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Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information.
Gong, Lun; Wang, Haifeng; Peng, Chengtao; Dai, Yakang; Ding, Min; Sun, Yinghao; Yang, Xiaodong; Zheng, Jian.
Afiliação
  • Gong L; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Wang H; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Peng C; Department of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China.
  • Dai Y; Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, 230061, China.
  • Ding M; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
  • Sun Y; School of Science, Nanjing University of Science and Technology, Nanjing, 210094, China.
  • Yang X; Department of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China.
  • Zheng J; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Biomed Eng Online ; 16(1): 8, 2017 Jan 10.
Article em En | MEDLINE | ID: mdl-28086888
ABSTRACT

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Próstata / Reto / Biópsia / Processamento de Imagem Assistida por Computador / Imageamento por Ressonância Magnética / Cirurgia Assistida por Computador Tipo de estudo: Diagnostic_studies Limite: Humans / Male Idioma: En Revista: Biomed Eng Online Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Próstata / Reto / Biópsia / Processamento de Imagem Assistida por Computador / Imageamento por Ressonância Magnética / Cirurgia Assistida por Computador Tipo de estudo: Diagnostic_studies Limite: Humans / Male Idioma: En Revista: Biomed Eng Online Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2017 Tipo de documento: Article País de afiliação: China
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