ISMRM 24th Annual Meeting & Exhibition • 07-13 May 2016 • Singapore

Power Pitch Session: Diffusion at the Cutting Edge

Monday, May 9, 2016
Power Pitch Theatre, Exhibition Hall
10:45 - 12:45
Moderators: Robin Heidemann, Yi-Fen Yen

Click Here to view the Power Pitch introductory session

Note: The videos below are only the slides from each presentation.
They do not have audio.

    Plasma #

1 DWI^2: exploring the MRI-phase for imaging diffusion
Ralph Sinkus1, Simon Auguste Lambert1, Lucas Hadjilucas1, Shaihan Malik2, Anirban Biswas1, Francesco Padormo2, Jack Lee1, and Joseph V Hajnal2
1Imaging Sciences & Biomedical Engineering Division Kings College, King's College London, London, United Kingdom, 2Centre for the Developing Brain & Department Biomedical Engineering, King's College London, London, United Kingdom
Classical DWI methods extract information about microstructural tissue complexity from the signal decrease of the MR-magnitude as a function of b-value. Utilization of linear gradients for motion encoding prevents theoretically the use of the MR-phase. Rather, the diffusion information is encoded in the MR-magnitude via global spin dephasing due to Brownian motion with zero net phase shift. This dogma is overturned when considering quadratic gradient fields in space. We demonstrate in theory, experiment, and simulation that the diffusion process leads to a net phase shift with minimal loss in signal magnitude when imaging at the minimum of the quadratic gradient.

2 High resolution diffusion tensor reconstruction from simultaneous multi-slice acquisitions in a clinically feasible scan time
Gwendolyn Van Steenkiste1, Ben Jeurissen1, Steven Baete2,3, Arnold J den Dekker1,4, Dirk H.J. Poot5,6, Fernando Boada2,3, and Jan Sijbers1
1iMinds-Vision Lab, University of Antwerp, Antwerp, Belgium, 2Center for Advanced Imaging Innovation and Research (CAI2R), NYU School of Medicine, New York, NY, United States, 3Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States, 4Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands, 5Imaging Science and Technology, Delft University of Technology, Delft, Netherlands, 6Biomedical Imaging Group Rotterdam, Erasmus Medical Center Rotterdam, Rotterdam, Rotterdam, Netherlands
Achieving a high spatial resolution with DTI is challenging due to the inherent trade-off between resolution, acquisition time and signal-to-noise ratio (SNR). We propose a strategy to improve this trade-off by combining super-resolution DTI (SR-DTI) and simultaneous multi-slice (SMS) acquisition. With SMS-SR-DTI, high resolution DTI parameters can be recovered from thick slice images which have a high SNR. By acquiring the images with SMS, the overall acquisition time remains clinically feasible. As such, high resolution in vivo DTI becomes feasible in a clinical setting. This opens up exciting possibilities for diffusion MRI research.

3 Quantitative evaluation of eddy-current and motion correction techniques for diffusion-weighted MRI
Mark S Graham1, Ivana Drobnjak1, and Hui Zhang1
1Centre for Medical Image Computing & Department of Computer Science, UCL, London, United Kingdom
It is necessary to perform correction of eddy-current and motion artefacts before analysing DW-MR data, but none of the commonly used correction techniques have been evaluated quantitatively using direct measures of correspondence. Here we apply a recently proposed simulation framework to evaluate four correction techniques. We found the three techniques that register to a b=0 image (Eddy_correct, ACID, ExploreDTI) perform worse than a technique that registers to predicted DWIs (eddy).  Furthermore, we found that one of the most commonly used methods for registration to b=0, eddy_correct, performs significantly worse than the other methods considered.

