scholarly journals Multi Tissue Modelling of Diffusion MRI Signal Reveals Volume Fraction Bias

2020 ◽  
Author(s):  
Matteo Frigo ◽  
Rutger H. J. Fick ◽  
Mauro Zucchelli ◽  
Samuel Deslauriers-Gauthier ◽  
Rachid Deriche
Keyword(s):  
Diffusion Mri ◽  
Volume Fraction ◽  
Tissue Modelling

scholarly journals Multi-Tissue Multi-Compartment Models of Diffusion MRI

2021 ◽  
Author(s):  
Matteo Frigo ◽  
Rutger H.J. Fick ◽  
Mauro Zucchelli ◽  
Samuel Deslauriers-Gauthier ◽  
Rachid Deriche
Keyword(s):  
Human Brain ◽  
Diffusion Mri ◽  
Volume Fraction ◽  
State Of The Art ◽  
Compartment Models ◽  
New Interpretation ◽  
Tissue Modelling ◽  
Python Package

AbstractState-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

Characterizing glioma microenvironment with ultra-high gradient diffusion MRI.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 2050-2050
Author(s):  
Ina Ly ◽  
Barbara Wichtmann ◽  
Susie Yi Huang ◽  
Aapo Nummenmaa ◽  
Ovidiu Andronesi ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Diffusion Mri ◽  
Volume Fraction ◽  
Response To Treatment ◽  
Imaging Biomarker ◽  
Normal Brain ◽  
Post Contrast ◽  
Major Barrier ◽  
High Gradient ◽  
Gradient Diffusion

2050 Background: The infiltrating nature of gliomas, particularly into the peritumoral area, is a major barrier to improving clinical outcome as microscopic disease remains even after apparent gross total resection. Conventional T1 post-contrast and T2/FLAIR MRI do not capture full tumor extent. A better imaging biomarker is needed to improve differentiation between tumor, peritumoral area and normal brain. Methods: 4 pre-surgical patients with non-enhancing, FLAIR-hyperintense lesions suspicious for glioma underwent ultra-high gradient diffusion MRI on the Connectome MRI scanner, a unique scanner with maximum gradient strength of 300 mT/m enabling mapping of cellular microstructures on a micron-level scale. The FLAIR area was defined as the tumor region of interest (ROI). Radiographically normal appearing brain up to 1 cm around the FLAIR area was defined as the peritumoral ROI. Using a novel 3 compartment diffusion model (Linear Multiscale Model), the volume fraction of water (VFW) was calculated within restricted (intracellular), hindered (extracellular) and free (CSF) spaces. VFW in the tumor, peritumoral ROI, contralateral normal white matter (WM) and cortex were compared. Results: Within the tumor ROI, the median VFW in the restricted compartment was decreased vs. the peritumoral ROI (↓ 34%), WM (↓ 46%) and cortex (↓ 18%) while median VFW in the hindered compartment was increased vs. the peritumoral ROI (↑ 26%), WM (↑ 54%) and cortex (↑ 25%). Within the peritumoral ROI, median VFW in the hindered compartment was increased compared to WM (↑ 23%). 3 patients had available histopathology revealing isocitrate dehydrogenase-mutant gliomas. Conclusions: Using ultra-high gradient diffusion MRI and a novel diffusion model, we detected distinct diffusion patterns in the tumor and peritumoral area not seen on conventional MRI. Lower VFW in the restricted compartment within the tumor may reflect decreased intracellular water mobility due to enlarged nuclei. Higher VFW in the hindered compartment in the tumor and peritumoral area may reflect higher degree of tissue permeability and edema. MRI-pathology and larger cohort validation studies are underway to elucidate microenvironment changes in response to treatment.

scholarly journals Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Daniel Johnson ◽  
Antonio Ricciardi ◽  
Wallace Brownlee ◽  
Baris Kanber ◽  
Ferran Prados ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Diffusion Mri ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Healthy Controls ◽  
Age And Gender ◽  
Spherical Mean ◽  
White Matter Microstructure ◽  
And Gender

Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings.Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics.Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients.Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42).Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.

scholarly journals Enabling complex fibre geometries using 3D printed axon-mimetic phantoms

2021 ◽  
Author(s):  
Tristan K. Kuehn ◽  
Farah N. Mushtaha ◽  
Ali R. Khan ◽  
Corey A. Baron
Keyword(s):  
Diffusion Mri ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Ground Truth ◽  
Diffusion Kurtosis Imaging ◽  
Strongly Correlated ◽  
3D Print ◽  
3D Printed ◽  
Stick Model

AbstractPurposeTo introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterize the performance of diffusion MRI models and representations in the presence of orientation dispersion.MethodsAn extension to an open source 3D printing package was created to 3D print a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A fiducial in each phantom was used to register a ground truth map of that phantom’s crossing angles and/or arc radius to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric.ResultsThe mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle, but increased with fibre curvature. Axial diffusivity (AD) decreased sharply with increasing crossing angle. DKI’s diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric, decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI’s intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was strongly correlated to crossing angle. Bingham-NODDI’s intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also strongly correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle.ConclusionsThis study demonstrates that inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. As a proof of concept, the dependence of several representations and models on fibre dispersion/crossing were investigated. As expected, Bingham-NODDI was best able to characterize planar fibre dispersion in the phantoms.

