TY - GEN
T1 - Diffeomorphic Smoothing for Retinotopic Mapping
AU - Tu, Yanshuai
AU - Tal, Duyan
AU - Lu, Zhong Lin
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Retinotopic mapping, the mapping of visual input on the retina to cortical neurons, is an important topic in vision science. Typically, cortical neurons are related to visual input on the retina using functional magnetic resonance imaging (fMRI) of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology studies that retinotopic mapping is locally diffeomorphic (i.e., smooth, differentiable, and invertible) within each local area, the retinotopic maps from fMRI are often not diffeomorphic, especially near the fovea, because of the low signal-noise ratio of fMRI. The aim of this study is to develop and solve a mathematical model that produces diffeomorphic retinotopic mapping from fMRI data. Specifically, we adopt a geometry concept, the Beltrami coefficient, as the tool to define diffeomorphism, and model the problem in an optimization framework. Efficient numerical methods are proposed to solve the model. Experimental results with both synthetic and real retinotopy data demonstrate that the proposed method is superior to conventional smoothing methods.
AB - Retinotopic mapping, the mapping of visual input on the retina to cortical neurons, is an important topic in vision science. Typically, cortical neurons are related to visual input on the retina using functional magnetic resonance imaging (fMRI) of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology studies that retinotopic mapping is locally diffeomorphic (i.e., smooth, differentiable, and invertible) within each local area, the retinotopic maps from fMRI are often not diffeomorphic, especially near the fovea, because of the low signal-noise ratio of fMRI. The aim of this study is to develop and solve a mathematical model that produces diffeomorphic retinotopic mapping from fMRI data. Specifically, we adopt a geometry concept, the Beltrami coefficient, as the tool to define diffeomorphism, and model the problem in an optimization framework. Efficient numerical methods are proposed to solve the model. Experimental results with both synthetic and real retinotopy data demonstrate that the proposed method is superior to conventional smoothing methods.
KW - Beltrami Coefficient
KW - Diffeomorphic Smoothing
KW - Retinotopic Mapping
UR - http://www.scopus.com/inward/record.url?scp=85085861650&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085861650&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098316
DO - 10.1109/ISBI45749.2020.9098316
M3 - Conference contribution
AN - SCOPUS:85085861650
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 534
EP - 538
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Y2 - 3 April 2020 through 7 April 2020
ER -