Mauro Zucchelli

Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.

What lies beneath? Diffusion EAP-based study of brain tissue microstructure

Diffusion weighted magnetic resonance signals convey information about tissue microstructure and cytoarchitecture. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of numerical indices. Two main categories of reconstruction models can be identified in diffusion magnetic resonance imaging (DMRI): ensemble average propagator (EAP) models and compartmental models. From both, descriptors can be derived for elucidating the underlying microstructural architecture. While compartmental models indices directly quantify the fraction of different cell compartments in each voxel, EAP-derived indices are only a derivative measure and the effect of the different microstructural configurations on the indices is still unclear. In this paper, we analyze three EAP indices calculated using the 3D Simple Harmonic Oscillator based Reconstruction and Estimation (3D-SHORE) model and estimate their changes with respect to the principal microstructural configurations. We take advantage of the state of the art simulations to quantify the variations of the indices with the simulation parameters. Analysis of in-vivo data correlates the EAP indices with the microstructural parameters obtained from the Neurite Orientation Dispersion and Density Imaging (NODDI) model as a pseudo ground truth for brain data. Results show that the EAP derived indices convey information on the tissue microstructure and that their combined values directly reflect the configuration of the different compartments in each voxel.

A sensitivity analysis of q-space indices with respect to changes in axonal diameter, dispersion and tissue composition

In Diffusion MRI, q-space indices are scalar quantities that describe properties of the ensemble average propagator (EAP). Their values are often linked to the axonal diameter - assuming that the diffusion signal originates from inside an ensemble of parallel cylinders. However, histological studies show that these assumptions are incorrect, and axonal tissue is often dispersed with various tissue compositions. Direct interpretation of these q-space indices in terms of tissue change is therefore impossible, and we must treat them as scalars that only give non-specific contrast - just as DTI indices. In this work, we analyze the sensitivity of q-space indices to tissue structure changes by simulating axonal tissue with changing axonal diameter, dispersion and tissue compositions. Using human connectome project data, we then predict which indices are most sensitive to tissue changes in the brain. We show that, in both multi-shell and single-shell (DTI) data, q-space indices have higher sensitivity to tissue changes than DTI indices in large parts of the brain. Based on these results, it may be interesting to revisit older DTI studies using q-space indices as markers for pathology.

An evolutionary procedure for inferring MP systems regulation functions of biological networks

Metabolic P systems are a modeling framework for metabolic, regulatory and signaling processes. The key point of MP systems are flux regulation functions, which determine the evolution of a system from a given initial state. This paper presents important improvements to a technique, based on genetic algorithms and multiple linear regression, for inferring regulation functions that reproduce observed behaviors (time series datasets). An accurate analysis of three case studies, namely the mitotic oscillator in early amphibian embryos, the Lodka–Volterra predator-prey model and the chaotic logistic map show that this methodology can provide, from observed data, significant knowledge about the regulation mechanisms underlying biological processes.

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology.

A generalized SMT-based framework for Diusion MRI microstructural model estimation

Diffusion Magnetic Resonance Imaging (DMRI) has been widely used to characterize the principal directions of white matter fibers, also known as fiber Orientation Distribution Function (fODF), and axonal density in brain tissues.

Shore-based biomarkers allow patient versus control classification in stroke

In diffusion MRI, numerical biomarkers are usually calculated for research and clinical purposes as Generalized Fractional Anisotropy (GFA). Recently, more eloquent indices allowing a more accurate description of tissue microstructure were derived from the SHORE model. Under certain experimental conditions, such indices express the morphological properties of the compartments where spins diffuse. Evidence of the suitability of such indices as biomarkers for stroke was provided in a previous study based on diffusion spectrum imaging (DSI) and focusing on the cortical motor loop. The goal of this work was to investigate the suitability of such indices for stratification, namely for distinguishing pathological from healthy subjects. To this end, two different paths were followed. First, the same approach used in the previous work for longitudinal analysis (statistics-based) was applied to detect inter-group variations. Then, a new approach based on the LASSO regressor was proposed. Results provided evidence of the suitability of the proposed indices for stratification purposes.

The confinement tensor model improves characterization of diffusion-weighted magnetic resonance data with varied timing parameters

Diffusion imaging with confinement tensor (DICT) is a new model that employs a tensorial representation of the geometry confining the movements of water molecules. The model differs substantially from the commonly employed diffusion tensor imaging (DTI) technique even at small diffusion weightings when the dependence of the signal on the timing parameters of the pulse sequence is concerned. In this work, we assess the accuracy of the two models on a data set acquired from an excised monkey brain. The publicly available data set features differing values for diffusion pulse duration and separation. Our results indicate that the normalized mean squared error is reduced in an overwhelming portion of the voxels when the DICT model is employed, suggesting the superiority of DICT in characterizing the temporal dependence of the diffusion process in nervous tissue.

Towards an evolutionary procedure for reverse-engineering biological networks

Metabolic P systems are a modeling framework for metabolic, regulatory and signaling processes. The synthesis of flux regulation functions from time series of substance concentrations is a key task for reverse-engineering biological systems by MP systems. In this paper we present some important improvements to a technique based on genetic algorithms and multiple linear regression for the synthesis of regulation functions. An accurate analysis of generated functions, for the case study of the mitotic oscillator in early amphibian embryos, shows that some knowledge about the regulation mechanisms of biological processes can be inferred from experimental data using this methodology.

A genetic approach for synthesizing metabolic models from time series

In this paper we introduce a new approach, based on genetic algorithms and multiple linear regression, for the synthesis of flux regulation functions in metabolic models from observed time series. Genetic algorithms are used as a variable selection technique to identify the best primitive functions for flux regulation, and multiple linear regression is employed to compute primitive function coefficients. Our methodology is here successfully applied to synthesize a set of regulation functions able to regenerate an observed dynamics for the mitotic oscillator in early amphibian embryos.