257 research outputs found
Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders
Background and purpose:
Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI).//
Methods:
For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed.//
Results:
MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other.//
Conclusion:
This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders
Model-Informed Machine Learning for Multi-component T2 Relaxometry
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR)
signals is challenging but has high potential as it provides biomarkers
characterizing the tissue micro-structure, such as the myelin water fraction
(MWF). In this work, we propose to combine machine learning and aspects of
parametric (fitting from the MRI signal using biophysical models) and
non-parametric (model-free fitting of the T2 distribution from the signal)
approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron
(MLP) for the distribution reconstruction. For training our network, we
construct an extensive synthetic dataset derived from biophysical models in
order to constrain the outputs with \textit{a priori} knowledge of \textit{in
vivo} distributions. The proposed approach, called Model-Informed Machine
Learning (MIML), takes as input the MR signal and directly outputs the
associated T2 distribution. We evaluate MIML in comparison to non-parametric
and parametric approaches on synthetic data, an ex vivo scan, and
high-resolution scans of healthy subjects and a subject with Multiple
Sclerosis. In synthetic data, MIML provides more accurate and noise-robust
distributions. In real data, MWF maps derived from MIML exhibit the greatest
conformity to anatomical scans, have the highest correlation to a histological
map of myelin volume, and the best unambiguous lesion visualization and
localization, with superior contrast between lesions and normal appearing
tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than
non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020
Model-informed machine learning for multi-component T<sub>2</sub> relaxometry.
Recovering the T <sub>2</sub> distribution from multi-echo T <sub>2</sub> magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T <sub>2</sub> distribution from the signal) approaches to T <sub>2</sub> relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with a priori knowledge of in vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T <sub>2</sub> distribution. We evaluate MIML in comparison to a Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares algorithms (non-parametric) on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than the non-parametric and parametric methods, respectively
P2T2: a Physically-primed deep-neural-network approach for robust distribution estimation from quantitative -weighted MRI
Estimation of the T2 distribution from multi-echo T2-Weighted MRI (T2W) data
can provide insight into the microscopic content of tissue using macroscopic
imaging. This information can be used as a biomarker for several pathologies,
such as tumor characterization, osteoarthritis, and neurodegenerative diseases.
Recently, deep neural network (DNN) based methods were proposed for T2
distribution estimation from MRI data. However, these methods are highly
sensitive to distribution shifts such as variations in the echo-times (TE) used
during acquisition. Therefore, DNN-based methods cannot be utilized in
large-scale multi-institutional trials with heterogeneous acquisition
protocols. We present P2T2, a new physically-primed DNN approach for T2
distribution estimation that is robust to different acquisition parameters
while maintaining state-of-the-art estimation accuracy. Our P2T2 model encodes
the forward model of the signal decay by taking as input the TE acquisition
array, in addition to the MRI signal, and provides an estimate of the
corresponding T2 distribution as its output. Our P2T2 model has improved the
robustness against distribution shifts in the acquisition process by more than
50% compared to the previously proposed DNN model. When tested without any
distribution shifts, our model achieved about the same accuracy. Finally, when
applied to real human MRI data, our P2T2 model produced the most detailed
Myelin-Water fraction maps compared to both the MIML model and classical
approaches. Our proposed physically-primed approach improved the generalization
capacity of DNN models for T2 distribution estimation and their robustness
against distribution shifts compared to previous approaches without
compromising the accuracy
Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging
The g-ratio, quantifying the comparative thickness of the myelin sheath
encasing an axon, is a geometrical invariant that has high functional relevance
because of its importance in determining neuronal conduction velocity. Advances
in MRI data acquisition and signal modelling have put in vivo mapping of the
g-ratio, across the entire white matter, within our reach. This capacity would
greatly increase our knowledge of the nervous system: how it functions, and how
it is impacted by disease. This is the second review on the topic of g-ratio
mapping using MRI. As such, it summarizes the most recent developments in the
field, while also providing methodological background pertinent to aggregate
g-ratio weighted mapping, and discussing pitfalls associated with these
approaches. Using simulations based on recently published data, this review
demonstrates the relevance of the calibration step for three myelin-markers
(macromolecular tissue volume, myelin water fraction, and bound pool fraction).
It highlights the need to estimate both the slope and offset of the
relationship between these MRI-based markers and the true myelin volume
fraction if we are really to achieve the goal of precise, high sensitivity
g-ratio mapping in vivo. Other challenges discussed in this review further
evidence the need for gold standard measurements of human brain tissue from ex
vivo histology. We conclude that the quest to find the most appropriate MRI
biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of
many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience
Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest
Editor
Simultaneous Quantitative MRI Mapping of T1, T2* and Magnetic Susceptibility with Multi-Echo MP2RAGE.
