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Proceedings Paper

Sparse-view CT perfusion with filtered back projection image reconstruction
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Paper Abstract

CT perfusion (CTP) efficiently provides valuable hemodynamic information for triage of acute ischemic stroke patients at the expense of additional radiation dose from consecutive CT acquisitions. Low-dose CTP is therefore highly desirable but is often attempted by iterative or deep learning reconstructions that are computationally intensive. We aimed to demonstrate that acquiring fewer x-ray projections in a CTP scan while reconstructing with filtered back projection (FBP) can reduce radiation dose without impacting clinical utility. Six CTP studies were selected from the PRove-IT clinical database. For each axial source CTP slice, a 984-view sinogram was synthesized using a Radon Transform and uniformly under-sampled to 492, 328, 246, and 164-views. An FBP was applied on each sparse-view sinogram to reconstruct source images that were used to generate perfusion maps using a delay-insensitive deconvolution algorithm. The resulting Tmax and cerebral blood flow perfusion maps were evaluated for their ability to identify penumbra and ischemic core volumes using the Pearson correlation (R) and Bland-Altman analysis. In addition, sparse-view perfusion maps were assessed for fidelity to original full-view maps using structural similarity, peak signal-to-noise ratio, and normalized root mean squared error. Ischemic penumbra and infarct core volumes were accurately estimated by all sparse-view configurations (R<0.95, p<0.001; mean difference <3 ml) and overall perfusion map fidelity was well-maintained up to 328-views. Our preliminary analysis reveals that radiation dose can potentially be reduced by a factor of 6 with further validation that the errors in ischemic volume measurement do not impact clinical decision-making.

Paper Details

Date Published: 28 February 2020
PDF: 7 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170P (28 February 2020); doi: 10.1117/12.2549121
Show Author Affiliations
Kevin J. Chung, Univ. of Western Ontario (Canada)
Bijoy K. Menon, Univ. of Calgary (Canada)
Ting-Yim Lee, Univ. of Western Ontario (Canada)
Lawson Health Research Institute (Canada)
Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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