27th ISMRM Annual Meeting • 11-16 May 2019 • Montréal, QC, Canada

Digital Poster Session
Diffusion

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Wednesday, 15 May 2019
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Diffusion MRI: Image Reconstruction
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion MRI: Fiber Orientations & Tracking
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion MRI: Artefact Correction
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion: Neuro Applications
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion: Body Applications
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion MRI: Signal Representation & Modelling
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion MRI: Diffusion Gradient Waveform Design & Optimization
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 08:15 - 09:15

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Diffusion MRI: Data Acquisition
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15

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Microstructural Modelling & Mapping
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15

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Microstructure Modeling: 1
Digital Poster
Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15

 Computer # 3559. 51 Machine learning based estimation of axonal properties in the presence of beading Kévin GINSBURGER, Felix MATUSCHKE, Fabrice POUPON, Jean-François MANGIN, Markus AXER, Cyril POUPON In this work, we investigate the potential of machine learning techniques to make one step forward by quantitatively estimating beading amplitude, a specific marker of pathological beading using frequency-dependent changes in diffusion measurements. Classification and regression are performed using Extremely Randomized Trees from OGSE signals corresponding to 6 distinct frequencies and synthesized from numerical simulations in realistic white matter phantoms depicting beaded axons. 3560. 52 Tensorial formulation allowing to verify or falsify the microstructural standard model from multidimensional diffusion MRI Jose Pozo, Santiago Coelho, Alejandro Frangi The standard model (SM) has been proposed as an appropriate diffusion MRI microstructural model for brain white matter. Using the cumulant expansion up to 2nd order in the b-tensor, it has been recently shown that multidimensional diffusion encoding makes the model parameter estimation problem well-posed. However, the tensorial properties of the expansion and their relationship with the SM assumptions have not been exploited. We reformulate the solution of the SM in an elegant tensorial form and analyse the constraints the SM imposes, showing how they can potentially falsify the model. Some simulations show the test feasibility for high-quality signals (SNR≥50$\text{SNR}\ge 50$). 3561. 53 Permeable Barrier Modeling of Age Induced Changes in the Time Dependent Diffusion Eigenvalues Vadim Malis, Sinha Shantanu, Edward Smitaman, Usha Sinha The time dependence of the diffusion eigenvalues derived from diffusion tensor imaging allows one to probe tissue microstructure.  A diffusion model is required to derive inferences about the microstructure from the time dependent eigenvalue data.  We applied the Random Permeable Barrier Model1 to the time dependent diffusion data to infer age related changes in skeletal muscle microarchitecture. The tertiary eigenvalue at the largest diffusion time was significantly different between young and senior cohorts.  Model derived myofiber size decreased and free diffusion coefficient increased with age, though neither parameter reached significance. 3562. 54 Estimating mixtures of 6D diffusion tensor distributions with "Magic DIAMOND": are b-plates better than b-spheres? Alexis Reymbaut, Alex Valcourt Caron, Guillaume Gilbert, Simon Warfield, Maxime Descoteaux, Benoit Scherrer Tensor-valued diffusion encoding enables disentangling isotropic and anisotropic diffusion components. However, its impact on estimating brain microstructural features has only been studied in a handful of parametric models. In this work, we evaluate the Magic DIAMOND model, that allows characterization of crossing fascicles and assessment of diffusivities for each, using combinations of linear, planar and spherical encodings in vivo. Building statistics through stratified bootstrap, we show that spherical encoding substantially increases the variance in estimated parameters and should be avoided. Planar encoding, on the other hand, did not offer clear improvement or worsening within our current acquisition scheme and setup. 3563. 55 MicroLearn: Framework for machine learning, reconstruction, optimization and microstructure modeling Shreyas Fadnavis, Marco Reisert, Hamza Farooq, Maryam Afzali, Cheng Hu, Bago Amirbekian, Eleftherios Garyfallidis MicroLearn is a Machine Learning and Model Fitting framework that enables modular construction of multi-compartment microstructure models in crossings with fast and accurate parameter estimation. 3564. 