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Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information.

Authors :
Lun Gong
Haifeng Wang
Chengtao Peng
Yakang Dai
Min Ding
Yinghao Sun
Xiaodong Yang
Jian Zheng
Gong, Lun
Wang, Haifeng
Peng, Chengtao
Dai, Yakang
Ding, Min
Sun, Yinghao
Yang, Xiaodong
Zheng, Jian
Source :
BioMedical Engineering OnLine. 1/10/2017, Vol. 16, p1-21. 21p.
Publication Year :
2017

Abstract

<bold>Background: </bold>To improve the accuracy of ultrasound-guided biopsy of the prostate, the non-rigid registration of magnetic resonance (MR) images onto transrectal ultrasound (TRUS) images has gained increasing attention. Mutual information (MI) is a widely used similarity criterion in MR-TRUS image registration. However, the use of MI has been challenged because of intensity distortion, noise and down-sampling. Hence, we need to improve the MI measure to get better registration effect. <bold>Methods: </bold>We present a novel two-dimensional non-rigid MR-TRUS registration algorithm that uses correlation ratio-based mutual information (CRMI) as the similarity criterion. CRMI includes a functional mapping of intensity values on the basis of a generalized version of intensity class correspondence. We also analytically acquire the derivative of CRMI with respect to deformation parameters. Furthermore, we propose an improved stochastic gradient descent (ISGD) optimization method based on the Metropolis acceptance criteria to improve the global optimization ability and decrease the registration time. <bold>Results: </bold>The performance of the proposed method is tested on synthetic images and 12 pairs of clinical prostate TRUS and MR images. By comparing label map registration frame (LMRF) and conditional mutual information (CMI), the proposed algorithm has a significant improvement in the average values of Hausdorff distance and target registration error. Although the average Dice Similarity coefficient is not significantly better than CMI, it still has a crucial increase over LMRF. The average computation time consumed by the proposed method is similar to LMRF, which is 16 times less than CMI. <bold>Conclusion: </bold>With more accurate matching performance and lower sensitivity to noise and down-sampling, the proposed algorithm of minimizing CRMI by ISGD is more robust and has the potential for use in aligning TRUS and MR images for needle biopsy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1475925X
Volume :
16
Database :
Academic Search Index
Journal :
BioMedical Engineering OnLine
Publication Type :
Academic Journal
Accession number :
120750290
Full Text :
https://doi.org/10.1186/s12938-016-0308-5