Abstract
BackgroundTo 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.MethodsWe 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.ResultsThe 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.ConclusionWith 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.
Highlights
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
To improve the global searching ability and reduce the registration time, we introduce Metropolis acceptance criteria [16] to an stochastic gradient descent (SGD) optimizer, which increases both the random disturbance and the probability of escaping the local extremum
To increase the robustness of improved stochastic gradient descent (ISGD) with respect to the parameters used and to prevent falling into the local extremum, we introduce Metropolis acceptance criteria into the stochastic process as follows: If the current exact value is smaller than the previous value, the step size will be accepted; otherwise, the Boltzmann probability factor computed by Eq (24) will be compared with a small random number distributed in the interval (0, 1)
Summary
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. As the fifth leading cause of cancer death worldwide, prostate cancer affected approximately 1.1 million men worldwide in 2012 [1]. Transrectal ultrasound-guided (TRUS) prostate biopsy is the most common means of diagnosing prostate cancer when an individual exhibits high blood levels of prostate-specific antigen (PSA). TRUS has many advantages, including real-time detection, low cost, and easy operation, its poor image quality and lack of clear contrast between malignant and normal tissue lead to false-negative rates of up to 30% for systematic sextant biopsies [3]. The registration of pre-operative MR images onto inter-operative TRUS images is of important clinical significance for improving biopsy accuracy
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