Open Access3D MRI image super‐resolution for brain combining rigid and large diffeomorphic registration
Author(s) -
Liang Zifei,
He Xiaohai,
Teng Qizhi,
Wu Dan,
Qing Lingbo
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0517
Subject(s) - diffeomorphism , computer science , image registration , computer vision , artificial intelligence , robustness (evolution) , segmentation , rigid body , algorithm , mathematics , image (mathematics) , physics , mathematical analysis , biochemistry , chemistry , classical mechanics , gene
Most of the recent leading multiple magnetic resonance imaging (MRI) super‐resolution techniques for brain are limited to rigid motion. In this study, the authors aim to develop a super‐resolution technique with diffeomorphism mainly for longitudinal brain MRI data. For the images from different time slots, unpredicted deformation may occur. In previous studies, sole rigid registration or traditional non‐rigid registration has been frequently used to achieve multi‐plane super‐resolution. However, non‐rigid motion of two brains from different time slots is difficult to model, since brain contains a wealth of complex structure such as the cerebral cortex. In order to address such problem, rigid and large diffeomorphic registration has been embedded into their super‐resolution framework. In addition, many previous researchers use L 2 norm to achieve super‐resolution framework. In this work, L 1 norm minimisation and regularisation based on a bilateral prior are adopted. These operations ensure its robustness to the assumed model of data and noise. Their approach is evaluated using Alzheimer datasets from seven different resolutions. Results show that their reconstructions have advantages over rigid and conventional non‐rigid registration‐based super‐resolution, in terms of the root‐mean‐square error and structure similarity. Furthermore, their reconstruction results improve the precision of brain automatic segmentation.