 Research article
 Open Access
 Open Peer Review
 Published:
The same modality medical image registration with large deformation and clinical application based on adaptive diffeomorphic multiresolution demons
BMC Medical Imagingvolume 18, Article number: 21 (2018)
Abstract
Background
Diffeomorphic demons can not only guarantee smooth and reversible deformation, but also avoid unreasonable deformation. However, the number of iterations which has great influence on the registration result needs to be set manually.
Methods
This study proposed a novel method to exploit the adaptive diffeomorphic multiresolution demons algorithm to the nonrigid registration of the same modality medical images with large deformation. Firstly an optimized nonrigid registration framework and the diffeomorphism strategy were used, and then a similarity energy function based on the grey value was designed as registration metric, lastly termination condition was set based on the variation of this metric and iterations can be stopped adaptively. Quantitative analyses based on the registration evaluation indexes were conducted to prove the validity of this method.
Results
Registration result of synthetic image and the same modality MRI and CT image was compared with those obtained by other demons algorithms. Quantitative analyses demonstrated the proposed method’s superiority. Medical image with large deformation was produced by rotational distortion and extrusion transform, and the same modality image registration with large deformation was performed successfully. Quantitative analyses showed that the registration evaluation indexes remained stable with an increase in transform strength. This method can be also applied to pulmonary medical image registration with large deformation successfully, and it showed the clinical application value. The influence of different driving forces and parameters on the registration result was analysed, and the result demonstrated that the proposed method is effective and robust.
Conclusions
This method can solve the nonrigid registration problem of the same modality medical image with large deformation showing promise for diagnostic pulmonary imaging applications.
Background
Nonrigid registration has been applied to intersubjective registration to detect lesions and to establish a medical atlas. Comparisons of nonrigid registration algorithms have shown that those with demons based on the optical flow field theory provide superior results [1].
The demons algorithm was initially only applicable to image registration with small deformation. Therefore, many researchers have tried to improve it. In 2005, Wang and Pennec introduced the floating image gradient in the diffusion equation and proposed active demons [2, 3]. In 2006, Roglj et al. proposed symmetric demons method and demonstrated its high efficient [4]. In 2007, Vercauteren et al. applied the optimization framework of nonrigid registration to demons and proposed additive demons. In their method, nonrigid registration was equivalent to optimizing the similarity energy function, and iterations could be stopped adaptively [5]. In 2008, symmetric logdomain diffeomorphic demons algorithm was proposed, and Log Euclidean transformation was used to avoid timeconsuming computations of Log spatial transformation [6]. In 2009, diffeomorphic demons was proposed, and it was shown that the final deformation is topologically invariant and that unreasonable deformations are not produced [7]. In 2010, Xu et al. introduced a regularization term and designed a new similarity energy function to ensure smooth and reversible deformations [8]. In 2012, Lei et al. proposed a new active gradient and curvature (G&C) demons algorithm using the curvature to control the deformation [9].
In recent years, some researchers have proposed nonrigid image registration methods with large deformation. In 2014, Lombaert et al. introduced the direct feature matching technique to find global correspondences between reference and moving images, and they proposed spectral logdemons method for diffeomorphic image registration with large deformation [10, 11]. In 2014, Kong et al. proposed a robust discriminative clustering method based on mutual information and used it to efficiently perform brain MRI segmentation [12]. In 2015, Zhao et al. used multilayer convolutional neural networks to determine scale and translation parameters and proposed the deep adaptive logdemons method for diffeomorphic image registration with large deformation [13]. In 2015, Yan et al. performed image registration with large deformation by combining manifold learning and diffeomorphic demons [14]. In 2016, Deng et al. proposed a data classification method based on fused fuzzy deep neural network and used it to efficiently discriminate WM, GM, and CSF [15].
