Journal
INFORMATION FUSION
Volume 64, Issue -, Pages 149-187Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2020.07.006
Keywords
Multimodal data fusion; Neuroimaging; Magnetic resonance imaging; PET; SPECT; Fusion rules; Assessment; Applications; Partial volume effect
Funding
- Royal Society International Exchanges Cost Share Award, UK [RP202G0230]
- Medical Research Council Confidence in Concept Award, UK [MC_PC_17171]
- Hope Foundation for Cancer Research, UK [RM60G0680]
- British Heart Foundation Accelerator Award, UK
- MINECO/FEDER [RTI2018-098913-B100, A-TIC-080-UGR18]
- FPU predoctoral grant from Ministerio de Universidades, Spain [FPU 18/04902]
- Fundamental Research Funds for the Central Universities [CDLS-2020-03]
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education
- Guangxi Key Laboratory of Trusted Software [kx201901]
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Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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