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.
The steadily growing computational power of modern hardware allows use of more sophisticated reconstruction
methods. We present an implementation of the maximum likelihood (ML) method, a previously studied method,
for the case of a flat-panel rotational X-ray device. Contrary to the related principle of algebraic reconstruction
(ART), the ML method takes into consideration the physical properties of X-radiation, especially the corpuscular
character and the associated Poisson distribution of the measured number of photons. The basic principle is the
maximization of the joint probability of all measured projections with respect to the attenuation coefficients of
all voxels of the object. The application of the ML optimization procedure finally generates an iterative scheme
for the update of the attenuation coefficients. For this, in each step an accurate estimation of the forward
projections (FP) is mandatory. We use an approximate calculation of the footprints of single voxels based on
separable trapezoids. The resulting enormous computational effort is handled by an efficient implementation
on GPGPU (General-purpose computing on graphics processing units). As a first look, using data from 133
projections of a sheep head acquired by means of a flat-panel rotational angiography system, we compare the
reconstruction by the ML-based method with the gold standard - the Feldkamp filtered back projection (FBP)
procedure. The results reveal a clearly reduced amount of streak artifacts as well as less blurring in the statistical
reconstruction method.
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