Daan Christiaens^1,2, Thomas Vande Casteele^2,3,4, Maarten Laroy^2,4, Margot Van Cauwenberge^2,3,4, Jan Van den Stock^3,4, Filip Boeckaert^3,4, Stefan Sunaert^2,5,6, Mathieu Vandenbulcke^3,4, Frederik Maes^1,2, and Louise Emsell^2,3,4,5
1Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium, ²Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium, ³Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium, ⁴Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium, ⁵Department of Imaging & Pathology, Translational MRI, KU Leuven, Leuven, Belgium, ⁶Radiology, University Hospitals Leuven, Leuven, Belgium

¹¹C-UCB-J PET offers a unique imaging modality to map synaptic density in the human brain in vivo with high specificity. Here, we investigate its correlation with several diffusion MRI metrics and microstructure model parameters in diffusion-weighted PET-MR. We report moderate negative correlation of ¹¹C-UCB-J uptake with measures of anisotropy, consistent with a hypothesis that higher synaptic density is associated with a more disorganised neurite configuration. We also find weak positive correlation to the intra-axonal signal fraction in cortical grey matter. As such, ¹¹C-UCB-J PET-MR can further the interpretation and in vivo validation of more advanced microstructure models of grey matter.

Misha Pieter Thijs Kaandorp^1,2, Frank Zijlstra^1,2, João P. de Almeida Martins^1,2, Christian Federau^3,4, and Peter T. While^1,2
1Department of Radiology and Nuclear Medicine, St. Olav’s University Hospital, Trondheim, Norway, ²Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway, ³Institute for Biomedical Engineering, University and ETH Zürich, Zurich, Switzerland, ⁴AI Medical, Zürich, Switzerland

We demonstrate that a high learning rate, small network size, and early stopping in unsupervised deep learning for IVIM model fitting can result in sub-optimal solutions and correlated parameters. In simulations, we show that prolonging training beyond early stopping resolves these correlations and reduces parameter error, providing an alternative to exhaustive hyperparameter optimization. However, extensive training results in increased noise sensitivity, tending towards the behavior of least squares fitting. In in-vivo data from glioma patients, fitting residuals were almost identical between approaches, whereas pseudo-diffusion maps varied considerably, demonstrating the difficulty of fitting D* in these regions.

Paul Tar¹, N.A. Thacker², M. Babur², G. Lipowska-Bhalla², S. Cheung², R. Little², K.J. Williams², and J.P.B. O'Connor^2
1Cancer, University of Manchester, Manchester, United Kingdom, ²University of Manchester, Manchester, United Kingdom

In oncology, preclinical experiments using MRI often evaluate spatially complex and heterogeneous tumor micro-environments which have non-Gaussian data and small sample sizes, with cohorts typically of 10 animals or less. As a consequence, conventional use of t-tests that evaluate distribution parameters such as means and percentiles can be ineffective. Further, the cohort-level nature of such analyses also limits investigations to groups of tumors rather than identifying individually responding tumors. In contrast, Linear Poisson Modelling (LPM) enables quantitative analysis of complex data, can operate in small data domains and can also provide per-tumor assessments 2.ideal for co-clinical trials.

Roberta Maria Lorenzi¹, Alice Geminiani¹, Claudia AM Gandini Wheeler-Kingshott^1,2,3, Fulvia Palesi¹, Claudia Casellato¹, and Egidio D'Angelo^1,3
1Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom, ³Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy

Following MRI recordings, the connectome can be reconstructed and BOLD-signals simulated extracting relevant parameters on brain organization and dynamics. This requires mathematical models of neural activity in specific brain regions. However, models of the cerebellar circuit are still missing. We present here a biologically-driven mean-field (MF) model summarising the main statistical moments of cerebellar granular layer activity as the first “lego-brick” toward full cerebellar network reconstruction. Once integrated into brain simulators, like Dynamic Causal Modelling and The Virtual Brain, the cerebellar MF models will improve the investigation of neuronal functions at the origin of hemodynamic responses captured by BOLD fMRI.

Jibin Tang¹, Maximilian Pietsch^2,3, and Jacques-Donald Tournier^2,4
1Department of Medical Engineering and Physics, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom, ⁴Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Single-shell three-tissue constrained spherical deconvolution (SS3T-CSD) has been proposed to decompose the dMRI signal into three different tissue types from single-shell data. Here, we evaluate the SS3T-CSD method over a range of different situations using real data and simulations, varying parameters including SNR, partial volume effect, b-value, number of iterations, and tissue response functions. We evaluate their effect on results by comparing with the ground truth and the results estimated from MSMT-CSD. Our results indicate that while SS3T-CSD performs well, it is sensitive to some of these factors and shows some deficiencies in certain situations.

Elena Grosso¹, Claudia AM Gandini Wheeler-Kingshott^1,2,3, Egidio D'Angelo^1,3, Paolo Vitali⁴, and Fulvia Palesi^1
1Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom, ³Brain Connectivity Centre Research Unit, IRCCS Mondino Foundation, Pavia, Italy, ⁴Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy

Several mesoscopic multi-compartment diffusion MRI models have been developed to describe the complexity of water molecules diffusion in the brain by making assumptions. One frequently used model is Neurite Orientation Dispersion and Density Imaging (NODDI). Here, the aim is to propose a personalized-NODDI (pNODDI) by making NODDI assumptions subject-specific. Our findings show that NODDI assumptions impact on output metrics depending on brain region and that these metrics are able to discriminate between healthy and temporal lobe epilepsy subjects. pNODDI provides a mean to personalize a successful and clinically feasible model as NODDI, increasing metrics sensitivity to pathological alterations. 

