Evaluations of diffusion tensor image registration based on fiber tractography
 Yi Wang^{1},
 Yu Shen^{1},
 Dongyang Liu^{1},
 Guoqin Li^{1},
 Zhe Guo^{1},
 Yangyu Fan^{1} and
 Yilong Niu^{2}Email author
DOI: 10.1186/s1293801602992
© The Author(s) 2017
Received: 11 October 2016
Accepted: 12 December 2016
Published: 10 January 2017
Abstract
Background
Diffusion Tensor Magnetic Resonance Imaging (DTMRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development.
Methods and results
In this paper, six wellknown open source DTI registration algorithms: Elastic, Rigid, Affine, DTITK, FSL and SyN were applied on 11 subjects from an openaccess dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twentyseven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template.
Conclusion
All experimental results indicated that DTITK performed the best under the study conditions, and SyN ranked just behind it.
Keywords
DTI Registration algorithms Evaluation TractographyBackground
Diffusion Tensor Magnetic Resonance Imaging (DTMRI, also known as DTI) [1] is a magnetic resonance imaging technique. DTI measures the diffusion properties of water molecules in tissue and creates images showing physiological information such as neural bundles, which cannot be obtained by other imaging methods. DTI can be used to infer some microscopic features and organizational information regarding the structural anatomy of tissues, especially the orientation of fibrous tissues, which has been used extensively to study white matter fiber tracts [2–4]. DTI plays an important role in the in vivo study of anatomical structures and functional connectivity throughout the brain.
Many DTI registration algorithms have been proposed. With respect to data processing, registration methods can be divided into three categories: scalar imagebased registration algorithms, tensor imagebased registration algorithms, and fiber bundlebased registration algorithms. Scalar imagebased registration algorithms use scalar images derived from DTI images, which are mainly fractional anisotropy (FA) images, to perform registrations [2]. Voxels in tensor images are displaced according to the displacement field obtained with scalar registration and then tensor reorientation is performed. Different from scalar imagebased registration algorithms, tensor imagebased registration algorithms use the tensor instead of the scalar to perform registrations [5]. Meanwhile, fiber bundlebased registrations use fiber bundles tracked from the DTI images to perform registration directly [6, 7], but it spends more time on fiber tractography from DTI images according to regions of interest (ROIs).
Currently, there is no standard method for evaluating the performance of DTI registrations. As such, it is necessary to develop evaluation strategies on the topic. However, development of DTI registration evaluation strategies is challenging because each DTI registration algorithm has advantages and disadvantages for different ROIs, and a single evaluation strategy cannot be broadly applied to all algorithms.
Previous studies have utilized evaluation criteria based on regional matching. In 2000, Basser et al. [3] proposed the use of two diffusion tensor eigenvalueseigenvectors overlapping rates (Overlap of Eigenvalue–eigenvectors Pairs). In 2002, Jones et al. [4] proposed the use of a tensornormalized standard deviation (Normalized Standard Deviation of Tensors) and Dyadic Coherence to assess matching performance. Both evaluation criteria take advantage of the direction of the diffusion anisotropy value and principal eigenvector. In 2006, Zhang et al. [5] used the tensor Euclidean distance (Euclidean Distance) and the tensor deviation Euclidean distance (Euclidean Distance of the Deviatoric Tensor) to evaluate the spatial normalization accuracy. In 2007, Van Hecke et al. [8] proposed using the angles of diffusion tensor eigenvalueseigenvectors as evaluation criteria. However, the most direct way to evaluate the performance of registration algorithms is with a similarity metric of tensor. In 2007, Klein [9, 10] proposed the use of voxels and surface overlaying rate (Volume and Surface Overlap), and registration accuracy was assessed by computing the overlap of segmented edges. Precision and convergence properties were studied by comparing deformation fields. In 2011, Wang et al. [2] proposed a partial area matching quality criterion (Regional Matching Quality Criterion). In 2012, Adluru et al. [11] used the Euclidean distance, Euclidean norm, crosscorrelation, and eigenvalueeigenvector pair of overlapping rate assessment criteria. In 2013, de Groot et al. [12] used the spatial similarity metric as the assessment criteria.
Currently, other scholars are studying evaluation criteria based on fiber bundles. However, this technique requires that the fiber information be extracted prior to evaluation. Tract extraction techniques are mostly semiautomatic, although small or thin fiber tracts are difficult to track and extract, so application of this technique is relatively limited. In 2006, Zhang et al. [5] calculated the average distance of points in two corresponding tracts as an evaluation parameter. In 2007, Mayer et al. [6] calculated the mean squared error (MSE) between model and target fibers before and after image registration to validate their registration algorithm. In 2010, Shadmi et al. [7] calculated the MSE and the residual MSE (RMSE) between the warped model and the target fiber sets to assess their registration algorithm. In 2011, Wang et al. [2] proposed a fiber property profile approach to perform evaluation. In 2013, de Groot et al. [12] proposed the fiberbased spatial similarity metric to assess the registration algorithms.