4 A Mathematical Model and an Efficient Simulation Framework for Diffusion Cardiac Imaging: Application to Quantification of Cardiac Deformation on the Diffusion Signal
Imen Mekkaoui1, Kévin Moulin2,3, Jérôme Pousin1, and Magalie Viallon2,4
1ICJ, INSA-Lyon, Villeurbanne, France, 2Creatis, INSA-Lyon, Lyon, France, 3Siemens Healthcare, Saint-Denis, France, 4Department of Radiology, Universite´ J. Monnet, Saint Etienne, France
The diffusion process in the myocardium is difficult to investigate because of the unqualified sensitivity of diffusion measurements to  cardiac motion. We introduced a mathematical formalism to quantify the effect of tissue motion on the diffusion NMR signal. The presented model is based on the Bloch-Torrey equations and takes into account the cardiac deformation according to the laws  of continuum mechanics.  Approximating this model by using a finite element method, numerical simulations can predict the sensitivity of the signal to cardiac motion under the influence of  different preparation schemes. Our model  identified the existence of two time points of the cardiac cycle, called "sweet spots", on which the diffusion is unaffected by the cardiac deformation. This study also demonstrates that the sweet spots depend on the type of diffusion encoding schem.


5 Diffusion Kurtosis at varying diffusion times in the normal and injured mouse brains
Dan Wu1, Frances J Northington2, and Jiangyang Zhang1,3
1Radiology, Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 2Pediatrics, Johns Hopkins University School of Medicine, BALTIMORE, MD, United States, 3Radiology, New York University School of Medicine, New Yourk, NY, United States
To investigate the diffusion time dependence of diffusion kurtosis, we measured kurtosis at varying diffusion times using pulsed and oscillating gradients. The results showed reduced kurtosis as diffusion time decreased from 25 ms to 2.5 ms in the normal adult mouse brains, and the differences were higher in the gray matter than the white matter regions. Results from neonatal mice with severe hypoxic-ischemic injury showed that both kurtosis measurements at short and long diffusion times elevated in the edema region, and the changes were heterogeneous in the hippocampus, which may be correlated with long-term outcome. 

6 Can  the  Stretched  Exponential  Model  of  Gas  Diffusion  Provide Clinically -Relevant Parenchyma  Measurements of Lung Disease?
Alexei Ouriadov1, Eric Lessard1, David G McCormack2, and Grace Parraga1
1Robarts Research Institute, The University of Western Ontario, London, ON, Canada, 2Department of Medicine, The University of Western Ontario, London, ON, Canada
We hypothesized that using inhaled noble gas MRI diffusion-weighted imaging, the diffusion scale estimated using the stretched exponential model would be strongly related to MRI estimates of the mean linear intercept of the lung parenchyma.  In this proof-of-concept evaluation, we evaluated 34 never- and ex-smokers and compared parenchyma morphological estimates acquired using two different MRI approaches ad as well with CT and pulmonary function test measurements of acinar duct structure and function. This is important because in obstructive lung disease, the non-invasive measurement of parenchyma tissue destruction or maldevelopment may serve as a therapeutic target. 

7 Overestimation of CSF fraction in NODDI: possible correction techniques and the effect on neurite density and orientation dispersion measures
Samira Bouyagoub1, Nicholas G. Dowell1, Samuel A. Hurley2, Tobias C. Wood3, and Mara Cercignani1
1Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom, 2FMRIB Centre, University of Oxford, Oxford, United Kingdom, 3Neuroimaging, IoPPN, King’s College London, London, United Kingdom
NODDI is a diffusion MRI technique based on combining a 3 compartment tissue model with a (HARDI) protocol. NODDI overestimates CSF volume fractions (fiso), particularly in white matter regions. This is possibly due to the single T2 assumption for all compartments. High fiso could lead to inaccurate measure of neurite density (ficvf) and orientation dispersion (odi). We propose a method to correct these errors by scaling fiso with voxel-based T2 maps from DESPOT. We acquired NODDI data for 5 healthy subjects, and we run original NODDI analysis and another NODDI analysis using rescaled fiso. Results showed rescaling fiso generated low fiso measures consistent with those reported in literature. It also generated more physiologically acceptable measures of ficvf, whereas odi was not sensitive to the change.

8 Quantitative Assessment of Microstructure Properties of Human Corpus Callosum and Distinct Connectivity to Projected Cortices using Parametric T1 Imaging and Diffusion Tractography - Permission Withheld
Byeong-Yeul Lee1, Xiao-Hong Zhu1, and Wei Chen1
1Center for Magnetic Resonance Research, Radiology, University of Minnesota, Minneapolis, MN, United States
Imaging of callosal microstructures is of importance to understand its functional and anatomical connectivity to the projected cortical areas across two hemispheres. In this work, we tested our hypothesis that the parametric T1 measure could be sensitive to the corpus callosum (CC) microstructure and the fiber size within CC, and it may reflect the underlying functionality. In comparison with histology reports, our T1 maps indicate high inhomogeneity in CC and a positive trend between the T1 value and CC fiber size. In addition, diffusion tractograpy analysis shows that regional differentiation of CC T1 value or fiber size is indicative of unique connection to the cortical areas with distinct brain function. We found that the large callosal fibers likely connect to sensory and visual cortices; in contrast, small callosal fibers connect higher functional brain regions. The overall results show the new utility of parametric T1 imaging for quantitatively assessment of the fiber microstructure of human corpus callosum and its connections to functionally relevant cortices. This imaging approach could provide a robust and useful tool for detection of fiber abnormality in the human white matter and dysfunction.

9 Fibre directionality and information flow through the white matter: Preliminary results on the fusion of diffusion MRI and EEG
Samuel Deslauriers-Gauthier1, Jean-Marc Lina2, Russell Butler3, Kevin Whittingstall3, Pierre-Michel Bernier4, and Maxime Descoteaux1
1Computer Science department, Université de Sherbrooke, Sherbrooke, QC, Canada, 2École de Technologie Supérieure, Montréal, QC, Canada, 3Department of Diagnostic Radiology, Université de Sherbrooke, Sherbrooke, QC, Canada, 4Department of Kinanthropology, Université de Sherbrooke, Sherbrooke, QC, Canada
Diffusion MRI can recover white matter fibre bundles but it is blind to their directionality. We propose to identify the directionality of white matter fibre bundles by combining diffusion MRI and EEG data. Based on a realistic model of the brain and simulated EEG data, our preliminary results show that our proposed method is able to differentiate between afferent and efferent white matter connections.

10 Improved tractography by modelling sub-voxel fibre patterns using asymmetric fibre orientation distributions
Matteo Bastiani1, Michiel Cottaar1, Krikor Dikranian2, Aurobrata Ghosh3, Hui Zhang3, Daniel C. Alexander3, Timothy Behrens1, Saad Jbabdi1, and Stamatios N. Sotiropoulos1
1FMRIB Centre, University of Oxford, Oxford, United Kingdom, 2Department of Anatomy & Neurobiology, Washington University, St. Louis, MO, United States, 3Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom
Fiber bundles can cross or kiss, bend or fan within a single diffusion MRI (dMRI) voxel. Given the limited dMRI resolution and the inherent central symmetry in the measurements, these sub-voxel patterns cannot be distinguished by only using the voxel-wise signal. These asymmetric fibre patterns can be distinguished once information from neighbouring voxels is pooled together. We propose a direct estimation of asymmetric fiber orientation distributions (aFODs) based on neighbourhood-wise constrained spherical deconvolution that is capable of inferring sub-voxel patterns. We also propose a tractography algorithm based on the estimated aFODs and we assess performance using real histological fibre patterns.

11 Investigation of the influence of the extracellular matrix on water diffusion in brain and cartilage
Jakob Georgi1, Riccardo Metere1, Markus Morawski2, Carsten Jäger2, and Harald E. Möller1
1Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Paul-Flechsig-Institute for Brain Research, Leipzig, Germany
Water diffusivity in biological tissues can be related to the underlying microstructure that modulates the restricted or hindered diffusion, and can be studied with NMR experiments. The extracellular matrix, whose composition depends on the tissue type, may have an influence on diffusion. In this work we study the influence of the extracellular matrix on diffusion, by measuring brain and cartilage samples before and after the enzymatic removal of the extracellular matrix components. The activation energy for the self-diffusion of water seems to be not significantly affected by the treatment for brain tissues.

