122 research outputs found
Double diffusion encoding and applications for biomedical imaging
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important
contemporary non-invasive modalities for probing tissue structure at the
microscopic scale. The majority of dMRI techniques employ standard single
diffusion encoding (SDE) measurements, covering different sequence parameter
ranges depending on the complexity of the method. Although many signal
representations and biophysical models have been proposed for SDE data, they
are intrinsically limited by a lack of specificity. Advanced dMRI methods have
been proposed to provide additional microstructural information beyond what can
be inferred from SDE. These enhanced contrasts can play important roles in
characterizing biological tissues, for instance upon diseases (e.g.
neurodegenerative, cancer, stroke), aging, learning, and development.
In this review we focus on double diffusion encoding (DDE), which stands out
among other advanced acquisitions for its versatility, ability to probe more
specific diffusion correlations, and feasibility for preclinical and clinical
applications. Various DDE methodologies have been employed to probe compartment
sizes (Section 3), decouple the effects of microscopic diffusion anisotropy
from orientation dispersion (Section 4), probe displacement correlations, study
exchange, or suppress fast diffusing compartments (Section 6). DDE measurements
can also be used to improve the robustness of biophysical models (Section 5)
and study intra-cellular diffusion via magnetic resonance spectroscopy of
metabolites (Section 7). This review discusses all these topics as well as
important practical aspects related to the implementation and contrast in
preclinical and clinical settings (Section 9) and aims to provide the readers a
guide for deciding on the right DDE acquisition for their specific application
Reliability and Uncertainty in Diffusion MRI Modelling
Current Diffusion MRI studies often utilise more complex models beyond the single exponential decay model used in clinical standards. As this thesis shows, however, two of these models, biexponential and kurtosis, experience mathematical, ill-conditioning issues that can arise when used with regression algorithms, causing extreme bias and/or variance in the parameter estimates. Using simulated noisy data measurements from known truth, the magnitude of the bias and variance was shown to vary based on signal parameters as well as SNR, and increasing the SNR did not reduce this uncertainty for all data. Parameter estimate reliability could not be assessed from a single regression fit in all cases unless bootstrap resampling was performed, in which case measurements with high parameter estimate uncertainty were successfully identified. Prior to data analysis, current studies may use information criteria or cross-validation model selection methods to establish the best model to assess a specific tissue condition. While the best selection method to use is currently unclear in the literature, when testing simulated data in this thesis, no model selection method performed more reliably than the others and these methods were merely biased toward either simpler or more complex models. When a specific model was used to generate simulated noisy data, no model selection method selected this true model for all signals, and the ability of these methods to select the true model also varied depending on the true signal parameters. The results from these simulated data analyses were applied to ex vivo data from excised prostate tissue, and both information criteria measures and bootstrap sample distributions were able to identify image voxels whose parameter estimates had likely reliability issues. Removing these voxels from analysis improved sample variance of the parameter estimates
Placental Image Analysis using Coupled Diffusion-Weighted and Multi-Echo T2 MRI and a Multi-Compartment Model
Current popular methods of ow and uid measurement are
confounded by the interaction of relaxation and perfusion characteristics which are rarely simultaneously considered. To address this shortcoming we propose a new multi-compartment model for the tissue signal in MRI and apply this to placenta imaging data. Motivated by the different flow characteristics across the placenta, a three compartment model comprising fast and slowly circulating uid pools and a tissue pool is fitted to overlapping multi-echo T2 relaxometry and an intra-voxel incoherent motion diffusion acquisition with low b-values. The new model is supported by a modified image acquisition to enable successful model fitting, but this acquisition is clinically practical; we implemented the acquisition
on a standard 1.5T clinical system with acquisition taking less than 20 minutes with 26 slices. This is particularly important for placenta image acquisition. We test this combined acquisition and model-fitting routine on simulated data and show parametric maps for a placenta dataset
Doctor of Philosophy