However, there are some problems with the existing evaluation techniques. In 2009, Klein et al. [13] evaluated performances of registrations for anatomic regions and the whole voxels of brain using the overlap rates on voxels and surfaces, the similarity of voxels and measuring distances. They evaluated 14 registration algorithms, but compared the scalar imagebased registration algorithms without tensorbased registration algorithms. In 2011, Wang et al. [2] evaluated eight registration algorithms, including registration algorithms based on scalar images and tensor images. However, the Wang et al. study only used two evaluation criteria on infantile data which had lower FA value and signaltonoise ratio compared to adult datasets. Since results differ between registration of infantile and adult images using the same technique, adult data was selected for this study and is easily accessed in several open sources. In 2013, de Groot et al. [12] proposed use of the spatial similarity metric based on the fibers accessed through the registration algorithms, however only two algorithms were compared.
The performance metrics based on similarity of tractography are independent of any particular similarity matrix derived from scalar or higher order images, and are adopted in most registration approaches. It should also be noted that optimal white matter tract alignment is most closely linked to the eventual registration goal of obtaining anatomical correspondence in white matter [8]. In this study, the data from healthy individuals was used to evaluate the DTI registration algorithm based on white matter fiber tracts.
Six wellknown open source DTI registration algorithms (Elastic, Rigid, Affine, DTITK, FSL and SyN) were investigated. The performance of each registration algorithm was evaluated by computing the distances and intersection angles between fiber tracts, as well as with the FA profiles along the fiber tracts using deterministic streamline tractography. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from registered subjects and the template were calculated to assess the registration algorithm. The performance of each registration algorithm was also evaluated by computing the spatial correlation similarity of the fibers between the subjects as well as between each subject and the template using probabilistic tractography.
Methods
Materials
Diffusion MRI Data: The openaccess IXI dataset from the Hammersmith Hospital of London was used (http://www.braindevelopment.org). A 3 Tesla Philips MRI scanner was used to scan the healthy subjects. The spatial resolution of the images was 1.7409 × 1.7355 × 1.9806 mm, resulting in volume data for the head of 128 × 128 × 64 voxels. Diffusionweighted images were acquired along 15 unique gradient directions with b = 1000 s/mm^{2} (repetition time = 11,894.44 ms; echo time = 51 ms). Additional imaging parameters can be found at the image library website.
Subject and template
In this paper, 10 subjects were chosen at random from the dataset (mean age = 51.549 years, min age = 30.89 years, max age = 74.01 years, including: 5 males, mean age = 51.586 years, min age = 30.89 years, max age = 63.68 years; and 5 females, mean age = 51.512 years, min age = 33.76 years, max = 74.01 years). For the template, although DTITK (http://www.nitrc.org/projects/dtitk/) could produce a good template with sufficient DTI information to perform tractography, using DTITK would bias the analysis since it is compared here. So another subject with quality inspection was chosen from the same dataset at random to serve as the template (male, age = 37.83 years).
Preprocessing
The Brain Extraction Tool (BET) within the FMRIB software Library (FSL) was used to extract brain tissue for each subject and template. The mask used for skull stripping was generated from each subject or template individually and checked manually. Before tensor estimation, diffusionweighted images (DWIs) from 15 diffusion gradient directions were eddycurrent corrected with eddy tool in FSL, which is a tool to correct eddy currentinduced distortions and subject movements in diffusion data [14].
Registration methods
In accordance with the work of Wang et al. [2], we chose six relatively mature open source registration algorithms to evaluate. All of the subjects were normalized at first. The six DTI registration algorithms investigated in this paper are described in detail below.
In 2000, Alexander et al. [15] applied Elastic Registration Algorithm (referred to as Elastic in this paper) to diffusion tensor image. It can be performed with Advanced Normalization Tools (ANTs) (http://www.nitrc.org/projects/ants). In 1999, Studholme et al. [16] proposed Rigid body registration algorithm (referred to as Rigid in this paper). It also can be performed with ANTs, and it is one of the simplest algorithms of image registration.
In 2005, Leemans et al. [17] rendered an algorithm based on multichannel affine registration, and the mutual information was used for similarity criteria (referred to as Affine in this paper). It is often performed before most deformation registrations and available through ANTs.
In 2006, Zhang et al. [5] developed a diffeomorphic deformable tensor registration technique (termed DTITK) (http://www.nitrc.org/projects/dtitk/). It is the only open source and nonlinear tensorbased registration algorithm (referred to as DTITK in this paper).
In 2008, Andersson et al. [18] developed a Bspline registration algorithm based on the sumofsquared differences performed by FSL (http://www.nitrc.org/projects/fsl) (referred to as FSL in this paper).
In 2008, Avants et al. [19] developed a symmetric image normalization method based on mutual correlation (referred to as SyN in this paper) again with ANTs.
Evaluation methods
In this paper, deterministic streamline tractography [20, 21, 22] and probabilistic tractography [12, 23, 24, 25] were used to track fibers separately. Deterministic streamline tractography is used to evaluate the DTI registration based on the distances and intersection angles between fiber tracts as well as the fiber property profiles, MSE, and RMSE. Probabilistic tractography is used to evaluate DTI registration based on the spatial similarity metric.
ROI  Left/right 