12 Measurement of the Effect of Tissue Fixation on Tumour Microstructure using VERDICT Diffusion-MRI
Ben Jordan1, Tom Roberts1, Angela D'Esposito1, John Connell1, Andrada Ianus2, Eleftheria Panagiotaki2, Daniel Alexander2, Mark Lythgoe1, and Simon Walker-Samuel1
1Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom, 2Centre for Medical Image Computing, University College London, London, United Kingdom
It has previously been shown that compartmental models of tissue diffusion such as VERDICT can enable access to useful measures of in-vivo tumour microstructure such as cell radius. However, comparing the in-vivo values with those measured from histology showed that a discrepancy exists between the two; histological values were consistently smaller. In this study, we assess the ability of VERDICT MRI to detect this change in cell radius by acquiring data (9.4T MRI) both in-vivo and post-fixation. A significant decrease in cell radius was detected post-fixation, which was supported by a decrease in the intra-cellular volume fraction.

13 Validation of Surface-to-Volume Ratio derived from Oscillating Gradient Spin Echo on a clinical scanner using anisotropic fiber phantoms
Gregory Lemberskiy1, Steven H. Baete1, Martijn A. Cloos1, Dmitry S. Novikov1, and Els Fieremans1
1Radiology, NYU School of Medicine, New York, NY, United States
This work represents the first measurement of S/V on a clinical scanner using OGSE on a well-characterized anisotropic fiber phantom. The S/V measurement is validated externally via non-diffusion metrics (Spin Echo and MR Fingerprinting). Lastly, a comparison of $$$D(\omega)$$$ and $$$D(t)$$$ shows that the effective diffusion time is $$$t_{\rm eff}^{\rm Mitra} = 9/64f = 9/16\cdot t_{\rm eff}$$$ rather than the commonly used $$$t_{\rm eff} = 1/4f$$$. 

14 Demonstration of a Sliding-Window Diffusion Tensor Technique for Temporal Study of Post-Exercise Skeletal Muscle Dynamics
Conrad P Rockel1,2 and Michael D Noseworthy1,2,3
1School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada, 2Imaging Research Centre, St Josephs Healthcare, Hamilton, ON, Canada, 3Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
A novel sliding-window DTI analysis strategy, aimed at achieving both temporal resolution and valid spatial representation, was tested on one human subject pre- and post-exercise (plantar flexion) across 4 sets of different intensity.  Temporal diffusion measures comprised of 3- and 15-directions  (ADC and MD/FA, respectively) were assessed, as well as signal intensity of accompanying T2-weighted images (S0).  Peroneus longus demonstrated increase in MD, ADC and S0, the peak and duration of which reflected exercise intensity.  FA appeared noisy, although demonstrated large decreases following higher intensity exercise.  While further work is needed, this method shows promise in measuring skeletal muscle dynamics.

15 Denoising Diffusion-Weighted Images Using x-q Space Non-Local Means
Geng Chen1,2, Yafeng Wu1, Dinggang Shen2, and Pew-Thian Yap2
1Data Processing Center, Northwestern Polytechnical University, Xi'an, China, People's Republic of, 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
In this abstract, we show that improved denoising performance can be attained by extending the non-local means (NLM) algorithm beyond the x-space (i.e., the spatial space) to include the q-space (i.e., the wave-vector space). The advantage afforded by this extension is twofold: (1) Non-local information can now be harnessed not only across space, but also across measurements in q-space; (2) In white matter regions with high curvature, q-space neighborhood matching corrects for such non-linearity so that information from structures oriented in different directions can be used more effectively for denoising without introducing artifacts.

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