Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography.
Propagation-based X-ray phase-contrast tomography (PCT) seeks to reconstruct information regarding the complex-valued refractive index distribution of an object. In many applications, a boundary-enhanced image is sought that reveals the locations of discontinuities in the real-valued component of the refractive index distribution. We investigate two iterative algorithms for few-view image reconstruction in boundary-enhanced PCT that exploit the fact that a boundary-enhanced PCT image, or its gradient, is often sparse. In order to exploit object sparseness, the reconstruction algorithms seek to minimize the l(1)-norm or TV-norm of the image, subject to data consistency constraints. We demonstrate that the algorithms can reconstruct accurate boundary-enhanced images from highly incomplete few-view projection data.
Sidky
Emil Y
EY
Department of Radiology, The University of Chicago, Chicago, IL 60637, USA.
Anastasio
Mark A
MA
Pan
Xiaochuan
X
D000465:Algorithms; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012031:Refractometry / Q000379:methods*; D014054:Tomography / Q000379:methods*; D014961:X-Ray Diffraction / Q000379:methods*