The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations. DTI is a non-invasive technique that provides unique information regarding the microstructural characteristics of brain tissue. However, comparisons of DTI results between populations of patients and healthy volunteers have primarily focused only on scalar quantities derived from the diffusion tensor, overlooking portion of the information available in the tensor. The primary reason for this is the inability to perform accurate spatial normalization of DTI data, which is a necessary step for the comparison of certain orientation-dependent tensor information. The accuracy of spatial normalization of DTI data is compromised partly due to the fact that conventional DTI data acquisitions are based on echo- planar imaging (EPI), which suffers from distortions and image artifacts. Furthermore, to increase the accuracy of normalization, all the information of the tensor must be used in the registration process. However, accurate matching of brain structures and their tensors requires non-linear registration methods, which are sensitive to the tensors'noise. Finally, a brain template that contains not only anatomical features but also DTI information with low noise content does not exist. All of the above factors reduce the accuracy in registration of DTI data and prevent intergroup comparisons of certain diffusion and structural characteristics of brain tissue, thus limiting the clinical potential of DTI. In contrast, Turboprop-DTI is an imaging technique that provides DTI data with significantly fewer artifacts than EPI-based DTI. However, Turboprop-DTI is characterized by slower data acquisition and higher noise-levels than EPI-based DTI. We recently introduced an iterative image reconstruction method for Turboprop imaging based on the non-uniform fast Fourier transform (NUFFT), which can increase accuracy and reduce noise levels compared to conventional Turboprop reconstruction techniques. Therefore, the broad objectives of this project are: a) to develop Turboprop-DTI data acquisition strategies that, in combination with our recently introduced image reconstruction technique, will provide data with low noise content and minimal artifacts in a clinically acceptable time, b) to develop robust registration techniques that are less sensitive to the tensors'noise, in order to c) produce an accurate brain template for DTI. The successful completion of this research will allow for accurate registration of DTI data and for comprehensive comparisons of structural integrity and brain connectivity across populations. Therefore, the results of this research will enhance the role of DTI as a diagnostic tool for a wide range of clinical problems.
The development of a brain template for diffusion tensor imaging (DTI) is crucial for comparisons of neuronal structural integrity and brain connectivity across populations. The broad objective of this project is to develop robust registration techniques and produce an accurate brain template for DTI. The successful completion of this research will allow for comprehensive comparisons of structural integrity and brain connectivity across populations, and will enhance the role of DTI as a diagnostic tool for a wide range of clinical problems.
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