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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