Tissue perfusion measurement using C-arm angiography sys-tems capable of CT-like imaging (C-arm CT) is a novel tech-nique with potentially high benefit for catheter-guided treat-ment of stroke in the interventional suite. New rapid scan-ning protocols with increased C-arm rotation speed enable fast acquisitions of C-arm CT volumes and allow for sampling the contrast flow with improved temporal resolution. How-ever, the peak contrast attenuation values of brain tissue lie typically in a range of 5–30 HU. Thus perfusion imaging is very sensitive to noise. In this work we compare different denoising algorithms based on the algebraic reconstruction technique (ART) and introduce a novel denoising technique, which requires only iterative filtering in volume space and is computationally much more attractive. Our evaluation using a realistic digital brain phantom shows that all methods im-prove the perfusion maps perceptibly compared to Feldkamp-type (FDK) reconstruction. The volume-based technique per-forms similarly to the ART-based methods: the Pearson cor-relation of reference and reconstructed blood flow maps in-creases from 0.61 for the FDK method to 0.81 for the best ART method and to 0.79 for the volume-based method. Fur-thermore results from a canine stroke model study are shown. Index Terms — Perfusion imaging, iterative reconstruc-tion, C-arm CT, stroke treatment

Abstract—Tissue perfusion measurement using C-arm angiog-raphy 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. Index Terms—Perfusion imaging, dynamic reconstruction, C-arm CT, stroke treatment

Abstract—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. Recently we presented a dynamic, iterative reconstruction (DIR) approach to reconstruct TACs described by a weighted sum of linear spline functions with a regularization based on joint bilateral filtering (JBF). In this work we incorporate statistical ray weighting into the algorithm and show how this helps to improve the reconstructed cerebral blood flow (CBF) maps in a simulation study with a realistic dynamic brain phantom. The Pearson correlation of the CBF maps to ground truth maps increases from 0.85 (FDK), 0.87 (FDK with JBF), and 0.90 (DIR with JBF) to 0.92 (DIR with JBF and ray weighting). The results suggest that the statistical ray weighting approach improves the diagnostic accuracy of PCCT based on DIR. I.