课题基金 / 基金详情

Advanced Strategies for Image Quality Improvement and Dose Reduction in CT

Advanced Strategies for Image Quality Improvement and Dose Reduction in CT
提高 CT 图像质量和减少剂量的先进策略
批准号:
RGPIN-2014-05017
负责人:
Jaffray, David
金额:
$2.62万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

项目成果

Jaffray, David的其他基金

相似基金

相关文献

中文摘要
翻译
x射线计算机断层扫描(CT)已被证明是促进人类健康的关键和高度重视的工具。这些系统应用于医学的各个方面,从癌症筛查到损伤评估,最近还用于指导癌症的放射治疗和手术等治疗。现有的CT技术并没有充分利用底层物理的能力来最大限度地提高图像质量(例如,噪声对比)和最大限度地减少对患者的辐射剂量。拟议的研究项目建立在20多年的x射线成像方法和系统开发经验的基础上,开发下一代CT方法,利用计算能力,机电一体化和纳米技术的进步,为医学带来更安全,更高性能的CT成像水平。**研究项目主要集中在两个主要课题上:(i)校正探测器中不需要的x射线散射信号,(ii)开发一种新的影响场调制CT (FFMCT)方法。**到达探测器的散射x射线对图像质量有显著的负面影响,由于受照射的组织体积和缺乏抑制方法,这种影响在锥束CT (CBCT)中加剧。通过蒙特卡罗(MC)模拟对散射的物理基础进行精确建模,使我们能够更好地理解和纠正散射,从而改善图像和患者剂量管理。目前,MC模拟计算量大,耗时长。该项目将为基于mc的散射校正开发更有效的计算方法,从而使校正方法可以在现实世界的临床环境中实时应用,以补偿杂散信号并提高图像质量。**FFMCT是在我的实验室开发的,有可能通过根据成像任务的细节改变CT扫描仪的操作来改变CT技术。它涉及在图像采集过程中动态调整x射线通量模式,以便针对特定感兴趣的区域调整图像质量和辐射剂量。FFMCT能够解释物体中已知的x射线密度,例如骨骼的高密度,并减少对不感兴趣的敏感组织(例如肺部筛查中的乳腺组织)的剂量,因此应该提高现有的CT图像质量。使用FFMCT和高速MC方法的混合方法将允许探索其他CT几何形状,包括使用可编程多源x射线发射阵列进行成像。
英文摘要
X-ray computed tomography (CT) has proven to be a critical and highly valued tool in the advancement of human health. These systems are employed in every facet of medicine from screening for cancer, to assessing injuries, and most recently guiding treatments such as radiation therapy and surgery for cancer. Existing CT technology is not fully utilizing the capabilities of the underlying physics to maximize image quality (e.g. contrast-to-noise) and minimize the radiation dose to the patient. The proposed program of research builds upon 20+ years of experience in X-ray imaging methods and system development to develop next-generation CT methods that leverage advances in computational horsepower, mechatronics, and nanotechnology to bring a safer, higher performing level of CT imaging to medicine. **The program of research focuses on two major topics (i) correcting unwanted X-ray scatter signal in the detector, and (ii) the development of a novel fluence-field modulated CT (FFMCT) method. **Scattered X-rays reaching the detector have a significant negative impact on image quality and this is exacerbated in cone beam CT (CBCT) due to the volume of tissue irradiated and lack of a rejection method. Accurate modeling of the physics underlying scatter with Monte Carlo (MC) simulations allows us to better understand and correct for scatter, leading to improved images and patient dose management. At present, MC simulations are computationally intensive and time-consuming. This project will develop more efficient computational methods for MC-based scatter correction, leading to correction methods that can be applied in real-time in real-world clinical settings to compensate for spurious signal and improve image quality. **FFMCT was developed in my laboratory and has the potential to transform CT technology by changing the operation of the CT scanner depending on the details of the imaging task. It involves dynamically adjusting the X-ray fluence pattern during image acquisition so that the image quality and radiation dose are adjusted for specific regions-of-interest. FFMCT is able to account for known X-ray densities in the object, such as the high density of bone, and reduce dose to sensitive tissues not of interest (e.g. breast tissue in lung screening)-and as a result should improve existing CT image quality. The hybrid approach of using FFMCT and high-speed MC methods will allow alternative CT geometries to be explored, including the use of programmable multisource X-ray emitting arrays for imaging.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Advanced CT Strategies for Image Quality Improvement and Dose Reduction
  • 批准号:
    RGPIN-2019-06445
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $5.21万
  • 财政年份:
    2019
  • 负责人:
    Jaffray, David
  • 依托单位:
Advanced Strategies for Image Quality Improvement and Dose Reduction in CT
  • 批准号:
    RGPIN-2014-05017
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.62万
  • 财政年份:
    2017
  • 负责人:
    Jaffray, David
  • 依托单位:
Spatially encoded mass spectrometry data for intrasurgical pathology
  • 批准号:
    516328-2017
  • 项目类别:
    Engage Grants Program
  • 资助金额:
    $1.82万
  • 财政年份:
    2017
  • 负责人:
    Jaffray, David
  • 依托单位:
Advanced Strategies for Image Quality Improvement and Dose Reduction in CT
  • 批准号:
    RGPIN-2014-05017
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.62万
  • 财政年份:
    2016
  • 负责人:
    Jaffray, David
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis