In　dynamic　computed　tomography　(CT)　reconstruction,　the　data　acquisition　speed　limits　the　spatio-temporal　resolution.　Recently,　compressed　sensing　theory　has　been　instrumental　in　improving　CT　reconstruction　from　far　few-view　projections.　In　this　paper,　we　present　an　adaptive　method　to　train　a　tensor-based　spatio-temporal　dictionary　for　sparse　representation　of　an　image　sequence　during　the　reconstruction　process.　The　correlations　among　atoms　and　across　phases　are　considered　to　capture　the　characteristics　of　an　object.　The　reconstruction　problem　is　solved　by　the　alternating　direction　method　of　multipliers.　To　recover　fine　or　sharp　structures　such　as　edges,　the　nonlocal　total　variation　is　incorporated　into　the　algorithmic　framework.　Preclinical　examples　including　a　sheep　lung　perfusion　study　and　a　dynamic　mouse　cardiac　imaging　demonstrate　that　the　proposed　approach　outperforms　the　vectorized　dictionary-based　CT　reconstruction　in　the　case　of　few-view　reconstruction.