56 Investigating the Benefits of Incorporating Higher Order Spherical Harmonics in Axon Diameter Measurements Qiuyun Fan, Aapo Nummenmaa, Qiyuan Tian, Ned Ohringer, Thomas Witzel, Lawrence Wald, Bruce Rosen, Susie Huang Separating out the scalar and orientation-dependent components of the diffusion MRI signal offers the possibility of increasing sensitivity to microscopic tissue features unconfounded by the fiber orientation. Recent approaches to estimating apparent axon diameter in white matter have employed spherical averaging to avoid the confounding effects of fiber crossings and dispersion at the expense of losing sensitivity to effective compartment size. Here, we investigate the feasibility and benefits of incorporating higher-order spherical harmonic (SH) components into a rotationally invariant axon diameter estimation framework and demonstrate improved precision of axon diameter estimation in the in vivo human brain. 3565. 57 On the feasibility of apparent exchange rate mapping in non-periodic systems with multiple cell sizes Dominik Ludwig, Frederik Laun, Peter Bachert, Tristan Kuder Apparent exchange rate (AXR) mapping might provide an insight into the exchange of water between intra- and extracellular space by using a double-diffusion encoded sequence with varying mixing times between the two gradient pairs. The connection between AXR and permeability has already been validated for simplified two-compartment models. However, multi-compartment systems and non-periodic geometries have not been evaluated so far. In this study, we were able to show that it is also possible to reliably connect permeabilities to AXR-values for these geometries. Furthermore, the AXR was only dependent on average cell size and not on the number of compartments. 3566. 58 Computational frameworks for multi-fascicle models: impact on microstructural descriptors Benjamin Schloesing, Maxime Taquet, Jean-Philippe Thiran, Simon Warfield, Benoit Scherrer When performing operations on multi-fascicle DCI models, the need to preserve microstructural descriptors such as fFA and fMD is important. In this work we compared different multi-fascicle computational frameworks by assessing their impact on microstructure properties. Specifically, we investigated the impact after geometrical transformation and averaging of multi-fascicle models, two key operations when carrying out population studies. We found that Euclidean and log-Euclidean frameworks resulted in a decrease of fFA and fMD. More surprisingly, the values of microstructural descriptors depended on the number of subjects. The quaternion framework, in contrast, was the best at preserving microstructural features. 3567. 59 Non-parametric axon diameter distribution mapping with PGSE: reconstruction of uni- and multimodal distributions David Romascano, Jonathan Rafael-Patino, Muhamed Barakovic, Alessandro Daducci, Jean-Philippe Thiran, Tim Dyrby White matter diffusion MRI enables non-invasive estimation of the axon diameter distribution, which is of interest as it modulates communication speed and delays between brain regions, and changes during development and pathology. Distribution mapping is challenging: current methods simplify it by either estimating the mean diameter, imposing parametric distributions, or combining non-parametric approaches with Double Diffusion Encoding. We present a non-parametric framework based on a PGSE protocol. Simulations show robust reconstruction of unimodal and bimodal distributions. The method is sensitive to population specific changes within bimodal distributions, as long as the underlying populations are separated by a minimum distance. 3568. 60 Polynomial Meta-Model of Bloch-Torrey Equation for Track-based Regularization of Microstructural Inversion Noel Naughton, Arihant Jain, John Georgiadis A meta-model approach is presented which fits a multi-dimensional polynomial to numerical solutions of the Bloch-Torrey equation after being parameterized by microstructural and diffusion encoding parameters. This meta-model allows analytical representation of the solution space enabling analytical analysis of the space as well as reduced computational cost to solutions that do not need precise results. Possible uses of such a model are outlined, such as its use in allowing muscle tractography results to regularized by neighboring voxels in its defined muscle tract. 3569. 61 Improving Tractography in Baby Diffusion MRI via Asymmetric Spectrum Imaging Ye Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap, the UNC/UMN Baby Connectome Project Consortium Consortium Fiber tractography in baby diffusion MRI is challenging due to the low and spatially-varying diffusion anisotropy. In this abstract, we will introduce a method called asymmetric spectrum imaging (ASI) to improve estimation of white matter pathways in the baby brain by (1) incorporating an asymmetric fiber orientation model to help resolve subvoxel fiber configurations such as fanning and bending, and (2) explicitly modeling the spectrum of diffusion typical in the developing brain. 3570. 62 Diffusion of the perivascular space fluid is anisotropic in conventional DWI Farshid Sepehrband, Ryan Cabeen, Jeiran Choupan, Giuseppe Barisano, Meng Law, Arthur Toga Perivascular space (PVS), also known as Virchow-Robin space, is a pial-lined, fluid-filled structure that surrounds vessels in the cerebral cortex 1,2, and occupies a large portion of the cerebral tissue. PVS has an anisotropic morphology 3–5 and is mainly aligned with the white matter 6. However, its diffusion MRI profile has not been studied. Here we show experimental evidences that that PVS can be measured with diffusion MRI and the signature of this compartment is anisotropic. 