Acoustic radiation (Ar)  + 
Anterior thalamic radiation (Atr)  + 
Superior thalamic radiation (Str)  + 
Posterior thalamic radiation (Ptr)  + 
Superior longitudinal fasciculus (Slf)  + 
Inferior longitudinal fasciculus (Ilf)  + 
Inferior frontooccipital fasciculus (Ifo)  + 
Uncinate fasciculus (Unc)  + 
Cingulate gyrus part of cingulum (Cgc)  + 
Parahippocampal part of cingulum (Cgh)  + 
Forceps minor (Fmi)  − 
Forceps major (Fma)  − 
Middle cerebellar peduncle (Mcp)  − 
Medial lemniscus (Ml)  + 
Corticospinal tract (Cst)  + 
Evaluation method based on distance between fiber tracts
In most cases, the objective function of registration is the registration for anatomical structures. So performances of registration assessment also should be the measurement of anatomical structures.
Evaluation method based on the MSE and RMSE of fibers
Evaluation results of registration based on distances between fibers
Genu  Splenium  LATR  RATR  LCST  RCST  LIFO  RIFO  Mean  

Elastic  0.5202  0.5509  0.4892  0.5264  0.4953  0.5005  0.4697  0.4533  0.5007 
Rigid  0.5275  0.5097  0.4941  0.5693  0.5037  0.4960  0.4822  0.4704  0.5066 
Affine  0.5346  0.5244  0.4984  0.5392  0.5000  0.4912  0.4664  0.4773  0.5039 
DTITK  0.4876  0.4792  0.4872  0.4783  0.4736  0.4904  0.4549  0.4098  0.4701 
FSL  0.4971  0.4860  0.4914  0.5023  0.4920  0.4656  0.4427  0.4053  0.4728 
SyN  0.4835  0.4958  0.4788  0.5126  0.4748  0.4716  0.4548  0.4153  0.4734 
Evaluation results of registrations based on MSE of fibers
Genu  Splenium  LATR  RATR  LCST  RCST  LIFO  RIFO  Mean  

Elastic  0.0460  0.0505  0.0073  0.0099  0.0188  0.0307  0.0138  0.0088  0.0232 
Rigid  0.0362  0.0844  0.0231  0.0174  0.0492  0.0659  0.0193  0.0161  0.0390 
Affine  0.0805  0.0816  0.0119  0.0154  0.0264  0.0432  0.0230  0.0122  0.0368 
DTITK  0.0033  0.0097  0.0033  0.0034  0.0056  0.0069  0.0054  0.0036  0.0052 
FSL  0.0460  0.0517  0.0096  0.0127  0.0191  0.0285  0.0117  0.0073  0.0233 
SyN  0.0046  0.0140  0.0044  0.0049  0.0061  0.0080  0.0059  0.0058  0.0067 
Evaluation method based on the FA profiles along the fiber tracts
In 2011, Wang et al. [2] proposed a fiber property profilebased metric using normative correlation. Along each fiber bundle, FA profiles were calculated. For each registered subject, each fiber was recaptured with the same location as the fiber of the template. With the defined fiber bundles, FA curves of each fiber bundle were redefined, and then the corresponding mean FA curves were derived from the fiber bundles of the same ROI for all subjects.
Evaluation method based on intersection angles between fiber bundles
Here, \(F_{i}\) and \(G_{j}\) are fibers of the template and one subject respectively, F and G are two fiber bundles, and the value of \(\cos \alpha\) is between 0 and 1. The higher the value of \(\cos \alpha\) is, the better the performance is. For each ROI, the final result represents an average value of \(\cos \alpha\) across the fibers between all subjects and template.
Evaluation method based on spatial similarity between fiber tracts
Equation (5) provides a measure of the voxelwise similarity of the tracts density images (J and K) for two subjects. It computes over all voxels (i), and is bound on a 0–1 scale. A similarity matrix is calculated on the tract density images. A higher spatial correlation similarity indicates a better registration.
Results
Evaluation method based on distance between fiber tracts
Table 2 shows the average fiber distances between each subject and template pair of fibers where Genu, Splenium, LATR (left ATR), RATR (right ATR), LCST (left CST), RCST (right CST), LIFO (left IFO), RIFO (right IFO) are the eight fibers tracked by streamline fiber tracking algorithm of Deterministic Fiber Tractography for the template and subjects. “Mean” is the average value of each of the eight ROIs across all registration algorithms. For each ROI, the final result is the average distance of fibers between all subjects and template.
The average distances of each registration algorithm are presented in Table 2 DTITK had the lowest value and the SyN algorithm had the second lowest value. These results indicate that the DTITK registration algorithm outperforms all other tested registration methods, and the SyN presented as the next most effective method. However, the individual performance of registration algorithm across the various ROIs differs. For example, for the left ATR, the performance of SyN was slightly improved over DTITK.
Evaluation method based on the MSE and RMSE of fibers
Evaluation results of registrations based on RMSE of fibers
Genu  Splenium  LATR  RATR  LCST  RCST  LIFO  RIFO  Mean  

Elastic  0.1683  0.2049  0.0830  0.0907  0.1257  0.1597  0.1124  0.0929  0.1297 
Rigid  0.1739  0.2601  0.1393  0.1214  0.1967  0.2332  0.1297  0.1261  0.1726 
Affine  0.2284  0.2584  0.1093  0.1112  0.1491  0.1910  0.1425  0.1087  0.1617 
DTITK  0.0569  0.0963  0.0546  0.0546  0.0725  0.0800  0.0692  0.0597  0.0680 
FSL  0.1645  0.2049  0.0876  0.0891  0.1266  0.1569  0.1149  0.0905  0.1294 
SyN  0.0676  0.1173  0.0650  0.0678  0.0777  0.0872  0.0764  0.0755  0.0793 
From Tables 3 and 4, smaller values of MSE and RMSE indicate a better registration as it shows the difference levels between each subject after registration and the template. As values for DTITK are the lowest, the DTITK registration algorithm was shown to be the most effective in this study with the SyN method ranking second.
Evaluation method based on the FA profiles along the fiber tracts
Correlation coefficients between FA profiles of various fiber tracts on registered subjects and the template for the six registration algorithms
Elastic  Rigid  Affine  DTITK  FSL  SyN  Best  

