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In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the
measured data provide redundant information, composite image data with high SNR can be computed. Generally, these
composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution.
This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution)
‘source’ image data.
It was shown that the SNR of CT image data can be improved using iterative reconstruction [1] .We present a novel
iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region.
The generalized update equation is formulated in image space without further referring to raw data after initial
reconstruction of source and composite image data. The update equation consists of a linear combination of the previous
update, a correction term constrained by the source data, and a regularization prior initialized by the composite data.
The efficiency of the method is demonstrated for different applications: (i) Spectral imaging: we have analysed material
decomposition data from dual energy data of our photon counting prototype scanner: the material images can be
significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material
images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise
statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution:
splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window
optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac
imaging: the optimal phase image (‘best phase’) can be improved by transferring all applied over radiation into that
image.
In all these cases, we show that - at constant patient dose - SNR can efficiently be transferred from the composite data to
the source data while maintaining spatial, temporal and contrast resolution properties of the source data.
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