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Tutorial instructions and example data sets will be made available before the course.

The lecture slides will be made available after the course. You can register for this ExploreDTI workshop here. Once the payment is received, registration of the workshop attendee will be confirmed. Registration will close on December 1st at the latest or if the maximum number of participants has been reached. More specific details about room locations will be given later. You will need to organise your own housing for the workshop. We recommend Hotel Ave del Paraiso as it is very close to the workshop venue.

For people with little background in diffusion MRI, we would like to suggest the following literature:. On Sunday 8 December , we plan to have an informal get-together in the evening time and location tba. The mean absolute error MAE was calculated using a voxel-wise procedure, which informs the global error regarding the mean intensity value for each quantitative map.

In this study, variability DTI maps SD maps were reconstructed providing the possibility of a voxel-wise statistical analysis in patient-specific approach. The search for human brain templates and atlases has been progressing in the past decades.

Diffusion MRI

Initially, the most widely used atlases was one by Talairach and Tournoux Talairach, , being based on histology data from a single subject. These maps were created by a large number of T1-weighted MR images of normal subjects into a common template and are essentials as a target data for normalization-based group analyses Evans et al.

Following these brain templates and the raising necessity to others MR imaging modalities, the Diffusion Tensor Imaging DTI was also intensively studied to add more possibilities for the white matter brain studies Alves et al. In the past, the lack of white matter information is understandable due to a homogeneous appearance in conventional MRI, as well as in histology preparations Hua et al.

However, with the advances in the DTI image acquisition and processing, the usage of quantitative brain maps such as fractional anisotropy FA and mean diffusivity MD proved as an important measure for many studies and in the clinical routine Inglese and Bester, ; Kubicki et al. In addition, in order to understand disease patterns e. For this purpose, some DTI templates were developed Mori et al. Following the advances in DTI template reconstruction, the scientific community also provided several computational tools in order to apply DTI data on neurodegenerative diseases.

However, even though the computational application and DTI regularization on neuroscience has been growing, a patient-specific approach was still lacking. Our objective is to make available a useful information for statistical patient-specific analysis, that it is still lacking in the previous versions of the DTI-JHU template.

The acquisition protocol was set on a 3. The scanning time per dataset was approximately 4 minutes, which follows a reasonable data acquisition protocol in the clinical routine. In order to attenuate the subject motion and eddy-current induced image distortion, the raw diffusion-weighted images DWIs sequences were corrected using the recent EDDY method Andersson and Sotiropoulos, After diagonalization, the eigenvalues and eigenvectors were used to reconstruct the quantitative DTI maps, namely fractional anisotropy FA , mean diffusivity MD , relative anisotropy RA , and radial diffusivity RD as described in Equations 1 , 2 , 3 and 4.

These images were normalized using a hybrid registration approach, where an initial mode affine transformation and a diffeomorphic elastic sequential registration were applied using the FLIRT and FNIRT registration tools Jenkinson and Smith, ; Klein et al. A recent registration analysis showed that the hybrid approach adopted here results in optimum alignment Klein et al.

It is worth noting that the transformation matrices were also applied on the gradient tensors in order to rearrange the tensor information to the common space. First, only the FA maps were used here due to better structural contrast, improving registration accuracy. After the registration procedure, the transformation matrices were then applied on each quantitative map i.

To obtain population-averaged data, the linearly transformed trilinear interpolation tensor fields from each individual were used to calculate the average and standard deviation SD maps by simple scalar calculation of tensor elements. The mean absolute error MAE was calculated using a voxel-wise procedure as described in Equation 5 , which informs the global error regarding the mean intensity value for each quantitative map. The MAE evaluation is also described for each white matter tract labeled in the Mori et al.

The bottom row of Figure 1 C illustrates a 3D representation of the densest fiber tracts presented in the brain white matter, which is an important representation that is not provided in the ICBM T1-based templates Evans et al. The FA was chosen only for simplicity and the same procedure could be visualized on the other quantitative maps i. Using the local white matter tracts information, provided by the Mori et al. Table 1 summarizes the values obtained by the MAE approach.

The full table can be seen in the Supplementary data. However, as notice in the original paper of Mori et al. Hence, our DTI template was compensated with more number of samples, where additional 50 subjects were added. The reasoning for this additional number of subjects is based on the signal to noise ratio estimate in MRI acquisition, which is commonly adopted as a squared root function to N Haacke et al.

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Therefore, our DTI template showed a similar image quality than what is noticed in Mori et al. As seen in the results in Figures 1 and 2 , the DTI maps reconstruct here presented the same stereotaxic anatomical population characteristics as denoted in the previous brain template DTI-JHU Hua et al. In more details, the results in Table 1 also affirms that the distortions presented in our DTI maps can be considered as a minimal disturbance, mainly due to the registration procedure that was adopted here. Using these results, it is reasonable to use our SD maps for further statistical analysis, as seen in Figure 2.

The white matter structures that are appreciable in the DTI-USP models represent reproducible structures among normal adults.


Similarly to the Mori et al. In other words, the manual labels can be overlaid on our DTI template without loss of precision, as illustrated in Figure 2. It worth remembering that the ICBM template is based on T1-weighted images of normal volunteers and the white matter anatomical representation is missed due to its homogeneous appearance contrast in T1 weighted images. However, while the ICBM template is useful for anatomical and functional MRI studies, it does not provide detailed information about white matter anatomy.

The DTI-based atlas created in this study offers complementary information about the white matter anatomy in the same standardized coordinates. However, there is a limitation when one may need to use those maps in a statistical evaluation. In other words, the variability information was lacking in the previous Mori et al. For this reason, a complementary DTI maps were reconstructed in this study, where the populational standard deviation from healthy individuals was added in the same model presented in the DTI-JHU approach.

With this data format, it is possible to check the reproducibility and precision of quantitative DTI-related estimate, mainly in areas such as the uncinate fasciculus, the cingulum, a branch of the superior longitudinal fasciculus, and the subcortical white matter of the superior temporal gyrus can be clearly identified Mori et al.

The full analysis, for each white matter tract, is given as a supplementary dataset. It is important to understand the data fluctuation and limitation for brain regions that still suffer from lack of precision in diffusion-weighted acquisition. Mori et al.

Resolving Fine Cardiac Structures in Rats with High-Resolution Diffusion Tensor Imaging

In general, the main contribution given by Mori et al. The DTI-USP template proposed in this study should be interpreted as a statistical framework that could be applied to a patient-specific evaluation approach, where a voxel-wise statistical inference can be calculated over the entire brain volume.

The main new possibility that is added with our DTI template is the patient-specific evaluation on DTI data, which greatly improve the clinical evaluation, e. Some practical examples of DTI-USP usage can be commented, such as the brain surgical planning where the DTI image from a single patient should be analyzed in order to find the white matter fibers disturbance caused by the disease Berman, ; Kim et al. Another application could be thought in longitudinal progression of neurodegenerative brain diseases e.

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Multiple Sclerosis that affects several white matter areas Ganiler et al. The proposed DTI-USP template is a useful dataset for single subject evaluation, bringing more possibilities to surgical planning, neurodegenerative disease follow-up and general brain studies that rely on DTI data analysis. It worth to remembering that all the DTI maps provided here are already in the ICBM space that is useful for many statistical applications using this common space.

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Although there is no complementary computational toolkit offered in this study for statistical evaluation purposes , the brain templates described here were made available on the web site Senra and Murta, , which could be valuable for future statistical evaluations in DTI-related studies. Res Biomed Eng. DOI: Diffusion tensor imaging studies in vascular disease: a review of the literature. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.

Berman J.