  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,
\textit{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,
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.
