10605154
- 负责人:
- 金额:$ 57.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-07-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:3-Dimensional8 year oldAddressAdverse eventAlgorithmsAnesthesia proceduresCaringCephalometryChildChildhoodClinicalCompensationConsumptionDataDiagnosisDiagnosticDiagnostic ImagingDiseaseEquationFrustrationGoalsGrantHeadHead MovementsImageImaging TechniquesImaging technologyInstructionLifeMagnetic Resonance ImagingMeasurementMeasuresMethodsModelingMonitorMorphologic artifactsMotionPatientsPhysiologicalPopulationPositioning AttributeProcessRadialRadiology SpecialtyResearchResortRiskSamplingScanningScheduleSedation procedureSeriesSerious Adverse EventSignal TransductionSourceSpeedTechniquesTechnologyTestingTimeUnited States National Institutes of HealthUpdateVariantanatomic imagingbrain magnetic resonance imagingclinical imagingclinical practicecohortcostdiagnostic valueefficacy evaluationexperimental studyimage reconstructionimaging modalityimprovedinnovationinterestmagnetic fieldnovelolder patientprospectivequantitative imagingreconstructionrecruitresponseside effectsimulationspatial relationshipspatiotemporalsuccesstranslational impactverbalvolunteer
项目摘要
Project Summary
Magnetic resonance imaging (MRI) is critically important for pediatric care. However, patient motion
significantly limits our ability to produce high-quality images in young children who may be unable to respond
well to verbal instructions and who have difficulty remaining still inside the scanner. Head motion during MRI
disrupts spatial encoding and leads to data loss, generating a range of artifacts in the images, which hinders
diagnostic utility. Thus, sedation and anesthesia are routinely used in pediatric populations; however, these
practices are associated with severe adverse events and are extremely time-consuming and costly to
administer. Unfortunately, current state-of-the-art motion compensation technologies are not fast or accurate
enough to adequately compensate for large and frequent head movements in uncooperative children, or
require external hardware, which is far from ideal for clinical workflow. Under the previous grant period, we
made significant progress towards our overarching goal of improved motion-robust pediatric MRI by
successfully developing a new markerless motion tracking approach utilizing free induction decay (FID)
navigators and novel algorithms to generate diagnostic images from small periods of motion-free time. The
goal of the research proposed under this renewed application to the NIH is two-fold: 1) to continue to develop
and refine novel markerless technologies for motion measurement and correction to enable high-quality MRI in
the presence of large, frequent motion and 2) to evaluate these technologies for improving the quality and
success rate of pediatric MRI without the use of sedation and anesthesia. We hypothesize that improving the
accuracy of FID navigator motion measurements, and the extent and speed of our correction algorithms, will
successfully compensate for sources of persistent artifacts in the images. To achieve these ambitious goals,
we propose to undertake the following Specific Aims over the 5-year period of requested support: 1) develop
and evaluate an extended model that can, for the first time, simultaneously measure head motion and induced
magnetic field changes using FID navigators; 2) develop and evaluate a novel self-navigated 3D radial
acquisition with augmented reconstruction for retrospective correction of motion, and induced magnetic field
and coil sensitivity variations; 3) develop and evaluate prospective motion correction and dynamic shimming
utilizing real-time motion and field measurements to produce artifact-free images; and 4) apply and evaluate
these highly innovative motion compensation techniques for imaging 0–8 year old patients without the use of
sedation. The motion-robust imaging technologies proposed in this application can be easily deployed in
clinical settings with widely-available, standard MRI hardware, and are therefore expected to have rapid
translational impact for the countless pediatric diseases and disorders presently evaluated by MRI. The ability
to image young children without the use of sedation and anesthesia will dramatically decrease the time, cost
and risk involved in generating diagnostically useful images with MRI.
项目总结
项目成果
期刊论文数量(34)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(1)
Gradient-Guided Isotropic MRI Reconstruction from Anisotropic Acquisitions.
- DOI:10.1109/tci.2021.3128745
- 发表时间:2021
- 期刊:
- 影响因子:5.4
- 作者:Sui Y;Afacan O;Jaimes C;Gholipour A;Warfield SK
- 通讯作者:Warfield SK
Deep learning-based parameter estimation in fetal diffusion-weighted MRI.
