Deformable multi-modal registration using 3D-FAST conditioned mutual information

Abstract

Purpose: Mutual information (MI) has been a preferred choice of similarity measure for multi-modal image registration, but the accuracy and robustness of MI are not satisfactory as MI only considers the global intensity correlation while ignoring local and structural information. To address this problem, we combine MI with local and structural information.

Method: We bring structural information extracted by a modified Accelerated Segment Test (FAST) algorithm into MI. Traditional FAST is transferred into 3 D for the first time, and the 3 D-FAST based structural information is added into MI as another channel, thereby incorporating spatial and geometric information with intensity information in the registration.

Result: The robustness and accuracy of the proposed method were demonstrated in three experiments. The average registration errors of our method were 1.17, 1.33 and 1.20 compared to 1.47, 1.63 and 1.40 of LMI in T1-T2, T1-PD and T2-PD registration respectively.

Discussion: In this paper, we use the structural similarity computed by 3 D-FAST as the conditional information to encode spatial and geometric cues into LMI. In all of these three experiments, our method shows to be more robust and accurate than common registration methods based on information theory.

Keywords:

• Non-rigid
• Multi-modal
• Mutual information
• FAST
• MR
• clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study has been supported by: the Nation Natural Science Foundation of China (projects 60972102), the National Science and Technology Support Program (No. 2015BAK31B01), the National High Technology Research and Development Program (2015AA020507). This study is partly sponsored by Program of Shanghai Academic/Technology Research Leader project 16XD1424900 and project 15441905500, 17zr1401500 of Science and Technology Commission of Shanghai Municipality.