Multi-slice tomographic reconstruction

Gopal, Preeti; Chandran, Sharat; Svalbe, Imants; Rajwade, Ajit

doi:10.1145/3009977.3010054

Cited by 2 publications

(1 citation statement)

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“…Our previous work [2] partitioned a volume comprised of consecutive 2D slices into local groups of slices before performing the reconstruction of this volume from a sparse set of projected views. The grouping of these slices was based on their structural similarity, as measured through a comparison of the invariant moments of their projected views.…”

Section: Introductionmentioning

confidence: 99%

Tomographic Reconstruction Using Global Statistical Priors

Gopal

Chaudhry

Chandran

et al. 2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the increasingly sparse sets of measurements is to exploit the information from templates, i.e., prior data available in the form of already reconstructed, structurally similar images. Towards this, previous work has exploited using a set of global and patch based dictionary priors. In this paper, we propose a global prior to improve both the speed and quality of tomographic reconstruction within a Compressive Sensing framework.We choose a set of potential representative 2D images referred to as templates, to build an eigenspace; this is subsequently used to guide the iterative reconstruction of a similar slice from sparse acquisition data. Our experiments across a diverse range of datasets show that reconstruction using an appropriate global prior, apart from being faster, gives a much lower reconstruction error when compared to the state of the art.

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Section: Introductionmentioning

confidence: 99%