Dataset Information

Probing lung microstructure with hyperpolarized noble gas diffusion MRI: theoretical models and experimental results.

ABSTRACT: The introduction of hyperpolarized gases ((3)He and (129)Xe) has opened the door to applications for which gaseous agents are uniquely suited-lung MRI. One of the pulmonary applications, diffusion MRI, relies on measuring Brownian motion of inhaled hyperpolarized gas atoms diffusing in lung airspaces. In this article we provide an overview of the theoretical ideas behind hyperpolarized gas diffusion MRI and the results obtained over the decade-long research. We describe a simple technique based on measuring gas apparent diffusion coefficient (ADC) and an advanced technique, in vivo lung morphometry, that quantifies lung microstructure both in terms of Weibel parameters (acinar airways radii and alveolar depth) and standard metrics (mean linear intercept, surface-to-volume ratio, and alveolar density) that are widely used by lung researchers but were previously available only from invasive lung biopsy. This technique has the ability to provide unique three-dimensional tomographic information on lung microstructure from a less than 15 s MRI scan with results that are in good agreement with direct histological measurements. These safe and sensitive diffusion measurements improve our understanding of lung structure and functioning in health and disease, providing a platform for monitoring the efficacy of therapeutic interventions in clinical trials.

SUBMITTER: Yablonskiy DA

PROVIDER: S-EPMC6366858 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Probing lung microstructure with hyperpolarized noble gas diffusion MRI: theoretical models and experimental results.

Yablonskiy Dmitriy A DA Sukstanskii Alexander L AL Quirk James D JD Woods Jason C JC Conradi Mark S MS

Magnetic resonance in medicine 20140201 2

The introduction of hyperpolarized gases ((3)He and (129)Xe) has opened the door to applications for which gaseous agents are uniquely suited-lung MRI. One of the pulmonary applications, diffusion MRI, relies on measuring Brownian motion of inhaled hyperpolarized gas atoms diffusing in lung airspaces. In this article we provide an overview of the theoretical ideas behind hyperpolarized gas diffusion MRI and the results obtained over the decade-long research. We describe a simple technique based ...[more]

PMID: 23554008

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Project description:Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results.

Project description:In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.

Dataset Information

Probing lung microstructure with hyperpolarized noble gas diffusion MRI: theoretical models and experimental results.

Publications

Probing lung microstructure with hyperpolarized noble gas diffusion MRI: theoretical models and experimental results.

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