The　high　utility　and　wide　applicability　of　x-ray　imaging　has　led　to　a　rapidly　increased　number　of　CT　scans　over　the　past　years,　and　at　the　same　time　an　elevated　public　concern　on　the　potential　risk　of　x-ray　radiation　to　patients.　Hence,　a　hot　topic　is　how　to　minimize　x-ray　dose　while　maintaining　the　image　quality.　The　low-dose　CT　strategies　include　modulation　of　x-ray　flux　and　minimization　of　dataset　size.　However,　these　methods　will　produce　noisy　and　insufficient　projection　data,　which　represents　a　great　challenge　to　image　reconstruction.　Our　team　has　been　working　to　combine　statistical　iterative　methods　and　advanced　image　processing　techniques,　especially　dictionary　learning,　and　have　produced　excellent　preliminary　results.　In　this　paper,　we　report　recent　progress　in　dictionary　learning　based　low-dose　CT　reconstruction,　and　discuss　the　selection　of　regularization　parameters　that　are　crucial　for　the　algorithmic　optimization.　The　key　idea　is　to　use　a　＂balancing　principle＂　based　on　a　model　function　to　choose　the　regularization　parameters　during　the　iterative　process,　and　to　determine　a　weight　factor　empirically　for　address　the　noise　level　in　the　projection　domain.　Numerical　and　experimental　results　demonstrate　the　merits　of　our　proposed　reconstruction　approach.