3571. 63 Dependence of harmonic power on b-value for fiber ball imaging: comparison of theory and experiment Hunter Moss, Emilie McKinnon, Joseph Helpern, Jens Jensen Fiber ball imaging (FBI) is a recently proposed diffusion MRI (dMRI) method for estimating fiber orientation density functions together with specific microstructural parameters in white matter. The theory underlying FBI predicts the b-value dependence for the dMRI harmonic power of any given degree as long as the b-value is sufficiently large. Good agreement between theory and experiment has been previously demonstrated for the zero-degree harmonic power. Here the predicted functional forms for higher degree harmonics are shown to also agree well with experimental measurements, providing additional support for the validity of FBI. 3572. 64 Diffusion time dependence in the mid-time regime: a simulation study using PGSE. Annelinde Buikema, Arjan Dekker, Jan Sijbers The purpose of this work is to study the effect of varying the diffusion time on the estimation of the parameters of the two-compartment diffusion tensor model in the mid-time regime. Simulation results show that the precision of the diffusion time-dependent compartmental parameter estimates increases when a variable echo time acquisition scheme is used. At low SNR, however, including diffusion time-dependence may lead to a high bias and variance compared to the more conventional non diffusion time-dependent model. 3573. 65 Biases of microstructure models in baby diffusion MRI Khoi Huynh, Tiantian Xu, Ye Wu, Geng Chen, Weili Lin, Dinggang Shen, Pew-Thian Yap, the UNC/UMN Baby Connectome Project Consortium In this abstract, we show that some commonly used microstructure models do not perform as aspected when dealing with baby diffusion MRI, resulting in biased measurements. We found that this is mainly due to the greater water content in the developing brain. We show that the recently introduced Spherical Mean Spectrum Imaging (SMSI) [4] gives stable measurements in the presence of free water, making it well-suited for baby diffusion MRI. 3574. 66 MicroQIT - A Computational Framework for Population Microstructure Imaging Ryan Cabeen, Arthur Toga Microstructure imaging provides a quantitative tool for characterizing neural tissue with diffusion MRI and expanding our understanding of how the brain changes in health and disease. However, robust computational tools are needed for population imaging studies, so we developed a computational framework (MicroQIT) using the Quantitative Imaging Toolkit to meet this need by providing regional summaries and spatially normalized microstructure parameter maps. It supports a variety of ways to extract microstructure information from multi-shell diffusion MRI, leverages grid computing environments, and is available for use by the research community for future studies. 3575. 67 An in vivo investigation on quantitative metrics of diffusion kurtosis tensor: the effect of diffusion gradient parameters in the clinical setting Kuan-Hung Cho, Richard Buschbeck, Shih-Yen Lin, Ezequiel Farrher, Ming-Jye Chen, Chia-Wen Chiang, N. Jon Shah, Chang-Hoon Choi, Li-Wei Kuo Diffusion kurtosis imaging (DKI) is an emerging technique that provides additional information to delineate tissue microstructures by quantifying the non-Gaussian water molecular diffusion. Although the capability of DKI has been demonstrated, the effects of diffusion gradient parameters on its quantitative metrics, particularly in the clinical setting, have not been fully understood yet. This study aims to investigate the effect of diffusion gradient parameters on diffusion kurtosis tensor calculation and its quantitative metrics. In vivo results show that diffusion gradient duration has incremental influence on DKI quantitative metrics in the clinical setting. Further investigation with more subjects would help to statistically solidify our findings. 3576. 68 Test-retest reliability of 3-tissue constrained spherical deconvolution of diffusion MRI data by analysis of three separate cohorts Benjamin Newman, Thijs Dhollander, Kristen Reynier, Matthew Panzer, Thomas Druzgal It has previously been demonstrated that, by using 3-tissue constrained spherical deconvolution, separate compartments encompassing cerebrospinal fluid-like, white matter-like, and grey matter-like, signal fractions can be derived from diffusion MRI data. This study explores the reliability of these compartments in three test-retest cohorts with a variety of timescales and scanning parameters. Whole-brain average signal fractions show excellent reliability across all datasets, particularly in determining the CSF-like signal fraction. This suggests that variations in whole brain signal fraction measurements are likely to be attributable to experimental manipulation or pathology and not variation introduced by performing the analysis. 3577. 