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Journal of Mathematical Biology Nov 2023Malignant gliomas are notoriously invasive, a major impediment against their successful treatment. This invasive growth has motivated the use of predictive partial...
Malignant gliomas are notoriously invasive, a major impediment against their successful treatment. This invasive growth has motivated the use of predictive partial differential equation models, formulated at varying levels of detail, and including (i) "proliferation-infiltration" models, (ii) "go-or-grow" models, and (iii) anisotropic diffusion models. Often, these models use macroscopic observations of a diffuse tumour interface to motivate a phenomenological description of invasion, rather than performing a detailed and mechanistic modelling of glioma cell invasion processes. Here we close this gap. Based on experiments that support an important role played by long cellular protrusions, termed tumour microtubes, we formulate a new model for microtube-driven glioma invasion. In particular, we model a population of tumour cells that extend tissue-infiltrating microtubes. Mitosis leads to new nuclei that migrate along the microtubes and settle elsewhere. A combination of steady state analysis and numerical simulation is employed to show that the model can predict an expanding tumour, with travelling wave solutions led by microtube dynamics. A sequence of scaling arguments allows us reduce the detailed model into simpler formulations, including models falling into each of the general classes (i), (ii), and (iii) above. This analysis allows us to clearly identify the assumptions under which these various models can be a posteriori justified in the context of microtube-driven glioma invasion. Numerical simulations are used to compare the various model classes and we discuss their advantages and disadvantages.
Topics: Humans; Glioma; Anisotropy; Computer Simulation; Diffusion; Travel
PubMed: 38015257
DOI: 10.1007/s00285-023-02025-0 -
Magnetic Resonance in Medicine Aug 2021Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and...
PURPOSE
Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke.
METHODS
Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable.
RESULTS
Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values.
CONCLUSIONS
Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
Topics: Anisotropy; Brain Ischemia; Diffusion; Diffusion Magnetic Resonance Imaging; Humans; Ischemic Stroke; Stroke
PubMed: 33755261
DOI: 10.1002/mrm.28743 -
NeuroImage Nov 2021When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand...
Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow.
When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.
Topics: Anisotropy; Diffusion Tensor Imaging; Humans; Image Processing, Computer-Assisted; Reproducibility of Results; White Matter
PubMed: 34358660
DOI: 10.1016/j.neuroimage.2021.118451 -
The Neuroradiology Journal Feb 2020Diffusion tensor imaging is a magnetic resonance technique that provides information about the orientation and anisotropy of the white matter tracts. The aim of this...
PURPOSE
Diffusion tensor imaging is a magnetic resonance technique that provides information about the orientation and anisotropy of the white matter tracts. The aim of this study was to analyse diffusion tensor imaging quantitative parameters in idiopathic normal pressure hydrocephalus patients, in order to determine whether this method could correlate to clinical scores and cerebrospinal fluid flowmetry data.
METHODS AND MATERIALS
Fifteen consecutive patients with idiopathic normal pressure hydrocephalus and 15 age-matched controls underwent cerebrospinal fluid flowmetry and diffusion tensor imaging using a 1.5 Tesla system. Fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity values were calculated using region of interest atlas-based tract-mapping in nine cerebral areas and compared among the two groups. In addition, for idiopathic normal pressure hydrocephalus patients, diffusion tensor imaging parameters were correlated to clinical scores (mini mental state examination and frontal assessment battery) and cerebrospinal fluid flowmetry data.
RESULTS
Mean fractional anisotropy was significantly lower for the idiopathic normal pressure hydrocephalus group than for the control group in the forceps minor and motor cortex; the idiopathic normal pressure hydrocephalus group had significantly higher mean axial diffusivity for the genu of the corpus callosum and forceps minor. We did not find significant correlation between diffusion tensor imaging parameters and cerebrospinal fluid flowmetry and mini mental state examination, while we observed a correlation between forceps minor fractional anisotropy and frontal assessment battery; no correlation between flowmetry and clinical scores was found.
