课题基金 / 基金详情

项目摘要

项目成果

Chi Liu的其他基金

相似基金

相关文献

中文摘要
翻译
项目摘要
英文摘要
Project Abstract PET plays an important role in cancer management. However, image blurring and mismatched attenuation correction due to respiratory motion can substantially degrade detection efficacy and quantification accuracy for tumors located in the lung and abdomen. Existing motion correction methods might provide satisfactory results for patients with regular breathing patterns, which account for about 60% of patients. However, for the remaining 40% of patients with irregular breathing patterns, these methods neglect the major effects of intra-gate motion due to inter-cycle and intra-cycle motion variations. In addition, as dose reduction in PET imaging has become increasingly important, existing motion correction methods typically amplify image noise and degrade their performances on low-count data. Another important challenge is the mismatch between CT and PET that limits phase-matched attenuation correction for every gated PET image using a single helical CT. Therefore, to achieve accurate quantification for evaluation of response to cancer therapy and reliable detection of tumors using low-dose PET protocols, particularly for patients with breathing pattern changes including variable motion amplitude, baseline variation, and amplitude variation, it is critical to develop personalized motion correction strategies optimized for individual patient's breathing patterns and the imaging task to eliminate intra-gate motion and mismatched attenuation correction for low- dose PET. Extending our existing collaboration, Yale and Siemens form an ideal team to optimize a comprehensive solution to correct for breathing pattern variability with intrinsically phase-matched attenuation correction for both regular and irregular breathers in the first two Aims. We will then develop and translate a personalized strategy to automatically identify the most time-effective motion correction approach for each individual patient, considering task and breathing pattern. We will optimize our personalized motion correction methods and strategy particularly for low-count PET data, aiming to reduce radiation dose to 25%-50% of the dose in current PET protocols. The outcome of this research will be a comprehensive motion correction package including four correction approaches and a personalized strategy that is automatically optimized for each individual patient. This development will be ready to translate to commercial PET/CT scanners and clinical end-users. As existing motion correction methods only apply to ~60% regular breathers, but have substantial limitation for the remaining ~40% irregular breathers, our proposed development can provide a unified motion correction framework for all patients with both regular and irregular breathing. This fast translation with industrial partners can lead to a significant and timely clinical impact for cancer management.
期刊论文(6)
专著(0)
科研奖励(0)
会议论文
MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.
MCP-Net:将 Patlak 损失优化引入全身动态 PET 帧间运动校正。
DOI: 10.1109/tmi.2023.3290003
发表时间: 2023
期刊: IEEE transactions on medical imaging
影响因子: 10.6
作者: [Guo,Xueqi, Zhou,Bo, Chen,Xiongchao, Chen,Ming-Kai, Liu,Chi, Dvornek,NichaC]
通讯作者: Dvornek,NichaC
TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.
TAI-GAN:用于动态心脏 PET 运动校正中早期到晚期帧转换的时间和解剖学信息 GAN。
DOI: 10.1007/978-3-031-44689-4_7
发表时间: 2023
期刊: Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)
影响因子: --
作者: [Guo,Xueqi, Shi,Luyao, Chen,Xiongchao, Zhou,Bo, Liu,Qiong, Xie,Huidong, Liu,Yi-Hwa, Palyo,Richard, Miller,EdwardJ, Sinusas,AlbertJ, Spottiswoode,Bruce, Liu,Chi, Dvornek,NichaC]
通讯作者: Dvornek,NichaC
MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET.
MCP-Net:针对全身动态 PET 的采用 Patlak 正则化的帧间运动校正。
DOI: 10.1007/978-3-031-16440-8_16
发表时间: 2022
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: [Guo,Xueqi, Zhou,Bo, Chen,Xiongchao, Liu,Chi, Dvornek,NichaC]
通讯作者: Dvornek,NichaC
Patient motion correction for dynamic cardiac PET: Current status and challenges.
动态心脏 PET 的患者运动校正:现状和挑战。
DOI: 10.1007/s12350-018-01513-x
发表时间: 2020
期刊: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
影响因子: --
作者: [Lu,Yihuan, Liu,Chi]
通讯作者: Liu,Chi
Multi-isotope Hybrid PET/CT Imaging of Peripheral Artery Disease in Diabetes
  • 批准号:
    10586846
  • 项目类别:
  • 资助金额:
    $83.74万
  • 财政年份:
    2022
  • 负责人:
    Chi Liu
  • 依托单位:
Development of advanced cardiac SPECT imaging technologies
  • 批准号:
    10064473
  • 项目类别:
  • 资助金额:
    $80.69万
  • 财政年份:
    2020
  • 负责人:
    Chi Liu
  • 依托单位:
Generation of parametric images for FDG PET using dual-time-point scans
  • 批准号:
    9896329
  • 项目类别:
  • 资助金额:
    $8.04万
  • 财政年份:
    2020
  • 负责人:
    Chi Liu
  • 依托单位:
Development of advanced cardiac SPECT imaging technologies
  • 批准号:
    10221049
  • 项目类别:
  • 资助金额:
    $80.53万
  • 财政年份:
    2020
  • 负责人:
    Chi Liu
  • 依托单位:
海外基金