Kulam Najmudeen Magdoom^1,2,3, Alexandru V. Avram^1,2,3, Dario Gasbarra⁴, Thomas Witzel⁵, Susie Y Huang⁵, and Peter J. Basser^1,3
1Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Healt, Bethesda, MD, United States, ²The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States, ³Center for Neuroscience and Regenerative Medicine, Uniformed Services University of Health Sciences, Bethesda, MD, United States, ⁴University of Helsinki, Helsinki, Finland, ⁵Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States

Electron microscopy of nervous tissue reveals a multitude of compartments separated by plasma membranes where the diffusion of water may be hindered. Diffusion tensor distribution (DTD) MRI assumes each voxel consists of an ensemble of diffusion tensors described by a tensor variate probability distribution function in line with the EM observed microstructure. In this study, we show in vivo results obtained in the living human brain using a new DTD framework on the Connectome scanner using 300 mT/m gradients and a novel time efficient and concomitant field free pulse sequence which allowed shorter echo time for a given b-value.

Thomas R Barrick¹, Catherine A Spilling^1,2, Ian R Storey¹, Matt G Hall^3,4, and Franklyn A Howe^1
1St George's, University of London, London, United Kingdom, ²King's College London, London, United Kingdom, ³National Physical Laboratory, London, United Kingdom, ⁴University College London, London, United Kingdom

Quasi-Diffusion Imaging (QDI) is based on a model of diffusion dynamics that assumes diffusion is locally Gaussian within a heterogeneous tissue microstructural environment. We show here that QDI provides a compelling model-based alternative to Diffusional Kurtosis Imaging (DKI). Tensor measures determined by QDI are highly correlated with DKI, but exhibit greater parameter independence, indicating that DKI study results showing sensitivity and specificity to disease could be improved using QDI. As QDI also overcomes the limitations of DKI and can be acquired reproducibly in clinically feasible time it is a non-Gaussian diffusion imaging technique that overcomes barriers to clinical translation.  

Paulien H.M. Voorter^1,2, Jacobus F.A. Jansen^1,2,3, Merel M. van der Thiel^1,2, Julie Staals^4,5, Robert-Jan van Oostenbrugge^2,4,5, Maud van Dinther^4,5, Walter H. Backes^1,2,5, and Gerhard S. Drenthen^1,2
1Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands, ²School for Mental Health & Neuroscience, Maastricht University, Maastricht, Netherlands, ³Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ⁴Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands, ⁵School for Cardiovascular Disease, Maastricht University, Maastricht, Netherlands

Acquisition of intravoxel incoherent motion (IVIM) images with diffusion sensitization in at least six directions (IVIM tensor imaging) provides the unique opportunity to non-invasively measure the anisotropy of both the parenchymal and microvascular diffusivity (D and D*). We demonstrate the feasibility of whole-brain IVIM tensor analysis by utilizing a physics-informed neural network fitting approach to achieve more accurate assessment of D, D*, and the corresponding tensors. The fractional anisotropy of D* (FA(D*)) was explored for different brain tissue regions, which revealed lower FA(D*) in cortical gray matter and higher FA(D*) in deep gray matter compared to white matter.

Gerhard S Drenthen^1,2, Jacobus FA Jansen^1,2, Merel M van der Thiel^1,2, Paulien HM Voorter^1,2, and Walter H Backes^1,2
1School for Mental Health & Neuroscience, Maastricht University, Maastricht, Netherlands, ²Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands

Besides the parenchymal diffusion and microvascular pseudo-diffusion, a third diffusion component was previously found in cerebral intravoxel incoherent motion (IVIM), representing interstitial fluid. However, estimating this intermediate IVIM component can be challenging, since spectral decomposition techniques have strong dependence on the number of samples (i.e. acquired b-values) and SNR. Therefore, it is important to know which b-values are essential to be acquired. In this study, an optimal b-value sampling for estimating the intermediate component is derived using a genetic algorithm. The optimal sampling (0,20,100,270,280,370,540,650,660,710,720,790,980,990,1000 s/mm²) was shown to outperform linear and logarithmic samplings, for both simulated and in vivo data. 

Julien Lamy¹, Mathieu Santin², and Paulo Loureiro de Sousa^1
1ICube, Université de Strasbourg-CNRS, Strasbourg, France, ²Institut du Cerveau, Paris, France

The DW-SSFP sequence is useful to attain strong diffusion-weighting on clinical systems while keeping a short repetition time. However, its analytical model does not account for the echo time. Using a 3D EPG model, we show that the echo time plays an important role, and that it cannot be neglected. We then provide a new method to jointly estimate T₂ and the diffusion tensor on an ex-vivo fetal brain at 3 T.

