review of Tomosynthesis via Total Variation Minimization

the abstracts reads,
ABSTRACT
Recently, foundational mathematical theory, compressed sensing (CS), has been developed which enables accuratereconstruction from greatly undersampled frequency information (Candes et. al. and Donoho). Using numericalphantoms it has been demonstrated that CS reconstruction (e.g. minimizing the
1 norm of the discrete gradientof the image) offers promise for computed tomography. However, when using experimental CT projection data theundersampling factors enabled were smaller than in numerical simulations. An extension to CS has recently been proposed wherein a prior image is utilized as a constraint in the image reconstruction procedure (i.e. Prior ImageConstrained Compressed Sensing - PICCS). Experimental results are demonstrated here from a clinical C-armsystem, highlighting one application of PICCS in reducing radiation exposure during interventional procedureswhile preserving high image quality. In this study a range of view angles has been investigated from very limitedangle aquisitions (e.g. tomosythesis) to undersampled CT acquisitions.

some notes:
a good point of the paper, to get the image ahead as a prior image, then take the TV of the picture which obtained by 'Prior - reconstruct' as the sparse basis. obviously , such a sparse signal granteed.

such a idea could be used to reduce the x-ray dose. but there is not a Theoritical proof of the convergence of the reconstruction method.