The knowledge of relaxation times is essential for understanding the biophysical mechanisms underlying contrast in magnetic resonance imaging. Quantitative experiments, while offering major advantages in terms of reproducibility, may benefit from simultaneous acquisitions. In this work, we demonstrate the possibility of simultaneously recording relaxation-time and susceptibility maps with a prototype Multi-Echo (ME) Magnetization-Prepared 2 RApid Gradient Echoes (MP2RAGE) sequence. T1 maps can be obtained using the MP2RAGE sequence, which is relatively insensitive to inhomogeneities of the radio-frequency transmit field, [Formula: see text]. As an extension, multiple gradient echoes can be acquired in each of the MP2RAGE readout blocks, which permits the calculation of [Formula: see text] and susceptibility maps. We used computer simulations to explore the effects of the parameters on the precision and accuracy of the mapping. In vivo parameter maps up to 0.6 mm nominal resolution were acquired at 7 T in 19 healthy volunteers. Voxel-by-voxel correlations and the test-retest reproducibility were used to assess the reliability of the results. When using optimized paramenters, T1 maps obtained with ME-MP2RAGE and standard MP2RAGE showed excellent agreement for the whole range of values found in brain tissues. Simultaneously obtained [Formula: see text] and susceptibility maps were of comparable quality as Fast Low-Angle SHot (FLASH) results. The acquisition times were more favorable for the ME-MP2RAGE (≈ 19 min) sequence as opposed to the sum of MP2RAGE (≈ 12 min) and FLASH (≈ 10 min) acquisitions. Without relevant sacrifice in accuracy, precision or flexibility, the multi-echo version may yield advantages in terms of reduced acquisition time and intrinsic co-registration, provided that an appropriate optimization of the acquisition parameters is performed
Model-based multi-parameter mapping
Quantitative MR imaging is increasingly favoured for its richer information
content and standardised measures. However, computing quantitative parameter
maps, such as those encoding longitudinal relaxation rate (R1), apparent
transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat),
involves inverting a highly non-linear function. Many methods for deriving
parameter maps assume perfect measurements and do not consider how noise is
propagated through the estimation procedure, resulting in needlessly noisy
maps. Instead, we propose a probabilistic generative (forward) model of the
entire dataset, which is formulated and inverted to jointly recover (log)
parameter maps with a well-defined probabilistic interpretation (e.g., maximum
likelihood or maximum a posteriori). The second order optimisation we propose
for model fitting achieves rapid and stable convergence thanks to a novel
approximate Hessian. We demonstrate the utility of our flexible framework in
the context of recovering more accurate maps from data acquired using the
popular multi-parameter mapping protocol. We also show how to incorporate a
joint total variation prior to further decrease the noise in the maps, noting
that the probabilistic formulation allows the uncertainty on the recovered
parameter maps to be estimated. Our implementation uses a PyTorch backend and
benefits from GPU acceleration. It is available at
https://github.com/balbasty/nitorch.Comment: 20 pages, 6 figures, accepted at Medical Image Analysi
Myelin water imaging from multi-echo T-2 MR relaxometry data using a joint sparsity constraint
Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B-1(+)-T-2 dictionary.A single component analysis with this dictionary is used in an initial step to obtain a B-1(+) map. The T-2 distribution is then determined from a reduced dictionary corresponding to the estimated B-1(+) map using a combination of a non-negativity and a joint sparsity constraint.The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T-2 component is obtained. The joint sparsity constraint restricts the T-2 distribution to a small set of T-2 relaxation times shared between all voxels and reduces the noise sensitivity.The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multiecho gradient- and spin echo scan at 3 T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR = 250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.Neuro Imaging Researc
Advances in Quantitative MRI: Acquisition, Estimation, and Application
Quantitative magnetic resonance imaging (QMRI) produces images of potential MR biomarkers: measurable tissue properties related to physiological processes that characterize the onset and progression of specific disorders. Though QMRI has potential to be more diagnostic than conventional qualitative MRI, QMRI poses challenges beyond those of conventional MRI that limit its feasibility for routine clinical use. This thesis first seeks to address two of those challenges. It then applies these solutions to develop a new method for myelin water imaging, a challenging application that may be specifically indicative of certain white matter (WM) disorders.
One challenge that presently precludes widespread clinical adoption of QMRI involves long scan durations: to disentangle potential biomarkers from nuisance MR contrast mechanisms, QMRI typically requires more data than conventional MRI and thus longer scans. Even allowing for long scans, it has previously been unclear how to systematically tune the "knobs" of MR acquisitions to reliably enable precise biomarker estimation. Chapter 4 formalizes these challenges as a min-max optimal acquisition design problem and solves this problem to design three fast steady-state (SS) acquisitions for precise T1/T2 estimation, a popular QMRI application. The resulting optimized acquisition designs illustrate that acquisition design can enable new biomarker estimation techniques from established MR pulse sequences, a fact that subsequent chapters exploit.
Another QMRI challenge involves the typically nonlinear dependence of MR signal models on the underlying biomarkers: these nonlinearities cause conventional likelihood-based estimators to either scale very poorly with the number of unknowns or risk producing suboptimal estimates due to spurious local minima. Chapter 5 instead introduces a fast, general method for dictionary-free QMRI parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. Chapter 5 demonstrates PERK for T1/T2 estimation using one of the acquisitions optimized in Chapter 4. Simulations as well as single-slice phantom and in vivo experiments demonstrated that PERK and two well-suited maximum-likelihood (ML) estimators produce comparable T1/T2 estimates, but PERK is consistently at least 140x faster. Similar comparisons to an ML estimator in a more challenging problem (Chapter 6) suggest that this 140x acceleration factor will increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.
Chapter 6 applies ideas developed in previous chapters to design a new fast method for imaging myelin water content, a potential biomarker for healthy myelin. It first develops a two-compartment dual-echo steady-state (DESS) signal model and then uses a Bayesian variation of acquisition design (Chapter 4) to optimize a new DESS acquisition for precise myelin water imaging. The precision-optimized acquisition is as fast as conventional SS myelin water imaging acquisitions, but enables 2-3x better expected coefficients of variation in fast-relaxing fraction estimates. Simulations demonstrate that PERK (Chapter 5) and ML fast-relaxing fraction estimates from the proposed DESS acquisition exhibit comparable root mean-squared errors, but PERK is more than 500x faster. In vivo experiments are to our knowledge the first to demonstrate lateral WM myelin water content estimates from a fast (3m15s) SS acquisition that are similar to conventional estimates from a slower (32m4s) MESE acquisition.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147486/1/gnataraj_1.pd
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