Medical image registration plays an important role in diagnosing diseases and establishing a medical atlas. Large deformation may exist between reference image and moving image, and it could result in problems in nonrigid medical image registration with large deformation. This study proposes an adaptive diffeomorphic multiresolution demons algorithm for medical image registration with large deformation. An optimized framework with nonrigid registration and the diffeomorphic deformation strategy were used, a similarity energy function based on the grey value was designed as a registration metric, and the termination condition was set based on the variation of this metric. Iterations could be stopped adaptively, and medical image registration with large deformation was performed. This method was tested using synthetic images and the same modality MRI and CT images, and the mean square error, normalized cross correlation, and structural similarity were used as evaluation indexes to verify the superiority of this method. Medical image registration with large deformation simulated by rotational distortion and extrusion was performed successfully. Quantitative analyses of the influences of different driving forces and parameters on the registration result showed that our method is effective and robust. This method can be applied to the same modality medical image registration with large deformation, making it promising for diagnostic pulmonary imaging applications.
Methods
For the problems of the same modality medical image registration with large deformation, an adaptive diffeomorphic multiresolution demons algorithm was proposed. An optimized framework with nonrigid registration and the diffeomorphic deformation strategy were used, a similarity energy function based on the grey value was designed, and termination conditions were set to stop iterations adaptively. We applied this method to the same modality medical image registration with large deformation and found that it shows promise for pulmonary medical image registration in clinical application.
Additive demons
Nonrigid image registration is essentially a multiparameter optimization problem involving mapping a moving image to a reference image. It defines an appropriate objective function as registration metric and then optimizes this metric. For reference image F and moving image M, the registration metric should be optimized to seek the best transform t^{opt}. t:R^{n} → R^{n}, p → t(p) is the mapping of pixel p of the moving image to pixel t(p) of the reference image. The definition of registration metric is the crucial step in the nonrigid registration process. The mean square deviation based on the grey value as a registration metric can be calculated using Eq. 1.
In Eq. 1, Ω is the common region of F and M after registration, and ο is the transform operator. Directly minimizing the registration metric using Eq. 1 will lead to an unstable solution, and therefore, a regularization term needs to be added to restrict the geometric transform. The revised energy function E(t) is expressed using Eq. 2.
In Eq. 2, σ_{i} is the local noise level, and σ_{t} is the regularization parameter. Cachier et al. [16] introduced the parameters c (not ruled spatial transform) and t (ruled spatial transform). Then, the new energy function is expressed using Eq. 3.
In Eq. 3, Dist(c, t) = ‖c − t‖,σ_{x} is the uncertainty degree between c and t. The displacement field u is produced using the space geometric transform, and two vectors are added directly to form a new vector. The energy function of additive demons algorithm comprised similarity measure term, deformation error term, and regularization term. The final energy function is expressed as follows:
In Eq. 4, u is the updated displacement field, and Dist(c, t) = ‖c − t‖ = ‖u‖. By minimizing the energy function with respect to the displacement field, the final displacement field is expressed using Eq. 5.
In Eq. 5, σ_{i}(p) = F(p) − M ∘ t(p) and J_{P} = − ∇^{T}F(p).
Diffeomorphic deformation strategy
Diffeomorphic space was proposed in 2009, and it can make sure that the deformation is smooth, reversible, and topologically invariant. Diffeomorphic transform, which is based on the theory of Lie groups, is related to the exponential map of the velocity field v, that is, ϕ = exp(v), and a practical and fast approximation method with a scalingandsquaring strategy was described as follows.
Exponential ϕ = exp(v)  
Input: Velocity field v. Output: Diffeomorphic map ϕ = exp(v) Choose N such that 2^{−N}v → 0 e.g., such that max ‖2^{−N}v‖ ≤ 0.5 pixels Scale velocity field ϕ ← 2^{−N}v for N times do ϕ ← ϕ ∘ ϕ end for 
Algorithm implementation
The diffeomorphic deformation strategy was used for optimizing the energy function designed by additive demons. The deformation should always be topologically invariant. Rough deformation greatly influences the registration result; if it is calculated unsuitably, it is easy to fall into a local optimum. Therefore, a multiresolution strategy was used, and rough deformation was calculated with low resolution. Then, the energy function was minimised and the best registration result was obtained, and it can avoid the local optimum.
Iterations also have great influence on the registration result. If the number of iterations is not set properly, the best deformation field cannot be obtained. The consumed time will increase with the increasing of iterations. The mean square deviation based on the grey value was designed as a registration metric, and iterations could be stopped adaptively depending on the variation of energy function. It can eliminate the influence of iterations on registration result. The convergence condition was defined by Eq. 6.
When the displacement field was updated, three different demons driving forces were proposed as follows: Thirion’s primitive driving force, J_{P} = − ∇^{T}F(p); Gauss–Newtonbased advanced driving force, J_{P} = − ∇^{T}(M ∘ t(p)); and symmetric driving force, J_{P} = − (∇^{T}F + ∇^{T}(M ∘ t(p)))/2.
The steps for each registration layer can be described as follows:

