Multi-modality　cardiac　imaging　plays　a　key　role　in　the　management　of　patients　with　cardiovascular　diseases.　It　allows　a　combination　of　complementary　anatomical,　morphological　and　functional　information,　increases　diagnosis　accuracy,　and　improves　the　efficacy　of　cardiovascular　interventions　and　clinical　outcomes.　Fully　automated　processing　and　quantitative　analysis　of　multi-modality　cardiac　images　could　have　a　direct　impact　on　clinical　research　and　evidence-based　patient　management.　However,　these　require　overcoming　significant　challenges　including　inter-modality　misalignment　and　finding　optimal　methods　to　integrate　information　from　different　modalities.　This　paper　aims　to　provide　a　comprehensive　review　of　multi-modality　imaging　in　cardiology,　the　computing　methods,　the　validation　strategies,　the　related　clinical　workflows　and　future　perspectives.　For　the　computing　methodologies,　we　have　a　favored　focus　on　the　three　tasks,　i.e.,　registration,　fusion　and　segmentation,　which　generally　involve　multi-modality　imaging　data,　either　combining　information　from　different　modalities　or　transferring　information　across　modalities.　The　review　highlights　that　multi-modality　cardiac　imaging　data　has　the　potential　of　wide　applicability　in　the　clinic,　such　as　trans-aortic　valve　implantation　guidance,　myocardial　viability　assessment,　and　catheter　ablation　therapy　and　its　patient　selection.　Nevertheless,　many　challenges　remain　unsolved,　such　as　missing　modality,　modality　selection,　combination　of　imaging　and　non-imaging　data,　and　uniform　analysis　and　representation　of　different　modalities.　There　is　also　work　to　do　in　defining　how　the　well-developed　techniques　fit　in　clinical　workflows　and　how　much　additional　and　relevant　information　they　introduce.　These　problems　are　likely　to　continue　to　be　an　active　field　of　research　and　the　questions　to　be　answered　in　the　future.