Mariam Andersson^1,2,3, Jonathan Rafael-Patino^1,4,5, Hans Martin Martin Kjer^2,3, Marco Pizzolato^2,3,4, Gabriel Girard^4,5,6, Jean-Philippe Thiran^4,5,6, and Tim B. Dyrby^2,3
1* (Equal contributions), Lausanne, Switzerland and Copenhagen, Denmark, ²Copenhagen University Hospital - Amager and Hvidovre, Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark, ³Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark, ⁴Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁵Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, ⁶CIBM Center for Biomedical Imaging, Lausanne, Switzerland

Changes in diffusion properties of the intra-axonal space can be linked to brain health and function. Monte Carlo simulations of diffusion using realistic substrates are important for sequence design and for understanding diffusion MRI signals observed ex vivo and in vivo. However, it is difficult to construct tissue phantoms that accurately reflect compartment morphology. Here, we present the framework MISA for the generation of Microstructure-Informed Synthetic Axons. MISA uses statistical descriptions of axonal morphology from 3D histology to mimic real axons. We show here that MISA axons match axons from 3D histology in terms of their diffusion properties. 

Marios Georgiadis¹, Miriam Menzel², David Gräßel², Ivan Rajkovic³, Donald Born¹, Markus Axer², and Michael Zeineh^1
1Stanford University School of Medicine, Stanford, CA, United States, ²Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Julich, Germany, ³Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States

Generating a detailed network model of the brain requires a correct mapping of fiber orientations. Diffusion MRI is sensitive to neuronal alignment, yet each voxel contains hundreds of axons and other structures. Light and X-ray scattering can reveal more detailed information about nerve fibers, with much higher resolution and specificity respectively. Here we combine two methods, namely Scattered Light Imaging (SLI) and 3D-scanning small-angle X-ray scattering (3D-sSAXS), aiming to provide a micrometer-resolution gold standard for fiber orientation imaging. We compare that to high-resolution diffusion MRI of a region with challenging fiber orientations, the corona radiata, in human and non-human primates.

Kilian Stumpf¹, Britta Huch², Anna-Katinka Bracher², Thomas Hüfken¹, Meinrad Beer², Henning Neubauer², and Volker Rasche^1
1Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany, ²Department for Diagnostic and Interventional Radiology, Ulm University Medical Center, Ulm, Germany

Intravoxel incoherent motion (IVIM) imaging allows the quantification of diffusion and pseudo-perfusion in tissues and has been proposed as an alternative to conventional contrast agent enhanced (CE) T1-weighted scans. Its qualitative benefits for diagnosing juvenile idiopathic arthritis in the knee has previously been demonstrated. In this work, quantitative data of diffusion and perfusion fraction values between synovitis patients and healthy volunteers were compared in a prospective setting.  

Marco Pizzolato^1,2,3, Mariam Andersson³, Erick Jorge Canales-Rodríguez², and Tim Bjørn Dyrby^1,3
1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark, ²Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark

The strongly diffusion-weighted MRI signal contains information about the axonal parallel and perpendicular diffusivities. Powder averaging has simplified the estimation of the perpendicular diffusivity, however it is difficult to estimate the parallel one because, as we show, the powder-averaged signal at strong diffusion weightings is insensitive to it. In this work we propose a method that enables the estimation of both diffusivities from only two conventional linear PGSE signal shells collected at high b-values. The method is tested on public MGH-HCP data to retrieve axonal diffusivities and calculate the MR radius in white matter.

Ricardo Coronado-Leija¹, Omar Narvaez², Ricardo Rios³, Benoit Scherrer⁴, Alonso Ramirez-Manzanares⁵, and Luis Concha^3
1Radiology, New York University School of Medicine, New York, NY, United States, ²University of Eastern Finland, Kuopio, Finland, ³Institute of Neurobiology, Universidad Nacional Autonoma de Mexico, Queretaro, Mexico, ⁴Boston Children's Hospital, Harvard Medical School, Boston, MA, United States, ⁵Center of Research in Mathematics, Guanajuato, Mexico

Single fiber methods, such as the diffusion tensor, cannot accurately determine per-bundle characteristics in voxels occupied by multiple axonal populations specially in the case of pathology when they have different diffusion properties. In this work, we used animal models of axonal degeneration and inflammation to evaluate longitudinally the sensitivity of bundle specific metrics provided by three multi-fiber methods in a region of crossing fiber in which only one fiber population is injured.  