Step 1: initialize displacement field.

Step 2: calculate demons driving force u(p) and update velocity field v.

Step 3: regulate deformation field using Gauss filter.

Step 4: obtain exponential mapping of deformation field by diffeomorphic transform.

Step 5: calculate similarity measurement function E(t) using Eq. 4.

Step 6: judge convergence condition.
Figure 1 shows the algorithm flow of proposed method.
Algorithm evaluation
The mean square error, normalized cross correlation, and structural similarity [17] were calculated as evaluation indexes. The mean square error is defined by Eq. 7.
The normalized crosscorrelation coefficient is defined by Eq. 8.
Here, n is the total pixel number, S is the reference image, M is the registration result, and \( \overline{S} \) and \( \overline{M} \) are the average grey values of each pixel point in the reference image and registration result, respectively.
Results
The superiority of our method was verified through comparisons with active demons, additive demons, and diffeomorphic demons. The experimental parameters were set as follows: Gaussian filter parameter G_{σ}, where σ = 2; deformation updating steplength σ_{x} = 1.0; multiresolution layer number N = 3; and convergence condition stop_criterium = 0.005.
Synthetic image registration
Figure 2 shows the synthetic checkerboard images that were processed to validate our method. This method can realize nonrigid image registration and estimate the deformation field effectively.
The same modality medical image registration
MRI images were selected from the Simulated Brain Database (http://www.bic.mni.mcgill.ca/brainweb/) provided by the Montreal Neurology Institute of McGill University. Their size is 217 × 181 × 181, and T1weighted MRI images were selected as reference and moving images. Figure 3(a–c) show the reference image, moving images and initial difference. Figure 3(d–e) show that our method provides the best registration result and that the deformation field is smooth and reversible. The edge of deformation field is more reasonable than that in the case of active demons and additive demons. Table 1 shows the quantitative analysis of evaluation indexes for MRI image registration. It is seen that the normalized crosscorrelation coefficient and structural similarity are the highest and the mean square error is the lowest; therefore, our method is superior to active demons, additive demons, and diffeomorphic demons.
CT images were selected from the Medical Image Database (http://www.med.harvard.edu/AANLIB) provided by Harvard Medical Center, and their size is 256 × 256. Figure 4 shows the registration result of CT images, and Table 2 shows the quantitative analysis of evaluation indexes for CT image registration. Comparative analysis showed that the registration and statistical results of CT image were the same as those of the MRI image, indicating that our method can be also applicable to CT image registration.
Medical image registration with large deformation
Large deformation was produced by two types of free transforms: rotational distortion and extrusion. The deformation field was determined by the transform strength.
Medical image registration with large deformation produced by rotational distortion was performed using our proposed method, as shown in Fig. 5. Column 1 show moving images produced with rotational distortional strengths of 30–90%, column 2 shows the corresponding registration result, and column 3 shows the corresponding final deformation field. Table 3 shows the quantitative analysis result; the normalized crosscorrelation coefficient and structural similarity decreased and the mean square error and the consumed time gradually increased with an increase in the deformation field strength.
Figure 6 shows the result for large deformation produced by extrusion. Column 1 show moving images produced for extrusion strengths of 10–70%, column 2 shows the corresponding registration result, and column 3 shows the corresponding final deformation field. Table 4 shows the quantitative analysis result; the normalized crosscorrelation coefficient, structural similarity, and mean square error basically remained stable and the consumed time gradually increased with an increase in the deformation field strength. Therefore, medical image registration with large deformation produced by both rotational distortion and extrusion can be performed efficiently.
Clinical application
Pulmonary medical images of the same individual show large deformations under different respiration states, and therefore, nonrigid registration of such images remains challenging. Thoracic images were selected from DIRLab (http://www.DIRlab.com). DIRLab provides 12 cases, each of which contains a thoracic image with six phases. Slices with inhale and exhale phases were used as reference and moving images with large deformation. Figure 7 shows the registration result of pulmonary images. The deformation field obtained using our method was smooth and topologically invariant. Table 5 shows the quantitative analysis of the evaluation indexes for thoracic image registration with large deformation; the normalized crosscorrelation coefficient and structural similarity are the highest and the mean square error is the lowest; and the evaluation indexes obtained using our method were the closest to the standard value. The registration and statistical results indicate that our method can be clinically applied to the nonrigid registration of pulmonary images with large deformation.
Analysis of different driving forces
