CT imaging is useful and ubiquitous. There is, however, a desire to reduce imaging artifacts, improve resolution, while reducing radiation. Iterative reconstruction algorithms have been proposed as one approach towards achieving these goals. In this paper we compare phantom images produced using commercial FBP-based reconstruction to three different iterative algorithms. We focus specifically on statistical characterizations of the noise, both at full radiation dose and at 50% dose. An iterative algorithm which segregates the image into two components (soft tissue and dense object), and imposes different constraints on these components, yielded better noise characteristics than ART, total variation, and FBP.
A common problem arising in medical imaging is the suppression of undesired image artifacts with the simultaneous
preservation of salient clinical information. Often the proposed processing "cure" introduces its own artifacts in other
parts of the image that confound reliable diagnosis. A canonical example is the suppression of artifacts from hyperdense
objects, such as metal and calcium. In this paper we propose a new decomposition-based approach to the combined
image formation and the suppression of localized image artifacts which is motivated by recent results on image
inpainting. The approach, which we term Model-Based Algebraic Iteration (MBAI) processing, decomposes an image
into a collection of homogeneous components, each of which can be reconstructed in the manner most appropriate to its
underlying nature. Because each component is localized, the effects of processing on that component do not contaminate
other areas of the image. Our specific motivation is the mitigation of artifacts in cardiac multi-detector computed
tomography (MDCT) images.
Recently, MDCT has offered the promise of a non-invasive alternative to invasive coronary angiography to evaluate
coronary artery disease. An impediment preventing its utilization as a routine clinical replacement for angiography is the
presence of image "blooming" artifacts due to the presence vascular calcium. We develop MBAI for the purpose of
ameliorating artifacts in cardiac images and thus increase the applicability of MDCT for the evaluation of at-risk patient
population. We demonstrate preliminary results in the reduction of the calcium blooming-effect in software simulation,
phantom, ex-vivo, and in-vivo MDCT data.
Modern CT systems have advanced at a dramatic rate. Algebraic iterative reconstruction techniques have shown
promising and desirable image characteristics, but are seldom used due to their high computational cost for complete
reconstruction of large volumetric datasets. In many cases, however, interest in high resolution reconstructions is
restricted to smaller regions of interest within the complete volume. In this paper we present an implementation of a
simple and practical method to produce iterative reconstructions of reduced-sized ROI from 3D helical tomographic
data. We use the observation that the conventional filtered
back-projection reconstruction is generally of high quality
throughout the entire volume to predict the contributions to
ROI-related projections arising from volumes outside the
ROI. These predictions are then used to pre-correct the data to produce a tomographic inversion problem of
substantially reduced size and memory demands. Our work expands on those of other researchers who have observed
similar potential computational gains by exploiting FBP results. We demonstrate our approach using cardiac CT cone-beam
imaging, illustrating our results with both <i>ex vivo</i> and <i>in vivo</i> multi-cycle EKG-gated examples.