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Improved Motion Robust MRI of Children

Improved Motion Robust MRI of Children
改进儿童运动鲁棒性 MRI
批准号:
10605154
负责人:
SIMON K WARFIELD
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
项目状态:
未结题
起止时间:
2015-07-01 至 2025-03-31

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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.
期刊论文(34)
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科研奖励(0)
会议论文
DOI: 10.1109/tci.2021.3128745
发表时间: 2021
期刊: IEEE transactions on computational imaging
影响因子: 5.4
作者: [Sui Y, Afacan O, Jaimes C, Gholipour A, Warfield SK]
通讯作者: Warfield SK
DOI: 10.1016/j.neuroimage.2021.118482
发表时间: 2021-11
期刊: NeuroImage
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: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子: --
作者: [Stamm,Aymeric, Singh,Jolene, Afacan,Onur, Warfield,SimonK]
通讯作者: Warfield,SimonK
DOI: 10.1097/rmr.0000000000000219
发表时间: 2019-10-01
期刊: Topics in magnetic resonance imaging : TMRI
影响因子: --
作者: [Afacan, Onur, Estroff, Judy A, Gholipour, Ali]
通讯作者: Gholipour, Ali
23
    Motion Compensated fMRI for Pre-Surgical Planning in Epilepsy
    • 批准号:
      10659634
    • 项目类别:
    • 资助金额:
      $67.11万
    • 财政年份:
      2023
    • 负责人:
      SIMON K WARFIELD
    • 依托单位:
    Machine learning algorithms to analyze large medical image datasets
    • 批准号:
      10434022
    • 项目类别:
    • 资助金额:
      $37.61万
    • 财政年份:
      2021
    • 负责人:
      SIMON K WARFIELD
    • 依托单位:
    Machine learning algorithms to analyze large medical image datasets
    • 批准号:
      10182522
    • 项目类别:
    • 资助金额:
      $36.96万
    • 财政年份:
      2021
    • 负责人:
      SIMON K WARFIELD
    • 依托单位:
    Machine learning algorithms to analyze large medical image datasets
    • 批准号:
      10584569
    • 项目类别:
    • 资助金额:
      $37.61万
    • 财政年份:
      2021
    • 负责人:
      SIMON K WARFIELD
    • 依托单位:
    海外基金