Thirion, GaussNewton, and symmetric driving forces were used, and the displacement field was defined differently. Figure 8 shows the variation of the energy function E(t); the convergence is the fastest and the similarity measure based on energy function is the lowest with symmetric driving forces. Table 6 shows the quantitative analysis; the symmetric driving force shows the best performance.
Influence of parameters on registration result
The Gaussian filter, deformation updating steplength, resolution layer, and convergence condition were used as experimental parameters. The resolution layer can only influence the registration speed, and the convergence condition can influence the registration accuracy. Table 7 shows details about the deformation updating steplength; the registration accuracy remained stable and the consumed time decreased with an increase in the steplength from 0.8 to 2.0.
Discussion
To verify the superiority of our method, synthetic images and the same modality MRI and CT images were used to perform nonrigid image registration with large deformation. Figures 3(e) and 4(e) show the obtained deformation fields for MRI and CT images. The edge and detail information of final deformation field obtained using our method are more accurate and reasonable than those obtained using active demons and additive demons. The diffeomorphism strategy was used to guarantee smooth and reversible deformation; by contrast, active demons and additive demons produce unreasonable deformation during registration. This conclusion agrees with that reported previously [7, 8]. Tables 1 and 2 show quantitative analysis results for the same modality MRI and CT image registration, respectively; it is seen that the mean square error is the lowest and normalized crosscorrelation and structural similarity are the highest when using the proposed method. Therefore, the proposed method is superior to active demons, additive demons, and diffeomorphic demons. Large deformation was simulated by two types of free transform, rotational distortion and extrusion, and the deformation field was determined by the transform strength. The same modality medical image registration with large deformation was performed successfully, as seen from the registration results in Figs. 5 and 6 and the quantitative analysis results in Tables 3 and 4.
The influence of the deformation updating steplength on registration result was analysed. The result shows that nonrigid registration with large deformation can be performed successfully and that the evaluation indexes remain stable when the update is within a reasonable range. The registration speed accelerates with an increase in the steplength update, and the proposed algorithm is robust. The influence of driving forces on the registration result was also analysed; the result shows that the convergence of the proposed algorithm is the fastest and that the evaluation indexes are the most perfect with symmetric driving force. The finding for the driving force is consistent with those reported previously [5, 7]. A similarity energy function based on the grey value was designed, and the termination condition was set according to the variation of the energy function. Furthermore, iterations could be stopped adaptively, and therefore, the registration speed was accelerated. By contrast, when implementing active demons and diffeomorphic demons, the number of iterations needs to be set manually, and this greatly influences the registration result. If the number of iterations is insufficient, the best registration result cannot be obtained. Diffeomorphic demons use the diffeomorphic deformation strategy to guarantee smooth and reversible deformation; however, they are timeconsuming. By contrast, additive demons consume less time; however, they cannot guarantee smooth and topologically invariant deformation, and they are prone to producing unreasonable deformations. The proposed algorithm can stop iterations automatically, thus greatly accelerating the registration speed and ensuring a smooth and topologically invariant deformation field.
Pulmonary medical images of the same individual under different respiration states show large deformation, and registration of such images is crucial for planning the treatment of thoracic malignancies and quantitatively analysing pulmonary function. Quantitative analyses showed that pulmonary medical images with large deformation were successfully and efficiently registered using our proposed method, and the deformation was smooth and topologically invariant. Therefore, our proposed method is promising for applications in diagnostic pulmonary imaging and radiation oncology.
Conclusion
This study proposed an adaptive diffeomorphic multiresolution demons algorithm and solved the problem of the same modality medical image nonrigid registration with large deformation. Synthetic images and the same modality MRI and CT image registration were tested by this method, and quantitative analyses demonstrated the proposed method’s superiority. Medical images with large deformation were produced by rotational distortion and extrusion transform, registration result and quantitative analyses demonstrated that image registration with large deformation could be performed successfully and efficiently using our method. Quantitative analyses under different driving forces demonstrated that our method based on symmetric driving force is the most efficient. The influence of parameters on registration result was analysed indicating that our method is robust. This method can be also applied to pulmonary medical image registration with large deformation, and have an important clinical application value.
Abbreviations
 CSF:

Cerebrospinal fluid
 CT:

Computed tomography
 GM:

