Antonio Napolitano¹, Martina Lucignani¹, Chiara Parrillo¹, Maria Camilla Rossi Espagnet¹, Daniela Longo¹, and Lorenzo Figà Talamanca^1
1Bambino Gesù Children's Hospital, Rome, Italy

Investigating optimal NODDI intrinsic diffusivity d|| values in the first years of life would provide accurate diffusion properties estimation. We aimed to investigate d|| for 1-36 months range. Diffusion data from 265 baby Human Connectome Project subjects were processed and used for NODDI model fitting with MDT tool. Among different d|| tested, optimal value was chosen according to maximum LogLikelihood maps and investigated as a function of age.  Infants exhibit optimal d||  values of 1.95 µm2 ms-1, where d|| decreases for increasing age. Based on our results, 1.7 µm2 ms-1 represent a suboptimal assumption in the case of infant population.

Anna Stefańska-Bernatowicz¹, Weronika Mazur², Rafał Obuchowicz³, and Artur Tadeusz Krzyżak^1
1Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Kraków, Poland, ²Faculty of Physics and Applied Computer Sciences, AGH University of Science and Technology, Kraków, Poland, ³Department of Diagnostic Imaging, Jagiellonian University Medical College, Kraków, Poland

BSD DTI is a method to reduce systematic errors observed in standard Diffusion Tensor Imaging (DTI) of anisotropic tissues. In the work the effect of systematic errors reduction was shown based on DTI of the brain. Diffusion tensor parameters and tractography were compared between standard DTI (STD) and after the application of BSD DTI method in 11 ROIs chosen in axial and sagittal image planes. Depending on ROI eigenvalues and FA were under- or overestimated in STD, which was corrected by BSD DTI. Reduction of systematic errors leads to decreased standard deviation and improved diffusion tensor tractography.

Juan Luis Villarreal Haro¹, Remy Gardier¹, Erick J Canales-Rodríguez¹, Gabriel Girard^1,2,3, Jean-Philippe Thiran^1,2,3, and Jonathan Rafael-Patino^1,3
1Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ²CIBM, Center for Biomedical Imaging, Lausanne, Switzerland, ³Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

One main goal of Diffusion-Weighted Magnetic Resonance Imaging is to infer microscopic tissue properties from the measured signal. The use of Monte-Carlo Simulations for DW-MRI on realistic substrates will help the study of DWI-MRI signals in controlled environments and to investigate, extract, and validate approaches for the understanding of white matter features. In this work, we present a novel framework for creating complex white matter phantoms with unprecedented characteristics like high packing density greater than 90% intra-axonal volume fraction and voxel size of (140 um)³.

Justino Rafael Rodríguez-Galván¹, Carmen Martín-Martín¹, Antonio Tristán-Vega¹, Carlos Alberola-López¹, and Santiago Aja-Fernández^1
1Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain

Validation in Deep Learning for enhancement of diffusion Magnetic Resonance Imaging results usually sticks to conventional image similarity metrics. Despite those results, further research on synthetic data may result in discordance with the real one. In this paper we have compared 61 real gradient directions against 61 quasi-identical synthetic gradient directions, obtained by subsampling the real ones, for the assessment of the differences between chronic and episodic migraine patients. Even with high image comparison metrics, differences in t-test are not compelling. For that reason, we do not recommend synthetic images for clinical use.