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This report develops a new strategy for the acceleration of a maximum likelihood (ML) iterative reconstruction algorithm for CT, by selecting a starting image which is closer to the solution of the ML algorithm than the commonly used filtered backprojection image. The starting image is obtained by passing both the acquired projection data and the reconstructed volume though a novel de-noising algorithm which uses the same image penalty function as the ML reconstruction. Clinical examples suggest that a savings of 5-10 iterations of the separable paraboloidal surrogates algorithm per volume is possible when using this new acceleration strategy.

Differential phase-contrast imaging in the x-ray domain provides three physically complementary signals:1, 2the attenuation, the differential phase-contrast, related to the refractive index, and the dark-field signal, strongly influenced by the total amount of radiation scattered into very small angles. In medical applications, it is of the utmost importance to present to the radiologist all clinically relevant information in as compact a way as possible. Hence, the need arises for a method to combine two or more of the above mentioned signals into one image containing all information relevant for diagnosis. We present an image composition algorithm that fuses the attenuation image and the differential phase contrast image into a composite, final image based on the assumption that the real and imaginary part of the complex refractive index of the sample can be related by a constant scaling factor. The merging is performed in such a way that the composite image is characterized by minimal noise-power at each frequency component.