Abstract
Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.
Highlights
X-ray computed tomography (CT) is one of the frontline imaging examinations used in disease diagnosis
In this paper we investigate image reconstruction for an interrupted-beam approach tailored for use on diagnostic clinical scanners, which we call ‘SparseCT’
All displayed images in figures 4 and 5 were reconstructed with the normalized root-mean-square difference (NRMSD)-optimal β for that method. The results of these reconstructions are shown in figures 4 and 5. Both tube-current reduction and SparseCT acquisitions show image degradation as the dose is reduced in the simulated images in figures 4 and 5
Summary
X-ray computed tomography (CT) is one of the frontline imaging examinations used in disease diagnosis. Tube-current reduction methods are the most common, but result in increased sinogram noise during acquisition This increased noise is commonly addressed by using advanced algorithms to minimize a cost function that takes into account statistics of the system noise and prior knowledge of the image volume. These algorithms can incorporate noise models in either the transmission (La Riviére et al 2006, Xu and Tsui 2009) or post-log domain (Thibault et al 2007, Beister et al 2012) for improved data consistency. These methods have been an ongoing area of extensive research in CT, with advances in algorithm design in recent years improving practicality for the clinic (Kim et al 2015, McGaffin and Fessler 2015, Nien and Fessler 2016)
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