CONCLUSION
Our findings suggest that diffusion tensor imaging provides a non-invasive biomarker of white matter changes in idiopathic normal pressure hydrocephalus patients. Forceps minor is the best site to analyse. As diffusion tensor imaging offers a better correlation to clinical status than cerebrospinal fluid flowmetry, it should be included in the routine idiopathic normal pressure hydrocephalus protocol.
Topics: Aged; Aged, 80 and over; Anisotropy; Diffusion Tensor Imaging; Female; Humans; Hydrocephalus, Normal Pressure; Male; Middle Aged; Neuroimaging; Rheology
PubMed: 31771415
DOI: 10.1177/1971400919890098 -
NMR in Biomedicine Jan 2021Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate... (Comparative Study)
Comparative Study
Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q-sampling imaging (GQI), q-ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI-computed diffusivities and fractional anisotropy were significantly confounded by gel-mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel-mimicked edema when compared with the gold standard (r = 0.92, P = 1.9 × 10 , bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel-mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.
Topics: Animals; Anisotropy; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Edema; Female; Humans; Mice, Inbred C57BL; Models, Biological; Nerve Fibers; Phantoms, Imaging; Trigeminal Nerve; White Matter; Mice
PubMed: 33015890
DOI: 10.1002/nbm.4414 -
Frontiers in Endocrinology 2022To noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D...
To noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D diffusion-weighted spin-echo pulse sequence at 7.0 T. The collagen fiber alignment was revealed in each voxel and the complex 3D collagen network was visualized for the entire meniscus using tractography. The proposed automatic segmentation methods divided the whole meniscus to different zones (Red-Red, Red-White, and White-White) and different parts (anterior, body, and posterior). The diffusion tensor imaging (DTI) metrics were quantified based on the segmentation results. The heatmap was generated to investigate the connections among different regions of meniscus. Strong zonal-dependent diffusion properties were demonstrated by DTI metrics. The fractional anisotropy (FA) value increased from 0.13 (White-White zone) to 0.26 (Red-Red zone) and the radial diffusivity (RD) value changed from 1.0 × 10 mm/s (White-White zone) to 0.7 × 10 mm/s (Red-Red zone). Coexistence of both radial and circumferential collagen fibers in the meniscus was evident by diffusion tractography. Weak connections were found between White-White zone and Red-Red zone in each part of the meniscus. The anterior part and posterior part were less connected, while the body part showed high connections to both anterior part and posterior part. The tractography based on diffusion MRI may provide a complementary method to study the integrity of meniscus and nondestructively visualize the 3D collagen fiber architecture.
Topics: Animals; Anisotropy; Collagen; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Meniscus; Swine
PubMed: 35620393
DOI: 10.3389/fendo.2022.876784 -
Magma (New York, N.Y.) Oct 2019Several studies have demonstrated that anisotropic phantoms can be utilized for diffusion magnetic resonance imaging. The purpose of our study was to examine whether...
OBJECTIVE
Several studies have demonstrated that anisotropic phantoms can be utilized for diffusion magnetic resonance imaging. The purpose of our study was to examine whether wood is suitable as an anisotropic phantom material from the viewpoints of affordability and availability. In the current study, wood was used for restricted diffusion, and fibers were used for hindered diffusion.
MATERIALS AND METHODS
Wood and fiber phantoms were made. Diffusion kurtosis images were acquired with three magnetic resonance scanners. Fractional anisotropy, radial diffusivity, axial diffusivity, radial kurtosis and axial kurtosis values were measured. The wood phantom was imaged, and its durability was confirmed. The phantoms were imaged in varying orientations within the magnetic field. The wood was observed using an optical microscope.
RESULTS
Ten kinds of wood and the fiber had a diffusion metrics. The wood diffusion metrics suggested low variation over a period of 9 months. Changing the orientation of the phantoms within the magnetic field resulted in changes in diffusion metrics. Observation of wood vessels and fibers was conducted.
DISCUSSION
Wood and fibers have anisotropy and are promising as phantom materials. The development of anisotropic phantoms that anyone can use is useful for diffusion magnetic resonance imaging research and clinical applications.