69 Powering Up Microstructural Imaging:  assessing cross-metric and cross-tract statistical power on an ultra-strong gradient MRI system Kristin Koller, Suryanarayana Umesh Rudapandra, Maxime Chamberland, Erika Raven, Greg Parker, Chantal Tax, Mark Drakesmith, John Evans, Tobias Wood, Derek Jones We present cross-metric and cross-tract assessment of test-retest repeatability for microstructure measures on an ultra strong gradient MRI scanner (CONNECTOM 3T) in the human brain. We show that several MRI metrics of tissue microstructure are reliable and present relative sample sizes required to provide sufficient statistical power across different white matter pathways and microstructure metrics. 3578. 70 Improving reproducibility of diffusion connectome analysis using deep convolutional neural network model Min-Hee Lee, Nolan O'Hara, Jeong-Won Jeong Reproducibility of diffusion-weighted structural connectomes is highly dependent on acquisition and tractography model, limiting the interpretation of connectomes acquired in the clinical setting. This study proposes a novel deep convolutional neural network (DCNN) to improve the reproducibility of structural connectomes, by which highly reproducible streamlines can be identified via an end-to-end deep learning of reference streamline coordinates in Human Connectome Project diffusion data. Preliminary results demonstrate that the proposed DCNN prediction model can improve the reproducibility of clinical connectomes (31.29% of F-statistics in intraclass correlation coefficient) and effectively remove noisy streamlines based on based on their poor prediction probabilities. 3579. 71 Resolving crossing fibers with the inclusion of intra-axonal diffusion modelingPresentation Not Submitted Chunyu Song, Peng Sun, Zezhong Ye, Sheng-Kwei (Victor) Song A new diffusion histology imaging (DHI) model is proposed to model crossing fibers considering both intra- and extra-axonal water diffusion, along with extra-axonal isotropic diffusion within an image voxel. Both Monte-Carlo simulation and in vivo MRI data from one healthy volunteer brain were examined to assess whether DHI can resolve crossing fibers while quantifying axonal injury, demyelination, and inflammation. 3580. 72 A diffusion MRI pipeline leveraging Nextflow & Singularity: Robust, Efficient, Reproducible in time! Guillaume Theaud, Jean-Christophe Houde, Felix Morency, Maxime Descoteaux How do we assure a diffusion MRI processing pipeline that is: i) deterministic, i.e. given two runs of the pipeline on the same data, the same output is returned, ii) reproducible in time, and iii) efficient? Diffusion MRI has several processing steps that may not be reproducible between multiple runs. This reproducibility varies because of the parameters, multi-threading and the versions of the tools used. Moreoever, processing time for a large database can take several hours when each step are ran sequentially. To solve these problems, we developed a reproducible and efficient diffusion MRI pipeline based on Nextflow and Singularity. 3581 73 Diffusion Kurtosis Imaging of the Brain with Free Water EliminationVideo Permission Withheld Teddy Salan, Sulaiman Sheriff, Varan Govind In diffusion weighted imaging (DWI) of the brain, a single voxel may contain gray matter (GM), white matter (WM), as well as free water embedded in GM, WM, and cerebrospinal fluid (CSF), each with different diffusion profiles. Free water elimination (FWE) is a method used to separate the free water components from tissue water. In this work, we extend FWE by fitting with a diffusion kurtosis imaging (DKI) model as it is a better descriptor of the non-Gaussian diffusion that occurs in tissue water. 3582. 74 A Graph Theory and Spectral Graph Theory Approach to Correlating Tractography with Parkinson’s Disease. Daniel DeYoung, Manish Amin, Catherine Price, Thomas Mareci Diffusion imaging is a potentially powerful tool for analyzing the effect of Parkinson’s disease on the brain. DWI analysis may provide some insight into how Parkinson’s disease may affect low executive or low memory functioning in the brain on a structural level. This research aims to analyze the complexity of the brain connectivity using graph theory and spectral graph theory to correlate structural differences between the brains of Parkinson’s patients with low memory functioning or low executive functioning, and controls.
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Diffusion in Disease
Digital Poster
Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15

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Microstructure Modeling: 2
Digital Poster
Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15

 Computer # 3607. 101 Resting-state diffusion fMRI bears strong resemblance and only subtle differences to BOLD fMRI Ileana Jelescu Diffusion fMRI (dfMRI) is a presumably non-BOLD technique sensitive to transient microstructural changes underlying neural activity. Previous task-fMRI studies have assessed the characteristics of the dfMRI signal and potential BOLD contamination, with conflicting results.  Here we acquired resting-state fMRI data with five protocols with incrementally reduced BOLD contributions and analyzed the characteristics of resting-state networks and functional connectivity using model-free approaches. We report dfMRI data does not contain fundamentally different information to BOLD-fMRI, with the exception of a few regions that switch from anti- to positively-correlated. Future work will focus on removing any remaining BOLD contribution from the dfMRI acquisition. 