Ying Liao¹, Santiago Coelho¹, Jenny Chen¹, Benjamin Ades-Aron¹, Dmitry S. Novikov¹, and Els Fieremans^1
1Radiology, NYU School of Medicine, New York, NY, United States

Biophysical modeling of diffusion MRI is instrumental in achieving specificity to tissue microstructure in human white matter. The “Standard Model” (SM) framework encompasses many approaches assuming multiple Gaussian compartments. To robustly estimate SM parameters, different constraints and techniques have been applied, resulting in different outcomes. Here we evaluate the precision and accuracy of commonly used implementations and constraints for SM parameter estimation, and compare their results both in simulations and in early human brain development.

Merry Mani¹, Baolian Yang², Vincent Magnotta¹, and Mathews Jacob^1
1University of Iowa, Iowa City, IA, United States, ²GE Healthcare, Waukesha, WI, United States

A modified reconstruction is proposed for highly accelerated dMRI. The method employs machine learning in a model-based setting. The current work improves the generalizability of the deep-learned plug-and-play prior employed in the reconstruction, for enabling utilization of the reconstruction from a wide range of acquisition settings. This is achieved by learning a compact q-space representation in a rotation invariant space. The method is tested for the reconstruction of combined multi-band and in-plane accelerated data from both single-shell and multi-shell experiments. The reconstruction error is shown to be less than 3% for net acceleration of R=12 for single- and multi-shell cases.

Dimitrios G. Gkotsoulias¹, Michael Paquette¹, Cornelius Eichner¹, Roland Müller ¹, Torsten Schlumm¹, Niklas Alsleben¹, Jingjia Chen², Carsten Jäger¹, Jennifer Jaffe^3,4, André Pampel¹, Catherine Crockford^3,4, Roman Wittig^3,4, Alfred Anwander¹, Chunlei Liu², and Harald Möller^1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ²Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, United States, ³Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, ⁴Tai Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire, Abidjan, Cote D'ivoire

We present a novel approach to estimate fiber Orientation Distribution Functions (ODFs) by applying the generalized Constant Solid Angle (CSA) method to High Angular Resolution Susceptibility Imaging (HARSI) data from post-mortem chimpanzee brain. The acquisition details and analytical pipelines are presented and derived susceptibility tensor metrics and ODFs are compared to metrics derived from traditional High Angular Resolution Diffusion Imaging (HARDI). The ODFs estimated from susceptibility data indicate comparable efficiency in resolving intersecting fiber orientations compared to HARDI-ODFs and increased sensitivity to secondary direction. This suggests a potential to obtain complementary information on brain white matter microstructural properties.

Jingjia Chen¹, Dimitrios Gkotsoulias², Jennifer Jaffe^3,4, Catherine Crockford^3,4, Roman Wittig^3,4, Harald E. Möller², and Chunlei Liu^1,5
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ²Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, ⁴Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Cote D'ivoire, ⁵Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States

We investigate susceptibility tensor imaging (STI) reconstruction by separating the diamagnetic from paramagnetic susceptibility tensors using the recently introduced DECOMPOSE method. The resulting para-/diamagnetic susceptibility tensors produce spatially more coherent eigenvectors than conventional STI and it shows the potential to reduce the number of orientations needed for STI. 