Genu  
MEAN  0.4317  0.3890  0.4101  0.4331  0.4337  0.4572  DTITK 
STDEV  0.0974  0.0963  0.0794  0.1324  0.1001  0.1220  
p value  0.8721  0.8628  0.7364  0.9854  0.9464  0.9784  
Rank  4  5  6  1  3  2  
Splenium  
MEAN  0.4524  0.4239  0.4312  0.4554  0.4624  0.4935  DTITK 
STDEV  0.1285  0.1118  0.1140  0.1527  0.1260  0.1393  
p value  0.8773  0.7255  0.7794  0.9726  0.9137  0.9488  
Rank  4  6  5  1  3  2  
Left ATR  
MEAN  0.3945  0.3695  0.3876  0.3818  0.3966  0.4056  DTITK 
STDEV  0.0602  0.0552  0.0559  0.0663  0.0595  0.0654  
p value  0.7731  0.5686  0.6687  0.8882  0.8170  0.8785  
Rank  4  6  5  1  3  2  
Right ATR  
MEAN  0.3876  0.3679  0.3793  0.3840  0.3930  0.4068  DTITK 
STDEV  0.0666  0.0609  0.0586  0.0783  0.0681  0.0806  
p value  0.7928  0.6317  0.6886  0.9120  0.8535  0.9091  
Rank  4  6  5  1  3  2  
Left CST  
MEAN  0.4374  0.4071  0.4257  0.4282  0.4435  0.4613  DTITK 
STDEV  0.1056  0.0965  0.1016  0.1070  0.0982  0.0968  
p value  0.9002  0.8189  0.8700  0.9507  0.9259  0.9376  
Rank  4  6  5  1  3  2  
Right CST  
MEAN  0.4634  0.4256  0.4490  0.4633  0.4723  0.4956  DTITK 
STDEV  0.1105  0.1113  0.1088  0.1154  0.1067  0.1072  
p value  0.8509  0.8121  0.8249  0.8249  0.9095  0.9239  
Rank  4  6  5  1  3  2  
Left IFO  
MEAN  0.3890  0.3629  0.3691  0.3843  0.3918  0.4145  DTITK 
STDEV  0.0609  0.0618  0.0547  0.0638  0.0580  0.0628  
p value  0.6879  0.6470  0.5858  0.8446  0.7382  0.8117  
Rank  4  5  6  1  3  2  
Right IFO  
MEAN  0.4110  0.3695  0.3988  0.3958  0.4148  0.4302  DTITK 
STDEV  0.0549  0.0563  0.0528  0.0687  0.0536  0.0566  
p value  0.7669  0.6525  0.6994  0.9072  0.7918  0.8124  
Rank  4  6  5  1  3  2 
Number of failures in mapping the subject fiber tracts to the template with a correlation value greater than 0.85 for the six registration algorithms
Elastic  Rigid  Affine  DTITK  FSL  SyN  

Genu  2  3  3  0  1  0 
Splenium  2  4  5  0  1  0 
Left ATR  9  10  10  2  7  2 
Right ATR  9  9  9  0  4  1 
Left CST  1  5  2  0  1  1 
Right CST  1  4  2  0  1  0 
Left IFO  9  8  10  4  9  5 
Right IFO  7  9  8  2  5  5 
Evaluation method based on intersection angles between fiber bundles
Average cosine values of intersection angles between each subject and the template tracts
Genu  Splenium  LATR  RATR  LCST  RCST  LIFO  RIFO  Mean  