3608. 102 Diffusion Kurtosis Image in assessment of 3D Cell Culture Jui-Heng Lin, Huei-Chun Hsiao, Shao-Chieh Lin, Yi-Jui Liu, Ruey-Hwang Chou, Ke-Sin Yan, Tan-Wei Liao, Chao-Chun Lin, Chia-Wei Lin, Wu-Chung Shen A non-Gaussian kurtosis model has been shown to take into account tissue heterogeneity and two relative imaging biomarkers namely, the kurtosis coefficient and the corrected diffusion coefficient can be quantified. In this study, 3D cell culture with hydrogels ECM was used to investigate whether DKI may provide information on these microenvironmental parameters and the microenvironment-associated metastatic propensity of tumors. Our results demonstrated DKI-MRI may provide the potential biomarkers on the change of microenvironment in the application of 3D cell culture experiment. 3609. 103 Does cerebrospinal fluid pulsation affect DWI thermometry?: healthy volunteer study Koji Sakai, Jun Tazoe, Hiroyasu Ikeno, Kentaro Akazawa, Masashi Yasuike, Toshiaki Nakagawa, Bárbara Abecassis, Kei Yamada Diffusion-weighted imaging (DWI) based thermometry has a potential to be a non-invasive method of temperature measurement for the deep inside of human brain. Nevertheless, the DWI at lateral ventricle in the brain might be influenced by the pulsation flow of cerebrospinal fluid (CSF), which is motivated by heartbeat. The purpose of this study was to investigate the influence of pulsation flow on brain DWI thermometry for healthy subjects. Comparisons were performed among  ΔT at three CSF speed selections (slow vs. fast vs. random). There was no significant difference in ΔT among the CSF speed and volume on healthy subjects. 3610. 104 Abnormal cerebellar connectivity within the motor subnetwork in MSA with cerebellar dysfunction Apurva Shah, Shweta Prasad, Santosh Dash, Jitender Saini, Pramod Kumar Pal, Madhura Ingalhalikar Multiple system atrophy with cerebellar features (MSA-C) is a distinct subtype of MSA characterized by predominant cerebellar symptoms. Neuroimaging studies have demonstrated cerebellar abnormalities; however, abnormality of structural connectivity of the motor subnetwork has not been studied and this study aims to investigate this aspect. We observed impairment in the structural segregation, integration and network resilience with significantly reduced nodal strength and connectivity in several cerebellar as well as non-cerebellar regions that correlate with UMSARS scores. Our findings provide definitive evidence of abnormalities that may be causally implicated in the motor features of cerebellar dysfunction and Parkinsonism observed in MSA-C. 3611. 105 Propagation of Bias in Moment-Matching Based Standard Model Parameter Estimation Jonas Olesen, Noam Shemesh, Sune Jespersen Moment-matching is one proposed approach for estimating Standard Model parameters which partly overcomes the issues of the model’s notoriously shallow fitting landscape. The method achieves robustness by matching the model’s moments to the cumulants of the data determined by diffusion kurtosis imaging which is stable and clinically feasible. However, estimates of cumulants generally suffer from bias due to the use of finite b-values. Here, it is demonstrated that this bias propagates to the model-parameter estimates resulting in substantial inaccuracy even for small b-values. 3612. 106 Random Walks in Stochastic Geometries Eric Baker, Brendan Moloney, Xin Li, Erin Gilbert, Charles Springer Monte Carlo random walk simulations of water molecule displacements in realistic cell ensembles are presented.  Within an ensemble, the cells have stochastic distributions of sizes, shapes, and interstitial spacings.  The probability of molecules permeating the cell membrane is varied.  The irreducible, fundamental system parameters are the cell density, ρ, the mean cell volume, , and the steady-state cellular water efflux rate constant, .  Even though the self-diffusion coefficient is that of pure water, non-Gaussian displacements are observed. 3613. 107 Characterising tissue heterogeneity in cerebral metastases using multi-shell multi-tissue constrained spherical deconvolution Maxime Chamberland, Najmus Iqbal, Suryanarayana Rudrapatna, Greg Parker, Chantal Tax, John Staffurth, James Powell, Richard Wise, Derek Jones Considerable attention has focused on characterizing brain tumours using diffusion tensor imaging, and only more recently using advanced modelling techniques. Building on the observation that metastatic tumors exhibit different signal intensities depending on their histological/cellular composition, we investigate how multi-shell multi-tissue constrained spherical deconvolution can characterise tissue heterogeneity within brain metastases. 3614. 108 Reproducibility of tractography based on fibre orientation distribution and anatomical constraints applied on paediatric diffusion MRI. Thanh Vân Phan, Thibo Billiet, Dirk Smeets, Maaike Vandermosten Tractography is known to be sensitive to technical variations but might also be sensitive to issues related to paediatric data, such as head motion. We assessed the reproducibility in paediatric data of the probabilistic tractography algorithm based on fibre orientation distribution and anatomical constraints that enables to deal with crossing fibres and to reconstruct tracts with more anatomical accuracy. Our results showed that the reproducibility of this approach when it is applied on paediatric data is negatively affected by younger age and by head motion but can still achieve good reproducibility for selected tracts. 