TY - GEN
T1 - Diffeomorphic Registration for Retinotopic Mapping Via Quasiconformal Mapping
AU - Tu, Yanshuai
AU - Ta, Duyan
AU - David Gu, Xianfeng
AU - Lu, Zhong Lin
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Human visual cortex is organized into several functional re-gions/areas. Identifying these visual areas of the human brain (i.e., V1, V2, V4, etc) is an important topic in neurophysiology and vision science. Retinotopic mapping via functional magnetic resonance imaging (fMRI) provides a noninvasive way of defining the boundaries of the visual areas. It is well known from neurophysiology studies that retino-topic mapping is diffeomorphic within each local area (i.e. locally smooth, differentiable, and invertible). However, due to the low signal-noise ratio of fMRI, the retinotopic maps from fMRI are often not diffeomorphic, making it difficult to delineate the boundaries of visual areas. The purpose of this work is to generate diffeomorphic retinotopic maps and improve the accuracy of the retinotopic atlas from fMRI measurements through the development of a specifically designed registration procedure. Although there are sophisticated existing cortical surface registration methods, most of them cannot fully utilize the features of retinotopic mapping. By considering unique retinotopic mapping features, we form a qua-siconformal geometry-based registration model and solve it with efficient numerical methods. We compare our registration with several popular methods on synthetic data. The results demonstrate that the proposed registration is superior to conventional methods for the registration of retinotopic maps. The application of our method to a real retinotopic mapping dataset also results in much smaller registration errors.
AB - Human visual cortex is organized into several functional re-gions/areas. Identifying these visual areas of the human brain (i.e., V1, V2, V4, etc) is an important topic in neurophysiology and vision science. Retinotopic mapping via functional magnetic resonance imaging (fMRI) provides a noninvasive way of defining the boundaries of the visual areas. It is well known from neurophysiology studies that retino-topic mapping is diffeomorphic within each local area (i.e. locally smooth, differentiable, and invertible). However, due to the low signal-noise ratio of fMRI, the retinotopic maps from fMRI are often not diffeomorphic, making it difficult to delineate the boundaries of visual areas. The purpose of this work is to generate diffeomorphic retinotopic maps and improve the accuracy of the retinotopic atlas from fMRI measurements through the development of a specifically designed registration procedure. Although there are sophisticated existing cortical surface registration methods, most of them cannot fully utilize the features of retinotopic mapping. By considering unique retinotopic mapping features, we form a qua-siconformal geometry-based registration model and solve it with efficient numerical methods. We compare our registration with several popular methods on synthetic data. The results demonstrate that the proposed registration is superior to conventional methods for the registration of retinotopic maps. The application of our method to a real retinotopic mapping dataset also results in much smaller registration errors.
KW - Beltrami Coefficient
KW - Diffeomorphic Registration
KW - Retinotopic Mapping
UR - http://www.scopus.com/inward/record.url?scp=85085862860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085862860&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098386
DO - 10.1109/ISBI45749.2020.9098386
M3 - Conference contribution
AN - SCOPUS:85085862860
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 687
EP - 691
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 -