Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy.

Development of a diffusion tensor (DT) template that is representative of the micro-architecture of the human brain is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the generation of a detailed white matter atlas. Furthermore, a DT template in ICBM space may simplify consolidation of information from DT, anatomical and functional MRI studies. The previously developed “IIT DT brain template” was produced in ICBM-152 space, based on a large number of subjects from a limited age-range, using data with minimal image artifacts, and non-linear registration. That template was characterized by higher image sharpness, provided the ability to distinguish smaller white matter fiber structures, and contained fewer image artifacts, than several previously published DT templates. However, low-dimensional registration was used in the development of that template, which led to a mismatch of DT information across subjects, eventually manifested as loss of local diffusion information and errors in the final tensors. Also, low-dimensional registration led to a mismatch of the anatomy in the IIT and ICBM-152 templates. In this work, a significantly improved DT brain template in ICBM-152 space was developed, using high-dimensional non-linear registration and the raw data collected for the purposes of the IIT template. The accuracy of inter-subject DT matching was significantly increased compared to that achieved for the development of the IIT template. Consequently, the new template contained DT information that was more representative of single-subject human brain data, and was characterized by higher image sharpness than the IIT template. Furthermore, a bootstrap approach demonstrated that the variance of tensor characteristics was lower in the new template. Additionally, compared to the IIT template, brain anatomy in the new template more accurately matched ICBM-152 space. Finally, spatial normalization of a number of DT datasets through registration to the new and existing IIT templates was improved when using the new template.

Digital diffusion tensor imaging (DTI) templates of the adult human brain are commonly used in neuroimaging research, and their characteristics influence the accuracy of the application. However, a systematic evaluation of the characteristics and performance of standardized and study-specific DTI templates has not been conducted. The purpose of this work was to compare eight available standardized DTI templates to each other (ICBM81, ENIGMA, FMRIB58, SRI24, IIT2, NTU-DSI-122-DTI, IIT v.3.0, Eve), as well as to study-specific templates, in terms of template characteristics (image sharpness, ability to identify small brain structures, artifacts, mean values, noise properties) and performance in spatial normalization and detection of small inter-group FA differences. The IIT v.3.0 template was shown to combine a number of desirable characteristics: includes full-tensor information, is population-based, has high image sharpness, shows no visible artifacts, has low noise levels, has diffusion tensor properties and spatial features representative of data from the average individual adult brain. Furthermore, the IIT v.3.0 template was shown to allow higher inter-subject DTI spatial normalization accuracy, and detection of smaller inter-group FA differences, compared to all other templates, including study-specific templates. These findings were consistent when evaluating the templates in younger as well as older adult cohorts.

The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations, as well as for the development of a white matter atlas. Previous efforts to produce a DTI brain template have been compromised by factors related to image quality, the effectiveness of the image registration approach, the appropriateness of subject inclusion criteria, the completeness and accuracy of the information summarized in the final template. The purpose of this work was to develop a DTI human brain template using techniques that address the shortcomings of previous efforts. Therefore, data containing minimal artifacts were first obtained on 67 healthy human subjects selected from an age-group with relatively similar diffusion characteristics (20–40 years of age), using an appropriate DTI acquisition protocol. Non-linear image registration based on mean diffusion-weighted and fractional anisotropy images was employed. DTI brain templates containing median and mean tensors were produced in ICBM-152 space and made publicly available. The resulting set of DTI templates is characterized by higher image sharpness, provides the ability to distinguish smaller white matter fiber structures, contains fewer image artifacts, than previously developed templates, and to our knowledge, is one of only two templates produced based on a relatively large number of subjects. Furthermore, median tensors were shown to better preserve the diffusion characteristics at the group level than mean tensors. Finally, white matter fiber tractography was applied on the template and several fiber-bundles were traced.

Purpose To investigate the effect of standardized and study-specific human brain diffusion tensor templates on the accuracy of spatial normalization, without ignoring the important roles of data quality and registration algorithm effectiveness. Materials and Methods Two groups of diffusion tensor imaging (DTI) datasets, with and without visible artifacts, were normalized to two standardized diffusion tensor templates (IIT2, ICBM81) as well as study-specific templates, using three registration approaches. The accuracy of inter-subject spatial normalization was compared across templates, using the most effective registration technique for each template and group of data. Results It was demonstrated that, for DTI data with visible artifacts, the study-specific template resulted in significantly higher spatial normalization accuracy than standardized templates. However, for data without visible artifacts, the study-specific template and the standardized template of higher quality (IIT2) resulted in similar normalization accuracy. Conclusion For DTI data with visible artifacts, a carefully constructed study-specific template may achieve higher normalization accuracy than that of standardized templates. However, as DTI data quality improves, a high-quality standardized template may be more advantageous than a study-specific template, since in addition to high normalization accuracy, it provides a standard reference across studies, as well as automated localization/segmentation when accompanied by anatomical labels.