Topics: Anisotropy; Artifacts; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; Materials Testing; Phantoms, Imaging; Wood
PubMed: 31144164
DOI: 10.1007/s10334-019-00761-3 -
Magnetic Resonance in Medicine Dec 2000Biexponential diffusion decay is demonstrated in the human brain in vivo using b factors up to 4000 sec mm(-2). Fitting of the signal decay data yields values for the... (Comparative Study)
Comparative Study
Biexponential diffusion decay is demonstrated in the human brain in vivo using b factors up to 4000 sec mm(-2). Fitting of the signal decay data yields values for the slow and fast diffusion components and volume fractions in agreement with previous studies in rat and human brain. In addition, differences in the fitted parameters are demonstrated in the white and gray matter and diffusion anisotropy is demonstrated in both the slow and fast diffusing components. Apparent anisotropy in the component fractions is discussed in terms of directionally dependent exchange rates between the compartments. The lack of a relationship between the estimated contribution to the signal of the fast and slow components and echo time appears to rule out T(2) differences in the observed water compartments. Values obtained for the fast diffusion coefficient, including differences between white and gray matter and the degree of anisotropy are compatible with the predictions of extracellular diffusion of water based on tortuosity models and the diffusion of tetramethylammonium ions in rat brain.
Topics: Anisotropy; Body Fluid Compartments; Body Water; Brain; Chi-Square Distribution; Diffusion; Humans; Magnetic Resonance Imaging; Reference Values; Time Factors
PubMed: 11108621
DOI: 10.1002/1522-2594(200012)44:6<852::aid-mrm5>3.0.co;2-a -
Magnetic Resonance in Medicine May 2019Microscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are...
PURPOSE
Microscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate µFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for µFA estimation. This study aims to evaluate µFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals.
METHODS
SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The µFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of µFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings.
RESULTS
µFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis).
CONCLUSION
Powder-averaged SMT and (2-component) SM are unable to accurately report µFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.
Topics: Algorithms; Animals; Anisotropy; Computer Simulation; Diffusion; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Gray Matter; Image Processing, Computer-Assisted; Mice; Models, Statistical; Normal Distribution; Powders; White Matter
PubMed: 30648753
DOI: 10.1002/mrm.27606 -
Journal of Parkinson's Disease 2022MRI is a valuable method to assist in the diagnostic work-up of Parkinson's disease (PD). The olfactory tract (OT) has been proposed as a potential MRI biomarker for...
BACKGROUND
MRI is a valuable method to assist in the diagnostic work-up of Parkinson's disease (PD). The olfactory tract (OT) has been proposed as a potential MRI biomarker for distinguishing PD patients from healthy controls.
OBJECTIVE
This study aims to further investigate whether diffusion measures of the OT differ between early stage PD patients and healthy controls.
METHODS
Twenty hyposmic/anosmic PD patients, 65 normosmic PD patients, and 36 normosmic healthy controls were evaluated and a 7T diffusion weighted image scan was acquired. Manual seed regions of interest were drawn in the OT region. Tractography of the OT was performed using a deterministic streamlines algorithm. Diffusion measures (fractional anisotropy and mean- radial- and axial diffusivity) of the generated streamlines were compared between groups.
RESULTS
Diffusion measures did not differ between PD patients compared to healthy controls and between hyposmic/anosmic PD patients, normosmic PD patients, and normosmic healthy controls. A positive correlation was found between age and mean- and axial diffusivity within the hyposmic/anosmic PD subgroup, but not in the normosmic groups. A positive correlation was found between MDS-UPDRSIII scores and fractional anisotropy.
CONCLUSION
This study showed that fiber tracking of the OT was feasible in both early stage PD and healthy controls using 7T diffusion weighted imaging data. However, 7T MRI diffusion measures of the OT are not useful as an early clinical biomarker for PD. Future work is needed to clarify the role of other OT measurements as a biomarker for PD and its different subgroups.
Topics: Anisotropy; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; Magnetic Resonance Imaging; Olfactory Bulb; Parkinson Disease
PubMed: 36093714
DOI: 10.3233/JPD-223349