Ealstic  0.8601  0.7746  0.8330  0.8428  0.8349  0.8134  0.8484  0.8423  0.8312 
Rigid  0.7746  0.7040  0.7889  0.7908  0.7793  0.7606  0.8078  0.7657  0.7715 
Affine  0.8276  0.7434  0.8090  0.8209  0.8097  0.7846  0.8178  0.8163  0.8037 
DTITK  0.9164  0.8491  0.8849  0.8923  0.8956  0.8762  0.9132  0.9010  0.8911 
FSL  0.8840  0.8008  0.8753  0.8785  0.8764  0.8152  0.8966  0.8848  0.8220 
SyN  0.8850  0.8062  0.8607  0.8576  0.8509  0.8376  0.8786  0.8693  0.8557 
From the average \(\cos \alpha\) values of six registration algorithms in Table 7 the value of \(\cos \alpha\) in DTITK is the largest, which means the angle is the smallest. The cosine value of SyN is larger than the other registration algorithms except DTITK. In conclusion, the DTITK registration algorithm performed the best, and the SyN ranked second as observed with other evaluation methods.
Evaluation method based on spatial similarity between fiber tracts
Discussion
In this paper, we used deterministic tractography for fiber tracking and evaluated six registration methods with the distance between fibers of subjects and the template, the MSE and RMSE, the average FA profiles, and angles between fibers of subjects and the template. From Table 2, the average distance of DTITK was smallest, which implied DTITK is the best, but it was not the smallest across all ROIs. For example, in the Genu ROI, the distance determined with SyN was smaller than that with DTITK. From Tables 3 and 4, results of MSE and RMSE show that the average values for DTITK were the smallest. However across the various ROIs, no single method performed the best for all ROIs. From Figs. 5, 6, 7, 8, 9, 10, 11, 12 and Tables 5 and 6, the six registration algorithms were easily ranked and the results are basically the same. Only the results for the Affine and Rigid algorithms differed between a few ROIs. The p values in Table 5 show that correlation coefficients obtained with DTITK are the highest. Further, in Table 6, DTITK had the minimum number of failures using the selected threshold and can be considered as the best algorithm based on that criterion. According to the average FA profile evaluation, DTITK seemed to show the best registration performance. Based on the angles between fibers of subjects and the template (Table 7) evaluation, similar to the distances between fibers of subjects and the template evaluation, DTITK again showed the best registration performance because the value of \(\cos \alpha\) in DTITK is the largest, which means the intersection angle is the smallest. However, the registration algorithms did not always perform the best for all ROIs in a single subject, and may be due to the fact that since subjects and the template were chosen at random for this study, the differences in registration performance across the six registration algorithms as observed on full tract evaluation. The performance of DTITK in correctly mapping the eight fiber tracts for all subjects can be attributed to the fact that the algorithm exploits the whole tensor orientation information for the registration compared to the scalar FA values.
We also used probabilistic tractography for fiber tracking and evaluated the six registration methods with a spatial correlation similarity metric. Spatial correlation as a similarity measurement provides a precise and reproducible evaluation of registration quality when using the appropriate framework [12] which is based on multiple tracts identified with probabilistic tractography. From Fig. 13, the spatial similarity metric of fibers between subjects shows DTITK was the best. To avoid occasional bias observed with the comparison of different subjects, we also calculated the spatial similarity metric of fibers between each subject and the template, which again indicated that DTITK outperformed the rest of the algorithms. It should be mentioned that the spatial similarity values were the average of all the ROIs across all subjects.
Registration performance measurements based on deterministic tractography of different ROIs are not always same as those based on probabilistic tractography. Again from Fig. 13, the spatial similarity metric calculated on pairs of subjects and individual subjects differed, similarly as in the calculation for pairs of subjects and template. As increasing the subjects would reduce the random error, future work would include a larger study cohort, and a template based on all of the subjects. We would also like to expand the ROIs chosen for analysis.
At the moment, evaluation methods based on deterministic tractography are gradually maturing; however, methods based on probabilistic tractography are still in the primary stage of development [12]. When tracking the fibers, probabilistic tractography still requires much more calculation time than deterministic tractography [2, 5, 12]. Reduction of the tracking time in probabilistic tractography and development of new evaluation methods based on probabilistic tractography are areas of ongoing research.
Conclusions
In this paper, six open source registration algorithms were applied with randomly chosen subjects from IXI dataset and evaluated based on fiber tracts obtained through deterministic and probabilistic tractography. Results indicated that the DTITK and SyN registration algorithms outperformed the other registration algorithms overall. In conclusion, DTITK qualifies as the best registration algorithm, and SyN ranks just behind DTITK for the evaluation techniques studied. It should be noted that results from criteria based on deterministic tractography are not the same as those based on probabilistic tractography. For example, the Affine registration algorithm is generally considered as the worst based on deterministic tractography while the Rigid registration algorithm is the worst based on probabilistic tractography.
Abbreviations
 DTMRI:

diffusion tensor magnetic resonance imaging
 ROIs:

regions of interest
 FA:

fractional anisotropy
 MSE:

mean squared error
 RMSE:

residual MSE
 DTITK:

diffusion tensor imaging toolkit
 BET:

brain extraction tool
 FSL:

FMRIB software library
 DWIs:

diffusionweighted images
 ANTs:

advanced normalization tools
 PPD:

preservation of principal directions
 FACT:

fiber assessment by continuous tracking
 ATR:

anterior thalamic radiations
 IFO:

inferior frontooccipital fasciculi
 CST:

corticospinal/corticobulbar tracts
Declarations
Authors’ contributions
YW and YN conceived and designed the experiments and analyzed the data. YS, DL and GL performed the experiments. ZG and YF helped to revise the manuscript. All authors contributed analysis tools and to the writing of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We acknowledge http://braindevelopment.org/ixidataset/ as the source of the IXI data used in this paper.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated during and/or analysed during the current study are not publicly available because it related to the National Natural Science Foundation of China, and needs to be secret for the patent.
Ethics approval and consent to participate
The openaccess IXI dataset from the Hammersmith Hospital of London was used through http://www.braindevelopment.org. This data is made available under the Creative Commons CC BYSA 3.0 license.
Funding
This study was funded by the National Natural Science Foundation of China (Grant No.61402371); Natural Science Basic Research Plan in Shaanxi Province of China (Grant Nos. 2015JM6317); The Seed Foundation of Innovation and Creation for Graduate Students in NPU (Grant No. Z2016121, Z2016024).
Open AccessThis 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.
Authors’ Affiliations
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