3615. 109 Cardiac Fiber Mobility During Contraction Using High Resolution Diffusion Tensor MRI Kevin Moulin, Ilya Verzhbinsky, Tyler Cork, Nyasha Maforo, Luigi Perotti, Daniel Ennis The main cardiomyocyte aggregate orientation, represented by the Helix Angle, remains poorly described in vivo during contractionas it is affected by the imaging resolution and thus by a change of wall thickening. This work evaluated the effect of the imaging resolution using a numerical phantom and ex vivoscans on porcine hearts. High-resolution cDTI was acquired in vivoto measure the transmural mobility of Helix Angle at three cardiac phases. A strong steepening of Helix Angle was observed as the resolution decreased in simulation and ex-vivo. A significant change in the Helix Angle distribution was observed during contraction. 3616. 110 Metabolite diffusion weighted imaging with golden angle radial echo planar spectroscopic imaging Vincent Boer, Itamar Ronen, Jan Pedersen, Esben Petersen, Henrik Lundell Diffusion weighted spectroscopy (DWS) is a promising tool for investigating compartment specific microstructure in heterogeneous tissues. Unlike water abundant in all cellular spaces, the mobility of metabolites provides a window into the microstructure of specific cell types. Multi-shot sequences for diffusion spectroscopic imaging suffer from translation induced phase fluctuations. This has previously been addressed with additional phase navigators. In this work we propose self navigated metabolite diffusion weighted spectroscopic imaging using golden angle radial echo planar gradient readouts with semi-LASER voxel localization. Initial data shows good spatial localization and spectral quality. 3617. 111 Needle in a Haystack: Finding connections of interest in the precentral gyrus from diffusion MRI based connectomics Jasmeen Sidhu, Francois Rheault, Maxime Descoteaux DMRI tractography and connectomics aim to create comprehensive maps of all neural circuitry in the brain, however, dMRI is a false-positive prone imaging modality. Yet, as it is the only non-invasive and in-vivo method to indirectly study the white matter connectivity of the brain it is heavily utilized. DMRI-Connectomic analyses often end with a “big data” problem, as there are many possible connections. Moreover, this data is likely populated by false-positive connections. By utilizing data-driven criteria to filter matrices this work attempts to narrow the “search window” by adding criteria on which we can discard “connections” or consider them potential candidates for further investigation. Such judgements may also lead to connections being evaluated as true or false-positives. 3618. 112 Characterization of white matter asymmetries in the healthy human brain using diffusion MRI fixel-based analysis Arush Honnedevasthana Arun, Robert Smith, Alan Connelly, Fernando Calamante The diffusion tensor model has been used extensively to study asymmetry in various regions of the human brain white matter. However, given the limitations of the tensor model, the nature of any underlying asymmetries remain uncertain, particularly in crossing fibre regions. Here, we applied fixel-based analysis (FBA) to state-of-the-art diffusion MRI data to assess white matter asymmetry of the healthy brain, demonstrating significant left/right asymmetries in various regions. The major advantage of this study is that FBA performs fibre-tract-specific modeling, and thus provides a method to assess the white matter asymmetry in a physically interpretable way. 3619. 113 Toward prognostic sensitivity of DTI to cognitive outcomes following physical exercise interventions. Anna Egbert, Ryan Falck, John Best, Teresa Liu-Ambrose Diffusion Tensor Imaging (DTI) is widely implemented in clinical research, yet, its prognostic value in brain and cognitive health remains uncertain. Prospective estimation of the effectiveness of interventions, such as physical exercise, can increase cost-effectiveness of treatment, thus, maximizing the impact of accessible modifiable preventive factors in improving health outcomes in the general population. We examined DTI in relationship to cognitive outcomes of physical activity intervention. This is the first study to show that white matter integrity related to sleep efficiency can be an early predictor of the cognitive outcomes of physical exercise intervention. 3620. 114 Assessment of Microstructural Changes Induced via Repeated Videogame Training as a Measure of Neuroplasticity in Normal Developing, College-age Brains Austin Patrick, Douglas Dean, Thomas Gorman, C. Shawn Green, Andrew Alexander In this study, diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were used to assess brain changes in white matter regions as a result of repeated videogame training. In a cohort playing a simulated race car game, we observe evidence of microstructural changes in tracts associated with working and long-term memory. Subjects playing a guitar simulation game, experienced changes in regions associated with attention, visuomotor learning, and proprioception of the limbs. This study demonstrates that diffusion MRI is promising for characterizing subtle brain changes associated with learning. 