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Proceedings Paper

Time-resolved C-arm cone beam CT angiography using SMART-RECON: quantification of temporal resolution and reconstruction accuracy
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Paper Abstract

Time-resolved cone beam CT angiography (CBCTA) imaging in the interventional suite has the potential to identify occluded vessels and the collaterals of symptomatic ischemic stroke patients. However, traditional C-arm gantries offer limited rotational speed and thus the temporal resolution is limited when the conventional filtered backprojection (FBP) reconstruction is used. Recently, a model based iterative image reconstruction algorithm: Synchronized MultiArtifact Reduction with Tomographic reconstruction (SMART-RECON) was proposed to reconstruct multiple CBCT image volumes per short-scan CBCT acquisition to improve temporal resolution. However, it is not clear how much temporal resolution can be improved using the SMART-RECON algorithm or what the corresponding reconstruction accuracy is. In this paper, a novel fractal tree based numerical timeresolved angiography phantom with ground truth temporal information was introduced to quantify temporal resolution using a temporal blurring model analysis along with other two quantification metrics introduced to quantify reconstruction accuracy: the relative root mean square error (rRMSE) and the Kullback-Leibler Divergence (DKL). The quantitative results show that the temporal resolution is 0.8 s for SMART-RECON and 3.6 s for the FBP reconstruction. The reconstruction fidelity with SMART-RECON was substantially improved with the rRMSE improved by at least 70% and the DKL was improved by at least 40%.

Paper Details

Date Published: 9 March 2018
PDF: 6 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731T (9 March 2018); doi: 10.1117/12.2292477
Show Author Affiliations
John Garrett, Univ. of Wisconsin-Madison (United States)
Yinsheng Li, Univ. of Wisconsin-Madison (United States)
Univ. of Wisconsin-Madison (United States)
Ke Li, Univ. of Wisconsin School of Medicine and Public Health (United States)
Sebastian Schafer, Siemens Medical Solutions USA, Inc. (United States)
Charles Strother, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin School of Medicine and Public Health (United States)
Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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