Mahsa Zoraghi¹, Nico Scherf^2,3, Carsten Jaeger¹, Ingolf Sack⁴, Sebastian Hirsch^5,6, Stefan Hetzer^5,6, and Nikolaus Weiskopf^1,7
1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ²Methods and Development Group Neural Data Science and Statistical Computing, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany, ⁴Department of Radiology, Charité – Universitätsmedizin Berlin, Berlin, Germany, Berlin, Germany, ⁵Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Berlin, Germany, ⁶Berlin Center for Computational Neuroscience, Berlin, Germany, ⁷Faculty of Physics and Earth Sciences, Felix Bloch Institute for Solid State Physics, Leipzig University, Leipzig, Germany

Recent studies on the brain suggest a link between brain function and biomechanics of brain. In this study we develop a novel simulation framework to investigate blood flow-induced deformation caused by increased neural activity in brain tissue considering its mechanical properties. We further investigate its impact in simulated fMRI experiments. Our results demonstrate that the displacement in gyrus induced by local volume and stiffness change might not be directly resolved due to limited fMRI resolution, but can lead to artifacts when interpreting measurements at layer-level resolution. Our findings may help to systematically analyze potential resulting artefacts in high resolution fMRI.

Andreea Hertanu^1,2, Lucas Soustelle^1,2, Ludovic de Rochefort^1,2, Axelle Grélard³, Antoine Loquet³, Erick J. Dufourc³, Gopal Varma⁴, David C. Alsop⁴, Guillaume Duhamel^1,2, and Olivier M. Girard^1,2
1Aix Marseille Univ, CNRS, CRMBM, Marseille, France, ²APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France, ³CBMN UMR 5248, CNRS, University of Bordeaux, Bordeaux INP, Pessac, France, ⁴Division of MR Research, Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States

Marcel Gutberlet^1,2, Enrico Pannicke^2,3, Inga Bruesch⁴, Regina Rumpel⁴, Eva-Maria Wittauer⁴, Florian W. R. Vondran⁵, Frank Wacker^1,2, and Bennet Hensen^1,2
1Institute of Diagnostic and Interventional Radiology, Medical School Hannover, Hannover, Germany, ²STIMULATE-Solution Centre for Image Guided Local Therapies, Magdeburg, Germany, ³Department Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany, ⁴Institute for Laboratory Animal Science and Central Animal Facility, Medical School Hannover, Hannover, Germany, ⁵Clinic for General, Abdominal and Transplant Surgery, Medical School Hannover, Hannover, Germany

In microwave ablation (MWA), heat induced susceptibility changes impair the assessment of the ablation zones using proton resonance frequency shift based magnetic resonance (MR) thermometry. In this work, these heat related field changes were modelled to improve the accuracy of MR thermometry to monitor microwave ablation. In a study of hepatic MWA in an in-vivo swine model, the proposed method provided increased accuracy to assess the ablation zone compared to uncorrected MR thermometry.

Samuel St-Jean^1,2, Alexander Leemans³, Filip Szczepankiewicz¹, Christian Beaulieu², and Markus Nilsson^1
1Clinical Sciences Lund, Lund University, Lund, Sweden, ²University of Alberta, Edmonton, AB, Canada, ³Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands

We present a framework to compute a response function for each voxel using a fingerprinting approach and a per voxel, per compartment estimation of the fiber orientation distribution (FOD) function for diffusion MRI. The method first matches the powder-averaged signal to a database of candidate signals and estimates a unique FOD from this matched signal for each compartment. This is done by turning the candidate signals into continuous Legendre polynomials, leading to a set of linear equations to estimate coefficients of the FODs, without requiring a prior segmentation or averaging multiple response functions.

Dylan Archer Dingwell^1,2 and Charles H Cunningham^1,2
1Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada

In order to model signal mechanisms relevant to HP 13C MRI of lactate, we developed a novel particle-based MR model which extends the Brownian dynamics simulator Smoldyn. The model performs concurrent calculation of a forward solution of the Bloch equations for simulated particles using quaternion rotations. Reaction kinetics of LDH-mediated conversion of pyruvate to lactate were simulated and compared to a benchtop experiment (LDH activity assay). Modelling of particle compartmentalization and motion were also tested in simulated structures.

Jonas Lynge Olesen^1,2, Andrada Ianus³, Noam Shemesh³, and Sune Nørhøj Jespersen^1,2
1Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Aarhus University, Aarhus, Denmark, ²Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark, ³Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal

The potential effect of compartmental water exchange and somas on the diffusion MRI signal in grey matter tissue are currently open questions with considerable implications for biophysical modelling of grey matter at (pre-)clinical diffusion time scales. Recent studies have begun to tackle these questions and found evidence that compartmental water exchange dominates the diffusion time dependence. Here, we extend the application of the Standard Model with exchange between the neurites and extracellular water (SMEX) to in vivo rat brain and again find evidence for exchange-dominated time dependence with very short neurite residence time on the order of 2 ms.

Stefan Kuczera¹, Fredrik Langkilde¹, and Stephan E. Maier^1,2
1Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenborg, Sweden, ²Department of Radiology, Brigham Women’s Hospital, Harvard Medical School, Boston, MA, United States

A novel clinical protocol for prostate diffusion imaging is presented. The commonly applied strategy of averaging MR signal at a small number of b-values is replaced by the acquisition of a whole range of b-values in conjunction with a model fitting post precessing step. With no increase in acquisition time, our results show that the same image quality can be achieved. As a main benefit, modelling is made more consistent and largely b-value independent for both the simple ADC model, as well as for more complex signal representations such as the kurtosis model.