[特邀报告]Quantitative Spectral Computed Tomography - 报告详情

Quantitative Spectral Computed Tomography
编号:25 访问权限:仅限参会人 更新:2021-11-02 19:30:44 浏览:174次 特邀报告

报告开始:2021年11月15日 00:10 (Asia/Shanghai)

报告时间:25min

所在会议:[PS1] Plenary Session 1 [CT1] Workshop on CT

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摘要
While diagnostic spectral CT has been developed, there remains little effort in developing spectral imaging capability on cone-beam CT (CBCT). As CBCT has found increasingly important applications for surgical guidance and assessment in interventional radiology, radiation therapy, and orthopedic procedures, it is recognized that there is a need to develop spectral imaging capability on CBCT. In the presentation, using quantitative dual-energy CT (QDECT) as an example, I report some of our recent research on the development of algorithm-enabled spectral capability on conventional CBCT consisting of widely commodity components without involving hardware additions/modifications. optimization-based algorithms for accurate image reconstruction in QDECT. Evidence will be provided to show that the algorithms developed can potentially be exploited for enabling innovative design of QDECT and its scanning configurations of practical application significance.

If time allows, I will also discuss the claim in literature that machine learning (ML), neural network (NN), deep learning (DL) or artificial intelligence (AI) can solve an inverse problem in CT. Specifically, I will share with the audience recent results of the AAPM Grand Challenge on ML/NN/DL for sparse-view image reconstructions.
While diagnostic spectral CT has been developed, there remains little effort in developing spectral imaging capability on cone-beam CT (CBCT). As CBCT has found increasingly important applications for surgical guidance and assessment in interventional radiology, radiation therapy, and orthopedic procedures, it is recognized that there is a need to develop spectral imaging capability on CBCT. In the presentation, using quantitative dual-energy CT (QDECT) as an example, I report some of our recent research on the development of algorithm-enabled spectral capability on conventional CBCT consisting of widely commodity components without involving hardware additions/modifications. optimization-based algorithms for accurate image reconstruction in QDECT. Evidence will be provided to show that the algorithms developed can potentially be exploited for enabling innovative design of QDECT and its scanning configurations of practical application significance.

If time allows, I will also discuss the claim in literature that machine learning (ML), neural network (NN), deep learning (DL) or artificial intelligence (AI) can solve an inverse problem in CT. Specifically, I will share with the audience recent results of the AAPM Grand Challenge on ML/NN/DL for sparse-view image reconstructions.
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报告人
Xiaochuan Pan
Professor The University of Chicago

Professor in the Department of Radiology
*Department of Radiation & Cellular Oncology
*The Committee on Medical Physics, the Comprehensive Cancer Center
*The College at The University of Chicago.

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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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