scholarly journals Validation of diffusion MRI estimates of compartment size and volume fraction in a biomimetic brain phantom using a human MRI scanner with 300 mT/m maximum gradient strength

NeuroImage ◽  
2018 ◽  
Vol 182 ◽  
pp. 469-478 ◽  
Author(s):  
Qiuyun Fan ◽  
Aapo Nummenmaa ◽  
Barbara Wichtmann ◽  
Thomas Witzel ◽  
Choukri Mekkaoui ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Volume Fraction ◽  
Maximum Gradient ◽  
Gradient Strength ◽  
Brain Phantom

Chemical partitioning of elements in Ni-base MC-carbide reinforced eutetic superalloys

1983 ◽  
Vol 41 ◽  
pp. 250-251
Author(s):  
E. F. Koch ◽  
E. L. Hall ◽  
S. W. Yang
Keyword(s):  
Alloy Composition ◽  
Volume Fraction ◽  
Lattice Mismatch ◽  
Turbine Blades ◽  
Eutectic Alloys ◽  
Alloy Matrix ◽  
X Ray ◽  
Gas Turbine Blades ◽  
Analytical Electron ◽  
Analytical Electron Microscope

The plane-front solidified eutectic alloys consisting of aligned tantalum monocarbide fibers in a nickel alloy matrix are currently under consideration for future aircraft and gas turbine blades. The MC fibers provide exceptional strength at high temperatures. In these alloys, the Ni matrix is strengthened by the precipitation of the coherent γ' phase (ordered L12 structure, nominally Ni3Al). The mechanical strength of these materials can be sensitively affected by overall alloy composition, and these strength variations can be due to several factors, including changes in solid solution strength of the γ matrix, changes in they γ' size or morphology, changes in the γ-γ' lattice mismatch or interfacial energy, or changes in the MC morphology, volume fraction, thermal stability, and stoichiometry. In order to differentiate between these various mechanisms, it is necessary to determine the partitioning of elemental additions between the γ,γ', and MC phases. This paper describes the results of such a study using energy dispersive X-ray spectroscopy in the analytical electron microscope.

Morphology, Distribution and Identification of Micro-Constituents Along Grain Boundaries in Nickel-Base Alloys

1973 ◽  
Vol 31 ◽  
pp. 176-177
Author(s):  
D. E. Fornwalt ◽  
A. R. Geary ◽  
B. H. Kear
Keyword(s):  
Electron Microscope ◽  
Grain Boundaries ◽  
Volume Fraction ◽  
Rupture Life ◽  
Stress Rupture ◽  
High Volume ◽  
Precipitate Morphology ◽  
Stress Rupture Life ◽  
Nickel Base ◽  
Scanning Electron

A systematic study has been made of the effects of various heat treatments on the microstructures of several experimental high volume fraction γ’ precipitation hardened nickel-base alloys, after doping with ∼2 w/o Hf so as to improve the stress rupture life and ductility. The most significant microstructural chan§e brought about by prolonged aging at temperatures in the range 1600°-1900°F was the decoration of grain boundaries with precipitate particles.Precipitation along the grain boundaries was first detected by optical microscopy, but it was necessary to use the scanning electron microscope to reveal the details of the precipitate morphology. Figure 1(a) shows the grain boundary precipitates in relief, after partial dissolution of the surrounding γ + γ’ matrix.

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

Formation of Persistent Slip Band in Fatigued Copper Single Crystals

1982 ◽  
Vol 40 ◽  
pp. 470-471
Author(s):  
N. Y. Jin
Keyword(s):  
Volume Fraction ◽  
Fatigue Cracks ◽  
Wall Structure ◽  
Matrix Structure ◽  
Dislocation Dipole ◽  
Slip Bands ◽  
Persistent Slip Bands ◽  
The Matrix ◽  
Hard Shell ◽  
Copper Single Crystals

Localised plastic deformation in Persistent Slip Bands(PSBs) is a characteristic feature of fatigue in many materials. The dislocation structure in the PSBs contains regularly spaced dislocation dipole walls occupying a volume fraction of around 10%. The remainder of the specimen, the inactive "matrix", contains dislocation veins at a volume fraction of 50% or more. Walls and veins are both separated by regions in which the dislocation density is lower by some orders of magnitude. Since the PSBs offer favorable sites for the initiation of fatigue cracks, the formation of the PSB wall structure is of great interest. Winter has proposed that PSBs form as the result of a transformation of the matrix structure to a regular wall structure, and that the instability occurs among the broad dipoles near the center of a vein rather than in the hard shell surounding the vein as argued by Kulmann-Wilsdorf.

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