Alfredo Ordinola¹ and Evren Özarslan^1
1Department of Biomedical Engineering, Linköping University, Linköping, Sweden

The distribution of net displacements, commonly referred to as the ensemble average propagator, has been measured using Stejskal-Tanner experiments. Here, a recently introduced diffusion sensitization method is employed in a similar manner to obtain the distribution of mean positions. By utilizing the two schemes synergistically, low b-value measurements are employed to decouple dynamic second moments from static ones. We illustrate our findings on an excised mouse spinal cord imaged using a benchtop MRI scanner.

Alberto De Luca^1,2, Alexander Leemans², Chantal MW Tax^2,3, Kurt G Schilling⁴, and Geert-Jan Biessels^1
1Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands, ²Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, ³Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, ⁴Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States

We evaluate the benefits of shifting from a global white matter (WM) model to an adaptive (voxel-wise) model in the Generalized Richardson Lucy (GRL) framework. Using simulations, we show that GRL with an adaptive model (AGRL) could resolve crossing fiber configurations with heterogeneous properties, whereas conventional GRL did not. In in-vivo data, AGRL simultaneously used different deconvolution models. Compared to GRL, fiber orientation distributions of AGRL showed remarkable angular differences, especially for the second and third peak. Tractography with AGRL resulted in a more extensive reconstruction of the arcuate fasciculus, suggesting adaptive modelling as a promising future direction.

Cornelius Eichner¹, Michael Paquette¹, Hannah Gerbeth¹, Carsten Jäger², Christian Bock³, Guillermo Gallardo¹, Torsten Möller⁴, Catherine Crockford^5,6, Roman Wittig⁶, Nikolaus Weiskopf^2,7, Angela D Friederici¹, and Alfred Anwander^1
1Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ²Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany, ⁴Kolmården Wildlife Park, Norrköping, Sweden, ⁵CNRS Institute of Cognitive Sciences, Lyon, France, ⁶Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, ⁷Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany

We present Local Spherical Deconvolution (LSD), a diffusion MRI deconvolution method to reconstruct crossing fiber directions in the brain. In contrast to previous approaches which assumed a single deconvolution kernel for the entire brain, LSD utilizes information theory to identify an optimal kernel in each image voxel. Using a high-resolution post-mortem chimpanzee brain, we show that fibers are reconstructed with LSD with increased precision and a reduced number of false peaks compared to conventional methods. A test-retest analysis supports the stability and accuracy of LSD.

David Romero-Bascones¹, Bramsh Qamar Chandio², Shreyas Fadnavis², Jong Sung Park², Serge Koudoro², Unai Ayala¹, Maitane Barrenechea¹, and Eleftherios Garyfallidis^2
1Biomedical Engineering Department, Mondragon Unibertsitatea, Mondragón, Spain, ²Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States

White matter bundle atlases play a crucial role in the segmentation of bundles and the understanding of brain connectomes. However, the construction of streamline atlases that accurately represent the underlying population anatomy is challenging. In this work, we present BundleAtlasing, a new method to compute population-specific bundle atlases in the space of streamlines. The proposed approach is based on two key aspects: an iterative groupwise unbiased bundle registration, and a pairwise bundle combination strategy. We show that our method is able to correctly generate unbiased atlases that represent the average group anatomy of a population.

Matteo Battocchio^1,2, Simona Schiavi^1,3, Maxime Descoteaux², and Alessandro Daducci^1
1University of Verona, Verona, Italy, ²University of Sherbrooke, Sherbrooke, QC, Canada, ³Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy

We introduce a new formulation for bundle-based tractography global reconstruction (bundle-o-graphy). The idea is to move the focus from streamlines to bundle-based reconstruction thanks to a convenient reduction in the number of parameters needed to be optimized and the injection of anatomical priors. We show the potential of our approach on both synthetic and real data.

Richard Stones¹, Ahmad Beyh^1,2, Rachel Barrett¹, Alessandro Daducci³, and Flavio Dell'Acqua^1
1Natbrainlab, King's College London, London, United Kingdom, ²University College London, London, United Kingdom, ³Universita di Verona, Verona, Italy

In this study we present a novel method for building high quality tractography-based DWI brain templates. We use streamline normalisation to obtain the correct anatomical fibre orientation information in template space and then regenerate the diffusion signal using the COMMIT framework. The template fODF field and tractography reconstructions show good anatomical agreement with well known white matter structures. This framework could provide new templates, atlases and tools for normalisation of DWI data, and simplify the creation of realistic DWI phantoms.