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.

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.

INTRODUÇÃO: O termo "fractal" é derivado do latim fractus, que significa "irregular" ou "quebrado", considerando a estrutura observada como tendo uma dimensão não-inteira. Há muitos estudos que empregaram a Dimensão Fractal (DF) como uma ferramenta de diagnóstico. Um dos métodos mais comuns para o seu estudo é a "Box-plot counting" (Método de contagem de caixas). OBJETIVO: O objetivo do estudo foi tentar estabelecer a contribuição da DF na quantificação da rejeição celular miocárdica após o transplante cardíaco. MÉTODOS: Imagens microscópicas digitalizadas foram capturadas na resolução 800x600 (aumento de 100x). A DF foi calculada com auxílio do "software ImageJ", com adaptações. A classificação dos graus de rejeição foi de acordo com a "Sociedade Internacional de Transplante Cardíaco e Pulmonar" (ISHLT 2004). O relatório final do grau de rejeição foi confirmado e redefinido após exaustiva revisão das lâminas por um patologista experiente externo. No total, 658 lâminas foram avaliadas, com a seguinte distribuição entre os graus de rejeição (R): 335 (0R), 214 (1R), 70 (2R), 39 (3R). Os dados foram analisados estatisticamente com os testes Kruskal-Wallis e curvas ROC sendo considerados significantes valores de P < 0,05. RESULTADOS: Houve diferença estatística significativa entre os diferentes graus de rejeição com exceção da 3R versus 2R. A mesma tendência foi observada na aplicação da curva ROC. CONCLUSÃO: ADF pode contribuir para a avaliação da rejeição celular do miocárdio. Os valores mais elevados estiveram diretamente associados com graus progressivamente maiores de rejeição. Isso pode ajudar na tomada de decisão em casos duvidosos e naqueles que possam necessitar de intensificação da medicação imunossupressora.

INTRODUCTION: The term "Fractal" is derived from the Latin fractus meaning "irregular" or "broken" considering the observed structure with a non-integer dimension. There are many studies which employed the Fractal Dimension (FD) as a diagnostic tool. One of the most common methods for its study is the "Box Counting Method". OBJECTIVE: The aim of the present study was to try to establish the contribution of FD in the quantification of myocardial cellular rejection after cardiac transplantation. METHODS: Microscopic digital images were captured at 800x600 resolution (magnification 100x). FD was calculated with the aid of "ImageJ software" with adaptations. The classification of the degrees of rejection was in agreement with the "International Society for Heart and Lung Transplantation" (ISHLT 2004). The final report of the degree of rejection was confirmed and redefined after an exhaustive review of the slides by an external experienced pathologist. 658 slides were evaluated with the following distribution among the degrees of rejection (R): 335 (0R); 214 (1R); 70 (2R); 39 (3R). The data were statistically analyzed with Kruskal-Wallis tests and ROC curves being considered significant values of P < 0.05. RESULTS: There was significant statistical difference between the various degrees of rejection with the exception of R3 versus R2. The same trend was observed in applying the ROC curve. CONCLUSION: FD may contribute to the assessment of myocardial cellular rejection. Higher values are directly associated with progressively higher degrees of rejection. This may help in decision making of doubtful cases and those which contemplate the intensification of immunosuppressive medication.