Dynamic iterative reconstruction for interventional 4-D C-arm CT perfusion imaging

Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (C-arm CT) is a novel technique with potentially high benefit for catheter guided treatment of stroke in the interventional suite. However, perfusion C-arm CT (PCCT) is challenging: the slow C-arm rotation speed only allows measuring samples of contrast time attenuation curves (TACs) every 5-6 s if reconstruction algorithms for static data are used. Furthermore, the peak values of the TACs in brain tissue typically lie in a range of 5-30 HU, thus perfusion imaging is very sensitive to noise. We present a dynamic, iterative reconstruction (DIR) approach to reconstruct TACs described by a weighted sum of basis functions. To reduce noise, a regularization technique based on joint bilateral filtering (JBF) is introduced. We evaluated the algorithm with a digital dynamic brain phantom and with data from six canine stroke models. With our dynamic approach, we achieve an average Pearson correlation (PC) of the PCCT canine blood flow maps to co-registered perfusion CT maps of 0.73. This PC is just as high as the PC achieved in a recent PCCT study, which required repeated injections and acquisitions.