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Abstract/Project Summary The objective of this program is to improve cone-beam computed tomography (CBCT) imaging performed on an interventional C-arm for guiding catheter-directed embolization of liver tumor in particular, and multiple other interventional procedures. The current standard of care for hepatic catheter directed therapy uses an interventional C-arm and injected contrast agent to visualize tumor and its feeding vessels. C-arm CBCT can be used to understand the lesion anatomy, allowing 3D visualization of the tumor and feeding vessels. However, current C-arm CBCT is far from being optimized for the application due to a number of important limitations concerning exam time, radiation dose, image quality, and intrusiveness to workflow. These limitations can be addressed through the development of innovative CBCT scans and optimization-based reconstructions. In the project, we focus on tackling the practical limitations of the C-arm CBCT in image- guided liver-tumor catheter delivery through the development of innovative scans and optimization-based reconstructions, and demonstrating their translational potential to clinical tasks of image-guided liver-tumor treatment. The specific aims of the project include (1) to develop optimization-based reconstruction for improving current C-arm imaging, (2) to develop scans and optimization-based reconstruction for further improving C-arm imaging, and (3) to verify, characterize, and evaluate optimization-based reconstructions. The clinical and technical significances of the project are high, because it can directly impact significantly intervention and treatment of liver-cancer and other forms of cancer, particularly, in the lung and kidney, and because it can lead to the development of advanced C-arm CBCT technology for addressing technical challenges such as sparse-view, truncation, and limited-angular-range problems of extremely high practical merit. The project has a high level of innovation, because it includes numerous creative ideas and methods for enhancing workflow and for reducing contrast volume, radiation dose, and patient discomfort.
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Algorithm-Enabled Auto-Calibrating Quantitative Dual-Energy CT
  • 批准号:
    10448987
  • 项目类别:
  • 资助金额:
    $22.83万
  • 财政年份:
    2022
  • 负责人:
    XIAOCHUAN PAN
  • 依托单位:
Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01
  • 批准号:
    9978584
  • 项目类别:
  • 资助金额:
    $52.85万
  • 财政年份:
    2018
  • 负责人:
    XIAOCHUAN PAN
  • 依托单位:
Advanced iterative image reconstruction for digital breast tomosynthesis - Resubmission 01
  • 批准号:
    10224861
  • 项目类别:
  • 资助金额:
    $51.79万
  • 财政年份:
    2018
  • 负责人:
    XIAOCHUAN PAN
  • 依托单位:
36th Annual International Conference of the IEEE Engineering in Medicine and Biol
  • 批准号:
    8720474
  • 项目类别:
  • 资助金额:
    $1.0万
  • 财政年份:
    2014
  • 负责人:
    XIAOCHUAN PAN
  • 依托单位:
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