Hyeong-Geol Shin¹, Jincheol Seo², Youngjeon Lee², Hwihun Jeong¹, Sooyeon Ji¹, Minjun Kim¹, Jang Woo Park³, Byeong C. Kim⁴, Kyung-Hwa Lee⁵, Seong Heon Kim⁶, Jaewon Jang⁶, Myung Kyun Woo¹, and Jongho Lee^1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, ²National Primate Research Center, Cheongju, Korea, Republic of, ³Korea Radioisotope center for pharmaceuticals, Seoul, Korea, Republic of, ⁴Chonnam National University Medical School, Gwangju, Korea, Republic of, ⁵Chonnam National University Hwasun Hospital and Medical School, Gwangju, Korea, Republic of, ⁶Kangwon National University Hospital, Chuncheon, Korea, Republic of

Recently, the susceptibility source separation method, χ-separation, was suggested to separate paramagnetic and diamagnetic susceptibility distributions in the brain, potentially providing quantitative information of paramagnetic iron and diamagnetic myelin. However, the ill-posed nature of dipole-inversion has hindered accurate estimation and direct comparison with histology. Here, we extended the model for multi-orientation GRE, resolving the ill-posedness. The new model is applied to in-vivo and ex-vivo, revealing exquisite details of susceptibility distribution. When the results are compared to iron and myelin histology, great similarities are observed, suggesting the potentials of χ-separation for non-invasively acquiring three-dimensional histological information of iron and myelin. 

Jiaen Liu¹, Peter van Gelderen², Xu Li^3,4, Jacco A. de Zwart², Kuo-Wei Lai^4,5, Jeremias Sulam⁵, Erin S. Beck⁶, Serhat V. Okar², Peter C.M. van Zijl^3,4, Daniel S. Reich², and Jeff H. Duyn^2
1Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States, ²National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, ³Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States, ⁴F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁵Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ⁶Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

High resolution submillimeter MRI of myelin and iron may enhance our capability to define neural diseases more accurately and at an earlier stage. Susceptibility-weighted MRI methods are intrinsically sensitive to myelin and iron and benefit from the increased contrast-to-noise ratio at 7 T. In this study, we systematically investigated R₂* and susceptibility distributions in cortical layers of healthy subjects with 0.3 mm in-plane resolution and 0.4 mm slice thickness. This effort was facilitated by a robust navigator-based motion- and B₀-corrected GRE sequence.

Filip Szczepankiewicz¹, Geraline Vis¹, Danielle van Westen¹, Carl-Fredrik Westin², Pia M Sundgren¹, and Markus Nilsson^1
1Clinical Sciences Lund, Lund University, Lund, Sweden, ²Radiology, Brigham and Women's Hospital, Boston, MA, United States

Diffusion weighted imaging has been used both as a radiological tool that provides a simple biomarker without quantitative qualities, and more recent efforts have endeavored to make it quantitative by fitting of biophysical models or representations. In this work, we explore the novel radiological contrasts that can be generated by introducing tensor-valued diffusion encoding. Unlike most model-based approaches, these contrasts can be produced by rapid acquisition schemes and they produce novel contrasts that may contribute new diagnostic and radiological biomarkers.

Jingjia Chen¹, Dimitrios Gkotsoulias², Jennifer Jaffe^3,4, Catherine Crockford^3,4, Roman Wittig^3,4, Harald E. Möller², and Chunlei Liu^1,5
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ²Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, ⁴Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Cote D'ivoire, ⁵Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States

DECOMPOSE-QSM was applied on high angular-resolution multi-echo gradient-echo data of a chimpanzee brain to evaluate its ability in resolving orientation mixture of substructure fiber bundles. The orientation distribution function (ODF) of the separated paramagnetic and diamagnetic susceptibility provides smooth transitions of multiple fiber orientations. Additionally, the paramagnetic maps from DECOMPOSE show a potential to resolve fiber crossings in deep gray matter regions.

Michiel Cottaar¹, Wenchuan Wu¹, Karla Miller¹, and Saad Jbabdi^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

DIPPI is a novel sequence that allows the measurement of the off-resonance field experienced by intra-axonal water, which in myelinated axons is modulated by the surrounding myealin sheath allowing for estimation of the g-ratio. Unlike other g-ratio imaging methods, DIPPI does not rely on combining multiple modalities and can provide g-ratio estimates for individual crossing fibre populations rather than averaged over voxels. We present the first in-vivo DIPPI measurements and correct it for confounds such as subject movement and eddy currents. We then estimate the g-ratio in multiple white matter tracts from the processed off-resonance field maps.