dissertationMyocardial microstructure plays an important role in sustaining the orchestrated beating motion of the heart. Several microstructural components, including myocytes and auxiliary cells, extracellular space, and blood vessels provide the infrastructure for normal heart function, including excitation propagation, myocyte contraction, delivery of oxygen and nutrients, and removing byproduct wastes. Cardiac diseases cause deleterious changes to some or all of these microstructural components in the detrimental process of cardiac remodeling. Since heart failure is among the leading causes of death in the world, new and novel tools to noninvasively characterize heart microstructure are needed for monitoring and staging of cardiac disease. In this regards, diffusion magnetic resonance imaging (MRI) provides a promising framework to probe and quantify tissue microstructure without the need for exogenous contrast agent. As diffusion in 3-dimensional space is characterized by the diffusion tensor, MR diffusion tensor imaging (DTI) is being used to noninvasively measure anisotropic diffusion, and thus the magnitude and spatial orientation of microstructural organization of tissues, including the heart. However, even though in vivo cardiac DTI has become more clinically available, to date the origin and behavior of different microstructural components on the measured DTI signal remain to be explicitly specified. The presented studies in this work demonstrate that DTI can be used as a noninvasive and contrast-free imaging modality to characterize myocyte size and density, extracellular collagen content, and the directional magnitude of blood flow. The identified applications are expected to provide metrics to enable physicians to detect, quantify, and stage different microstructural components during progression of cardiac disease
Effects of nongaussian diffusion on "isotropic diffusion measurements'': an ex-vivo microimaging and simulation study
Designing novel diffusion-weighted pulse sequences to probe tissue
microstructure beyond the conventional Stejskal-Tanner family is currently of
broad interest. One such technique, multidimensional diffusion MRI, has been
recently proposed to afford model-free decomposition of diffusion signal
kurtosis into terms originating from either ensemble variance of isotropic
diffusivity or microscopic diffusion anisotropy. This ability rests on the
assumption that diffusion can be described as a sum of multiple Gaussian
compartments, but this is often not strictly fulfilled. The effects of
nongaussian diffusion on single shot isotropic diffusion sequences were first
considered in detail by de Swiet and Mitra in 1996. They showed theoretically
that anisotropic compartments lead to anisotropic time dependence of the
diffusion tensors, which causes the measured isotropic diffusivity to depend on
gradient frame orientation. Here we show how such deviations from the multiple
Gaussian compartments assumption conflates orientation dispersion with ensemble
variance in isotropic diffusivity. Second, we consider additional contributions
to the apparent variance in isotropic diffusivity arising due to
intracompartmental kurtosis. These will likewise depend on gradient frame
orientation. We illustrate the potential importance of these confounds with
analytical expressions, numerical simulations in simple model geometries, and
microimaging experiments in fixed spinal cord using isotropic diffusion
encoding waveforms with 7.5 ms duration and 3000 mT/m maximum amplitude.Comment: 26 pages, 9 figures. Appearing in J. Magn. Reso
Quantitative mapping of the per-axon diffusion coefficients in brain white matter.
This article presents a simple method for estimating the effective diffusion coefficients parallel and perpendicular to the axons unconfounded by the intravoxel fiber orientation distribution. We also call these parameters the per-axon or microscopic diffusion coefficients
The sensitivity of diffusion MRI to microstructural properties and experimental factors
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic
Diffusionâ weighted imaging outside the brain: Consensus statement from an ISMRMâ sponsored workshop
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/1/jmri25196_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134160/2/jmri25196.pd
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
The value of in vivo preclinical diffusion MRI (dMRI) is substantial.
Small-animal dMRI has been used for methodological development and validation,
characterizing the biological basis of diffusion phenomena, and comparative
anatomy. Many of the influential works in this field were first performed in
small animals or ex vivo samples. The steps from animal setup and monitoring,
to acquisition, analysis, and interpretation are complex, with many decisions
that may ultimately affect what questions can be answered using the data. This
work aims to serve as a reference, presenting selected recommendations and
guidelines from the diffusion community, on best practices for preclinical dMRI
of in vivo animals. In each section, we also highlight areas for which no
guidelines exist (and why), and where future work should focus. We first
describe the value that small animal imaging adds to the field of dMRI,
followed by general considerations and foundational knowledge that must be
considered when designing experiments. We briefly describe differences in
animal species and disease models and discuss how they are appropriate for
different studies. We then give guidelines for in vivo acquisition protocols,
including decisions on hardware, animal preparation, imaging sequences and data
processing, including pre-processing, model-fitting, and tractography. Finally,
we provide an online resource which lists publicly available preclinical dMRI
datasets and software packages, to promote responsible and reproducible
research. An overarching goal herein is to enhance the rigor and
reproducibility of small animal dMRI acquisitions and analyses, and thereby
advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl
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