- DOI:10.1016/j.neuroimage.2021.118482
- 发表时间:2021-11
- 期刊:
- 影响因子:5.7
- 作者:Karimi D;Jaimes C;Machado-Rivas F;Vasung L;Khan S;Warfield SK;Gholipour A
- 通讯作者:Gholipour A
Analytic quantification of bias and variance of coil sensitivity profile estimators for improved image reconstruction in MRI.
对线圈灵敏度分布估计器的偏差和方差进行分析量化,以改进 MRI 中的图像重建。
- DOI:10.1007/978-3-319-24571-3_82
- 发表时间:2015
- 期刊:
- 影响因子:0
- 作者:Stamm,Aymeric;Singh,Jolene;Afacan,Onur;Warfield,SimonK
- 通讯作者:Warfield,SimonK
Fetal Echoplanar Imaging: Promises and Challenges.
- DOI:10.1097/rmr.0000000000000219
- 发表时间:2019-10-01
- 期刊:
- 影响因子:0
- 作者:Afacan, Onur;Estroff, Judy A;Gholipour, Ali
- 通讯作者:Gholipour, Ali
Head motion measurement and correction using FID navigators.
- DOI:10.1002/mrm.27381
- 发表时间:2019-01
- 期刊:
- 影响因子:3.3
- 作者:Wallace TE;Afacan O;Waszak M;Kober T;Warfield SK
- 通讯作者:Warfield SK
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SIMON K WARFIELD其他文献
SIMON K WARFIELD的其他文献
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{{ truncateString('SIMON K WARFIELD', 18)}}的其他基金
Motion Compensated fMRI for Pre-Surgical Planning in Epilepsy
用于癫痫手术前规划的运动补偿功能磁共振成像
- 批准号:
10659634 - 财政年份:2023
- 资助金额:
$ 57.65万 - 项目类别:
Machine learning algorithms to analyze large medical image datasets
用于分析大型医学图像数据集的机器学习算法
- 批准号:
10434022 - 财政年份:2021
- 资助金额:
$ 57.65万 - 项目类别:
Machine learning algorithms to analyze large medical image datasets
用于分析大型医学图像数据集的机器学习算法
- 批准号:
10182522 - 财政年份:2021
- 资助金额:
$ 57.65万 - 项目类别:
Machine learning algorithms to analyze large medical image datasets
用于分析大型医学图像数据集的机器学习算法
- 批准号:
10584569 - 财政年份:2021
- 资助金额:
$ 57.65万 - 项目类别:
Novel MRI Imaging Tools and Software for Assessing Pediatric Crohn's Disease
用于评估儿童克罗恩病的新型 MRI 成像工具和软件
- 批准号:
8997501 - 财政年份:2014
- 资助金额:
$ 57.65万 - 项目类别:
Novel MRI Imaging Tools and Software for Assessing Pediatric Crohn's Disease
用于评估儿童克罗恩病的新型 MRI 成像工具和软件
- 批准号:
9212806 - 财政年份:2014
- 资助金额:
$ 57.65万 - 项目类别:
MRI Biomarkers of Patients with Tuberous Sclerosis Complex and Autism
结节性硬化症和自闭症患者的 MRI 生物标志物
- 批准号:
9315944 - 财政年份:2013
- 资助金额:
$ 57.65万 - 项目类别:
MRI Biomarkers of Patients with Tuberous Sclerosis Complex and Autism
结节性硬化症和自闭症患者的 MRI 生物标志物
- 批准号:
9112028 - 财政年份:2013
- 资助金额:
$ 57.65万 - 项目类别:
MRI Biomarkers of Patients with Tuberous Sclerosis Complex and Autism
结节性硬化症和自闭症患者的 MRI 生物标志物
- 批准号:
8896887 - 财政年份:2013
- 资助金额:
$ 57.65万 - 项目类别:
MRI Biomarkers of Patients with Tuberous Sclerosis Complex and Autism
结节性硬化症和自闭症患者的 MRI 生物标志物
- 批准号:
8705058 - 财政年份:2013
- 资助金额:
$ 57.65万 - 项目类别:
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