3621. 115 Finding the Surrogate Biomarkers of Glioma Infiltration in the Normal Appearing White Matter adjacent to Tumor using Neurite Orientation Dispersion and Density Imaging (NODDI)Presentation Not Submitted Yi-Cen Ting, Chou-Ming Cheng, Tzu-Chen Yeh This work addresses the study in glioma, where Diffusion Tensor Imaging (DTI) provides incoherent information while Neurite orientation dispersion and density imaging (NODDI) may improve the microstructural characterization. Glioma recurrences are mostly located at the margin of the resection cavity as peritumoral area. NODDI, using a compartment-based biophysical model, would potentially provide specific surrogate biomarkers for the microscopic foci of glioma infiltration better than DTI. NODDI reveals that glioma cells can extend beyond the bulk tumor and peritumoral edema into normal appearing white matter (NAWM) adjacent to tumor or peritumoral edema by using Region of Interest (ROI)-based statistics. 3622. 116 Assessing inter-vender reproducibility of diffusion tensor imaging of the spinal cord for multi-center imaging studies Mitsuhiro Kitamura, Satoshi Maki, Takeo Furuya, Takuya Miyamoto, Yasuhiro Shiga, Kazuhide Inage, Sumihisa Orita, Koji Matsumoto, Shingo Terakado, Yoshitada Masuda, Seiji Ohtori  In this study, we assessed the inter-vendor precision of diffusion tensor imaging (DTI) metrics using consensus acquisition protocol across 3T scanners from 3 main vendor at the two sites. The data were acquired from the traveling human healthy volunteer. DTI data and anatomical images were also acquired and imaging data were processed using the Spinal Cord Toolbox (SCT). Relatively higher inter vender reproducibility of fractional anisotropy (FA) in and cross-sectional area were demonstrated and it is considered feasible to conduct multicenter DTI studies of the spinal cord using FA value as biomarker and SCT as postprocessing tool. 3623. 117 Performance of Intravoxel incoherent motion (IVIM) imaging in the curative effect evaluation of diabetic nephropathy - A preliminary studyPresentation Not Submitted Jing Chen, Xirong Zhang, Nan Yu, Qi Yang, Yong Yu, Robert Grimm, Shaoyu Wang Diabetes has nowadays become one of the major public health problems. Early detection and intervention is crucial to delay the progress of Diabetic kidney disease. In this study, IVIM imaging was used to evaluate the renal-cortex- and medulla-related parameter changes before and after the treatment of diabetic nephropathy. It was found that after treatment, D, f, D *and ADC values were increased, indicating the improved microvascular perfusion. 3624. 118 Diffusion MRI characterization of Proton-therapy-induced brain tissue changes: a case study Lisa Novello, Nivedita Agarwal, Stefano Lorentini, Sabina Vennarini, Domenico Zacà, Ofer Pasternak, Jorge Jovicich Radiotherapy-induced neurotoxicity may be life threatening and its characterization is crucial for cancer treatment management, especially in early treatment phases. Diffusion MRI allows to assess non-invasively microstructural changes occurring with radiation treatment, which are of interest in relation to local absorbed dose and cognitive changes. Proton therapy (PT) offers the promise of more focal tumor damage relative to conventional radiotherapy. However, few studies have evaluated how it affects brain microstructure. As part of an ongoing longitudinal study, we present the first available data of 5 acquisitions along PT course showing a possible treatment-related “necrosis-like” effect  during treatment. 3625. 119 Diffusion-Relaxation Imaging with B-tensor Encoding using the Cumulant Expansion Carl-Fredrik Westin, Filip Szczepankiewicz, Lipeng Ning, Yogesh Rathi, Markus Nilsson Quantitative T2 and diffusion imaging provide important information about tissue microstructure. However, a joint knowledge of quantitative T2 and diffusion-derived measures can provide richer information about the microstructure that is not accessible when using these modalities independently. The standard approach for estimating the joint distributions of the T2-diffusion relies on the inverse Laplace transform. This transform is known to be unstable and difficult to invert.  In this work, we introduce an alternative approach based on cumulant expansion, and extend the recently proposed multidimensional diffusion MRI framework Q-space Trajectory Imaging" (QTI) to include T2-relaxation modeling. The cumulants of the expansion include estimates of mean diffusion and T2 relaxation, as well as their variance and covariance. We demonstrate the feasibility of this approach in a healthy human brain. 