TY - GEN
T1 - Diffusing Coupling High-Frequency-Purifying Structure Feature Extraction for Brain Multimodal Registration
AU - Gao, Fan
AU - He, Yuanbo
AU - Li, Shuai
AU - Hao, Aimin
AU - Cao, Desen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The core of medical image registration is the alignment of corresponding structures. However, in multimodal image registration, substantial differences in appearance (intensity distribution) of the images often compel the registration model to prioritize intensity information over structure information, resulting in low accuracy of registration. Therefore, the disentangling structure information from intensity information is vital to improve the registration effectiveness. To this end, we propose a diffusing coupling high-frequency-purifying structure feature extraction for brain multimodal registration. Specifically, the denoising diffusion probabilistic models (DDPM) is firstly utilized to extract complete feature information from images. Then, the discrete cosine transform (DCT) is applied to purify high-frequency structure information from the complete feature information for registration. Furthermore, structure consistency constraint (SCC) is introduced based on purified structure information to emphasize the core position of the structure in registration. Through comprehensive comparisons with traditional and learning-based methods on the multimodal brain MRI dataset, our method demonstrates superior accuracy and stability in brain multimodal registration. Our code is available at https://github.com/goghfan/DDNet.
AB - The core of medical image registration is the alignment of corresponding structures. However, in multimodal image registration, substantial differences in appearance (intensity distribution) of the images often compel the registration model to prioritize intensity information over structure information, resulting in low accuracy of registration. Therefore, the disentangling structure information from intensity information is vital to improve the registration effectiveness. To this end, we propose a diffusing coupling high-frequency-purifying structure feature extraction for brain multimodal registration. Specifically, the denoising diffusion probabilistic models (DDPM) is firstly utilized to extract complete feature information from images. Then, the discrete cosine transform (DCT) is applied to purify high-frequency structure information from the complete feature information for registration. Furthermore, structure consistency constraint (SCC) is introduced based on purified structure information to emphasize the core position of the structure in registration. Through comprehensive comparisons with traditional and learning-based methods on the multimodal brain MRI dataset, our method demonstrates superior accuracy and stability in brain multimodal registration. Our code is available at https://github.com/goghfan/DDNet.
KW - denoising diffusion probabilistic models
KW - high frequency purifying
KW - multimodal registration
KW - structure consistency constraint
UR - https://www.scopus.com/pages/publications/85184899802
U2 - 10.1109/BIBM58861.2023.10385725
DO - 10.1109/BIBM58861.2023.10385725
M3 - 会议稿件
AN - SCOPUS:85184899802
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 508
EP - 515
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -