compressive sensing (CS) related research
CS issues in tomography
CS issues in tomography:
digital breast tomosynthesis
A CS application to clinical data for digital breast tomosynthesis (DBT).
few-view, circular cone-beam (available upon request)
Explains, in quite some detail, the ASD-POCS (adaptive steepest descent-projection onto convex sets) algorithm.
Although the data are computer-simulated projections of the FORBILD jaw phantom (phantom group at Erlangen),
this work performs image-reconstruction for cone-beam computed tomography (CT) systems with realistic dimensions.
A big constraint on CS algorithms for computed tomography is the enormous size of the reconstructed images; this
paper dealt with images containing 4003 voxels.
Another important issue, addressed here, is that the data are generated from a continuous jaw model,
but the image representation is discrete (a voxel array). This
mismatch can create trouble for the application of CS methods.
A slice-image comparison of CS-based image-reconstruction, left column, and a standard iterative algorithm, right column.
The tumor, modeled as a low-contrast sphere, is only visible on the left.
diffraction tomography (available upon request)
Application of CS to wave imaging, using simulated data.
Diagram of diffraction tomography data model
Our first attempt at CS in CT, using simulated data with the Shepp-Logan phantom.
The algorithm is primitive, but at least it explains the system model for CT in detail.
fan-beam configuration of simulated CT-scanner