19 March 2014 C-arm perfusion imaging with a fast penalized maximum-likelihood approach
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Abstract
Perfusion imaging is an essential method for stroke diagnostics. One of the most important factors for a successful therapy is to get the diagnosis as fast as possible. Therefore our approach aims at perfusion imaging (PI) with a cone beam C-arm system providing perfusion information directly in the interventional suite. For PI the imaging system has to provide excellent soft tissue contrast resolution in order to allow the detection of small attenuation enhancement due to contrast agent in the capillary vessels. The limited dynamic range of flat panel detectors as well as the sparse sampling of the slow rotating C-arm in combination with standard reconstruction methods results in limited soft tissue contrast. We choose a penalized maximum-likelihood reconstruction method to get suitable results. To minimize the computational load, the 4D reconstruction task is reduced to several static 3D reconstructions. We also include an ordered subset technique with transitioning to a small number of subsets, which adds sharpness to the image with less iterations while also suppressing the noise. Instead of the standard multiplicative EM correction, we apply a Newton-based optimization to further accelerate the reconstruction algorithm. The latter optimization reduces the computation time by up to 70%. Further acceleration is provided by a multi-GPU implementation of the forward and backward projection, which fulfills the demands of cone beam geometry. In this preliminary study we evaluate this procedure on clinical data. Perfusion maps are computed and compared with reference images from magnetic resonance scans. We found a high correlation between both images.
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Robert Frysch, Robert Frysch, Tim Pfeiffer, Tim Pfeiffer, Sebastian Bannasch, Sebastian Bannasch, Steffen Serowy, Steffen Serowy, Sebastian Gugel, Sebastian Gugel, Martin Skalej, Martin Skalej, Georg Rose, Georg Rose, } "C-arm perfusion imaging with a fast penalized maximum-likelihood approach", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90332M (19 March 2014); doi: 10.1117/12.2043450; https://doi.org/10.1117/12.2043450
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