Multiple sampling dataset for Diffusion Tensor Imaging studies- Raw dataset

<div><b>Acquisition protocol</b></div><div><br></div>This is a DTI dataset with large number of samples for 20 healthy individuals. All the subjects were acquired at the Hospital das Clínicas at Ribeirão Preto (M: 70, F: 61, average age: 34.12 (18 – 45 years old, right-handed). The acquisition protocol was set on a 3.0T MRI scanner (Phillips, Achieva) with the following acquisitions parameters:<div>Single-shot echo-planar imaging sequence, parallel imaging factor of 2.0, matrix of 128 × 128, field of view of 240 × 240 mm (nominal resolution: 2.0 mm isotropic), transverse sections were acquired parallel to the anterior commissure-posterior line (AC-PC), N=1 samples, and 72 sections covered the entire hemisphere and brainstem without gaps. Diffusion weighting images were encoded along 32 whole sphere independent orientations and the b-value was 1,000 s/mm2.<div><div>The scanning time per dataset was approximately 4 minutes, which follows a reasonable data acquisition protocol in the clinical routine. The study was approved by the Ethics Committee of the Medicine School of Ribeirão Preto at the University of São Paulo.</div></div><div><br></div><div>More details will be given after the original paper been published. </div><div><br></div><div><br></div></div

Populational brain models of diffusion tensor imaging for statistical analysis: a complementary information in common space

<div><p>Abstract Introduction: The search for human brain templates has been progressing in the past decades and in order to understand disease patterns a need for a standard diffusion tensor imaging (DTI) dataset was raised. For this purposes, some DTI templates were developed which assist group analysis studies. In this study, complementary information to the most commonly used DTI template is proposed in order to offer a patient-specific statistical analysis on diffusion-weighted data. Methods 131 normal subjects were used to reconstruct a population-averaged template. After image pre processing, reconstruction and 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). 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. Results the MAE values presented a low MAE estimate (max(MAE) = 0.112), showing a reasonable error measure between our DTI-USP-131 template and the classical DTI-JHU-81 approach, which also shows a statistical equivalence (p<0.05) with the classical DTI template. Hence, the complementary standard deviation (SD) maps for each quantitative DTI map can be added to the classical DTI-JHU-81 template. Conclusion In this study, variability DTI maps (SD maps) were reconstructed providing the possibility of a voxel-wise statistical analysis in patient-specific approach. Finally, the brain template (DTI-USP-131) described here was made available for research purposes on the web site (http://dx.doi.org/10.17632/br7bhs4h7m.1), being valuable to research and clinical applications.</p></div

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

<div><p>Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.</p></div