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  • Open Access

1 December 2017

Ultra-Low-Dose Sparse-View Quantitative CT Liver Perfusion Imaging

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1
Imaging Research Laboratories, Robarts Research Institute, London, ON, Canada
2
Imaging Program, Lawson Health Research Institute, London, ON, Canada
3
Department of Medical Imaging, Western University, London, ON, Canada
4
CT Engineering, GE Healthcare, Waukesha, WI, USA

Abstract

Radiation dose of computed tomography liver perfusion imaging can be reduced by collecting fewer x-ray projections in each gantry rotation, but the resulting aliasing artifacts could affect the hepatic perfusion measurement. We investigated the effect of projection undersampling on the assessment of hepatic arterial blood flow (HABF) in hepatocellular carcinoma (HCC) when dynamic contrast-enhanced (DCE) liver images were reconstructed with filtered backprojection (FBP) and compressed sensing (CS). DCE liver images of a patient with HCC acquired with a 64-row CT scanner were reconstructed from all the measured projections (984-view) with the standard FBP and from one-third (328-view) and one-fourth (246-view) of all available projections with FBP and CS. Each of the 5 sets of DCE liver images was analyzed with a model-based deconvolution algorithm from which HABF maps were generated and compared. Mean HABF in the tumor and normal tissue measured by the 328-view CS and FBP protocols was within 5% differences from that assessed by the reference full-view FBP protocol. In addition, the tumor size measured by using the 328-view CS and FBP average images was identical to that determined by using the full-view FBP average image. By contrast, both the 246-view CS and FBP protocols exhibited larger differences (>20%) in anatomical and functional assessments compared with the full-view FBP protocol. The preliminary results suggested that computed tomography perfusion imaging in HCC could be performed with 3 times less projection measurement than the current full-view protocol (67% reduction in radiation dose) when either FBP or CS was used for image reconstruction.

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