Fast dynamic reconstruction algorithm with joint bilateral filtering for perfusion C-arm CT

Tissue perfusion measurement using C-arm angiography systems is a novel technique with potential 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 peaks of the tissue TACs 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 linear spline functions. The optimization problem is solved using an appropriate initialization and a Landweber-based optimization strategy with a modified backprojection step. To reduce noise a novel regularization technique based on joint bilateral filtering (JBF) is introduced. The algorithm is evaluated using simulation data created with a dynamic cylindrical phantom, a realistic digital brain phantom and real measured data from an animal study with a canine stroke model. Results indicate that the DIR algorithm qualitatively and quantitatively improves reconstructed TACs and perfusion maps compared to classical Feldkamp (FDK) reconstruction. For the brain phantom study the Pearson correlation (PC) of the reconstructed cerebral blood flow (CBF) maps to the ground truth increased from 0.82 (FDK) to 0.87 (DIR). For the canine study the PC of the CBF maps to coregistered perfusion CT maps increased from 0.61 (FDK) to 0.73 (DIR).