TY - JOUR
T1 - Advances in multimodal data fusion in neuroimaging
T2 - Overview, challenges, and novel orientation
AU - Zhang, Yu Dong
AU - Dong, Zhengchao
AU - Wang, Shui Hua
AU - Yu, Xiang
AU - Yao, Xujing
AU - Zhou, Qinghua
AU - Hu, Hua
AU - Li, Min
AU - Jiménez-Mesa, Carmen
AU - Ramirez, Javier
AU - Martinez, Francisco J.
AU - Gorriz, Juan Manuel
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Applications
KW - Assessment
KW - Fusion rules
KW - Magnetic resonance imaging
KW - Multimodal data fusion
KW - Neuroimaging
KW - PET
KW - Partial volume effect
KW - SPECT
UR - http://www.scopus.com/inward/record.url?scp=85088387712&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2020.07.006
DO - 10.1016/j.inffus.2020.07.006
M3 - Article
AN - SCOPUS:85088387712
SN - 1566-2535
VL - 64
SP - 149
EP - 187
JO - Information Fusion
JF - Information Fusion
ER -