Grey matter
 MRI:

Magnetic resonance imaging
 MSE:

Mean square error
 NCC:

Normalized cross correlation
 WM:

White matter
References
 1.
Hellier P, Barillot C, Corouge I. Retrospective evaluation of intersubject brain registration. IEEE Trans on Medical Imaging. 2003;22:1120–30.
 2.
Wang H, Dong L, O’Daniel J. Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Phys Med Biol. 2005;50:2887–905.
 3.
Pennec X, Cachier P. Understanding the demons algorithm: 3D nonrigid registration by gradient descent. Med Image Comput Comput Assist Interv. 1999:597–605.
 4.
Rogelj P, Kovacic S. Symmetric image registration. Med Image Anal. 2006;10:484–93.
 5.
Vercauteren T, Pennec X, Perchant A. Nonparametric diffeomorphic image registration with the demons algorithm. Med Image Comput Comput Assist Interv. 2007:319–26.
 6.
Vercauteren T, Pennec X, Perchant A, et al. Symmetric logdomain diffeomorphic registration: a demonsbased approach. Med Image Comput Comput Assist Interv. 2008:754–61.
 7.
Vercauteren T, Pennec X, Perchant A. Diffeomorphic demons: efficient nonparametric image registration. NeuroImage. 2009;45:61–72.
 8.
Xu F, Liu W, Li CF. A nonrigid registration of the cerebral CT images with large deformations. Chin J Biomed Eng. 2010;29:172–7.
 9.
Lei WJ, Zhu X, Jia YT, et al. Image registration method by combination of gradient and curvature driven. Comput Eng Appl. 2012;48:204–6.
 10.
Lombaert H, Grady L, Pennec X, et al. Spectral logdemons: diffeomorphic image registration with very large deformations. Int J Comput Vis. 2014;107:254–71.
 11.
Lombaert H, Grady L, Pennec X, et al. Spectral demons image registration via global spectral correspondence. Eur Conference Comput Vision. 2012:30–44.
 12.
Kong YY, Deng Y, Dai QH. Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Processing Letters. 2014;22:573–7.
 13.
Zhao L, Jia K. Deep adaptive logdemons: diffeomorphic image registration with very large deformations. Comput Math Methods Med. 2015;15:1–16.
 14.
Yan DQ, Liu CF, Liu SL, et al. A fast image registration algorithm for diffeomorphic image with large deformation. Acta Automat Sin. 2015;41:1461–70.
 15.
Deng Y, Ren ZQ, Kong YY, et al. A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst. 2017;25:1006–12.
 16.
Cachier P, Bardinet E, Dormont D, et al. Iconic feature basednonrigid registration: the PASHA algorithm. Comput Vis Image Underst. 2003;89:272–98.
 17.
Wang Z, Bovik AC, Sheikh HR. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–12.
Acknowledgements
The authors are thankful for the supports from the School of biomedical engineering and Key Lab of Neurosense and Control (Xinxiang Medical University, Xinxiang, China).
Funding
This work is supported by the Fundamental Research Funds for Key Scientific Research Project of Henan Higher Education Institutions under grant no. 17A310005; Scientific and Technological Project of Henan Province under grant nos. 182102310528, 182102310555, and 152102310357; Natural Science Foundation of Xinxiang City under grant no. CXGG17005; Open Project of Biomedical Engineering School under grant no. 2018BMEKFKT06; and Disciplinary Group of Psychology and Neuroscience under grant no. 2016PNKFKT19.
The funding sponsors have estimated the feasibility of the study, but have no role in the collection, analysis, or interpretation of the data or in the decision to submit the manuscript for publication.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the following repositories:
Author information
Affiliations
Contributions
CW proposed adaptive diffeomorphic multiresolution demons, designed evaluation indexes for registration results, and performed medical image registration; QQR and XQ prepared the medial images with large deformation, and performed the same modality medical image registration with large deformation; YY performed the analysis and interpretation of the registration results. All authors have been involved in drafting and revising the manuscript and approved the final version to be published. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Chang Wang.
Ethics declarations
Ethics approval and consent to participate
This work was approved by the Institution Research Ethics Board of Xinxiang Medical University. Participant for all images have informed consent that he knew the risks and agreed to participate in the research.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Received
Accepted
Published
DOI
Keywords
 Diffeomorphic
 Demons
 Adaptive
 Large deformation
 Multiresolution