The perfusion imaging using C-arm CT could be used intraoperatively for liver cancer treatment planning and evaluation. To deal with undersampled data due to slow C-arm CT rotation and pause between the rotations, we applied model-based reconstruction methods. Recent works using the time separation technique with an analytical basis function set have led to a significant improvement in the quality of C-arm CT perfusion maps. In this work we apply the time separation technique with a prior knowledge basis function set extracted using singular value decomposition from CT perfusion reconstructions. On C-arm CT liver perfusion scan simulated based on the real CT liver perfusion scan we show that the bases extracted from only two CT perfusion scans are capable of modeling the C-arm CT data correctly.
Dynamic dose modulation techniques are applied in CT acquisitions to ensure optimal working conditions of the detector unit and to reduce overall radiation exposure of the patient. In C-arm CT systems, large variations in the desired irradiation may require tube voltage modulation (TVM). Recent studies showed that TVM does not affect the quality of perfusion images obtained by clinical CT. Here, we investigate the impact of TVM in a C-arm cone beam CT perfusion imaging setting. We conduct a simulation study based on a real perfusion acquisition (incl. tube modulation) to directly compare results from acquisitions with and without modulation. Using two different reconstruction techniques, we analyze the influence of TVM on the extracted perfusion parameters and quantify the similarity by their correlation coefficients. Our results demonstrate that high correlation (r < 0.99) between the results with and without TVM are achieved for all perfusion parameters using a straightforward and model-based reconstruction technique. These findings suggest that dose modulation techniques, incl. TVM, can be used in C-arm CT perfusion scans without the need for additional correction methods to retain image quality of constant voltage scans.
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