3626 120 Qualitative Comparison of Calculated and Measured Ultra-High b-Value Diffusion-Weighted Images in the Assessment of Clinically Significant Prostate Cancer.Video Permission Withheld David Bonekamp, Christopher Edler, Jan Philipp Radtke, Frederik Laun, Markus Hohenfellner, Heinz-Peter Schlemmer, Tristan Kuder To qualitatively compare measured and calculated ultra-high b-value (UHB, b-value up to 4000 s/mm2) DWI for detection of clinically significant prostate cancer. UHB-DWI was acquired in 55 patients at 3T in addition to standard DWI (S-DWI) extrapolated to UHB b-values. Two raters R1 and R2 independently assessed DWI and ADC in combination with T2w images. Lesion visibility was best on ultra-high b-value images monoexponentially extrapolated (ME-DWI) from S-DWI and equal on S- and UHB-ADC. The experienced rater was better able to adapt the improved lesion visibility on ME-DWI into the lesion detection task and utilized it for superior predictive performance. 3627. 121 Qualitative Diagnosis of Complex Ovarian Tumors Using Diffusion Weighted Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Correlation with MR Features and Pathology Shuping Weng, Ruqi Fang Aims: To evaluate the effectiveness of diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging in qualitative diagnosis of complex ovarian tumors. Methods: Semi-quantitative parameters of maximal slope of time-signal intensity curve and apparent diffusion coefficient measurements of diffusion-weighted imaging obtained from the tumor solid regions in 65 cases confirmed complex ovarian tumors were done. Results: A cut-off value of 1.61 × 10-3mm2 /s for ADC was used in differentiating invasive from borderline tumors.Optimal threshold value for maximal slope was 4.58% per second identifying benign and malignant tumors. Conclusion: Maximal slope value of time-signal intensity curve is the best index in distinguishing malignant from benign ovarian complex tumors,and ADC value of the solid component is the best index in differentiating invasive from borderline ovarian complex tumors. 3628. 122 Directional Intravoxel Incoherent Motion (IVIM) MR in Rodent Brain MinJung Jang, SeokHa Jin, MungSoo Kang, SoHyun Han, HyungJoon Cho The measurement of white matter flow, which is directional along the neuronal fibers, is challenging due to inherent limitations of low sensitivity and signal-to-noise ratio with arterial spin labeling(ASL)- and dynamic susceptibility contrast(DSC)-MRI acquisitions mainly due to low blood volumes. Recently, flow sensitive intravoxel incoherent motion(IVIM)-diffusion MR has been recognized as being particularly sensitive to directional flows. However, the multi-variable fittings usually suffer from over-fitting and thresholding issues. This study investigated the feasibility of constrained non-negative matrix factorization(cNMF) implementation to IVIM-MR for the automatic segmentation of directional IVIM signals for the purpose of robust detection of directional white matter flow. 3629. 123 Application of Spinal Cord White Matter Tract Integrity Quantification with Atlas-based Analysis in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder Masaaki Hori, Akifumi Hagiwara, Kazumasa Yokoyama, Kouhei Kamiya, Issei Fukunaga, Tomoko Maekawa, Koji Kamagata, Katsutoshi Murata, Shohei Fujita, Ryusuke Irie, Christina Andica, Kanako Kumamaru, Akihiko Wada, Julien Cohen-Adad, Shigeki Aoki We investigated both branches (Da ≤ De,? or Da > De,?) of Kurtosis-based white matter tract integrity to distinguish microstructural changes in the spinal cords of patients with MS and Neuromyelitis Optica. FA and AWF were significant higher and MD was significant lower in spinal cord white matter in MS (P=0.009, 0.024, 0.032, respectively). Di,axial, Di,radial and De,radial of spinal cord white matter in MS were significant lower (P=0.009, 0.024, 0.032, respectively,) in Branch 2(Da > De,?) in MS. There was no significant difference in Branch 1 ( Da ≤ De,?) metrics. 3630. 124 Delineation of thalamic substructures in ultra-high b-value DWI-measurement with reasonable acquisition time Nils Nuessle, Benjamin Bender, Uwe Klose The purpose of this study was to evaluate the capability of optimized and faster ultra-high b-value DWI in separating and identifying intrathalamic substructures compared to a previously described protocol. 7 subjects underwent MR-imaging, including T1 MPRAGE and two DWI sequences at 0 and 5000  s/mm2. DWI was performed with 64 directions and 5 averages (17:56 min) and with 6 directions and 25 averages (8:23 min). Intrathalamic substructures were semi-automatically delineate 4mm above AC/PC line. Accordance between the original sequence and the new speeded-up measurement was high. Acquisition time was reduced by more than 50 % with comparable results. 3631. 125 The value of Mean Apparent Propagator (MAP)-MRI in the diagnosis of hippocampal sclerosisPresentation Not Submitted Keran Ma, Jingliang Cheng, Xiaonan Zhang, Ankang Gao, Chengru Song, Shaoyu Wang, Xu Yan, Huiting Zhang This study aimed to explore the value of mean apparent propogation (MAP)-MRI parameters in the diagnosis of hippocampal sclerosis. By comparing the MAP-MRI parameters of hippocampus from both the hippocampal sclerosis patients and the healthy controls, the study found that the MAP-MRI parameters showed high consistency with pathological results, particularly for MSD and QIV. It suggests that MAP-MRI may be used as a diagnostic method with high sensitivity and specificity besides magnetic resonance spectroscopy(MRS) in the future.
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Diffusion: Validation
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Diffusion

Wednesday, 15 May 2019
 Exhibition Hall 09:15 - 10:15