Mustapha Bouhrara

Dynamic MRI and Thermal Simulation To Interpret Deformation and Water Transfer in Meat during Heating

Understanding and controlling structural and physical changes in meat during cooking is of prime importance. Nuclear magnetic resonance imaging (MRI) is a noninvasive, nondestructive tool that can be used to characterize certain properties and structures both locally and dynamically. Here we show the possibilities offered by MRI for the in situ dynamic imaging of the connective network during the cooking of meat to monitor deformations between 20 and 75 °C. A novel device was used to heat the sample in an MR imager. An MRI sequence was developed to contrast the connective tissue and the muscle fibers during heating. The temperature distribution in the sample was numerically simulated to link structural modifications and water transfer to temperature values. The contraction of myofibrillar and collagen networks was observed at 42 °C, and water began to migrate toward the interfascicular space at 40 °C. These observations are consistent with literature results obtained using destructive and/or nonlocalized methods. This new approach allows the simultaneous monitoring of local deformation and water transfer, changes in muscle structure and thermal history.

In situ imaging highlights local structural changes during heating: the case of meat.

Understanding and monitoring deformation and water content changes in meat during cooking is of prime importance. We show the possibilities offered by nuclear magnetic resonance imaging (MRI) for the in situ dynamic measurement of deformation fields and water content mapping during beef heating from 20 to 75 °C. MRIs were acquired during heating, and image registration was used to calculate the deformation field. The temperature distribution in the sample was simulated numerically to link structural modifications and water transfer to temperature values. During heating, proton density decreases because of a magnetic susceptibility drop with temperature and water expulsion due to muscle contraction. A positive relationship was found between local cumulative deformation and water content. This new approach makes it possible to identify the deformation field and water transfer simultaneously and to trace thermal history to build heuristic models linking these parameters.

Incorporation of Rician noise in the analysis of biexponential transverse relaxation in cartilage using a multiple gradient echo sequence at 3 and 7 Tesla.

Previous work has evaluated the quality of different analytic methods for extracting relaxation times from magnitude imaging data exhibiting Rician noise. However, biexponential analysis of relaxation in tissue, including cartilage, and materials is of increasing interest. We, therefore, analyzed biexponential transverse relaxation decay in the presence of Rician noise and assessed the accuracy and precision of several approaches to determining component fractions and apparent transverse relaxation times.

Stabilization of the inverse Laplace transform of multiexponential decay through introduction of a second dimension

We propose a new approach to stabilizing the inverse Laplace transform of a multiexponential decay signal, a classically ill-posed problem, in the context of nuclear magnetic resonance relaxometry. The method is based on extension to a second, indirectly detected, dimension, that is, use of the established framework of two-dimensional relaxometry, followed by projection onto the desired axis. Numerical results for signals comprised of discrete T1 and T2 relaxation components and experiments performed on agarose gel phantoms are presented. We find markedly improved accuracy, and stability with respect to noise, as well as insensitivity to regularization in quantifying underlying relaxation components through use of the two-dimensional as compared to the one-dimensional inverse Laplace transform. This improvement is demonstrated separately for two different inversion algorithms, non-negative least squares and non-linear least squares, to indicate the generalizability of this approach. These results may have wide applicability in approaches to the Fredholm integral equation of the first kind.

Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT.

Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in the human brain. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.

Analysis of mcDESPOT‐ and CPMG‐derived parameter estimates for two‐component nonexchanging systems

To compare the reliability and stability of the multicomponent‐driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and Carl‐Purcell‐Meiboom‐Gill (CPMG) approaches to parameter estimation.

Rapid Simultaneous High-resolution Mapping of Myelin Water Fraction and Relaxation Times in Human Brain using BMC-mcDESPOT.

ABSTRACT A number of central nervous system (CNS) diseases exhibit changes in myelin content and magnetic resonance longitudinal, T1, and transverse, T2, relaxation times, which therefore represent important biomarkers of CNS pathology. Among the methods applied for measurement of myelin water fraction (MWF) and relaxation times, the multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) approach is of particular interest. mcDESPOT permits whole brain mapping of multicomponent T1 and T2, with data acquisition accomplished within a clinically realistic acquisition time. Unfortunately, previous studies have indicated the limited performance of mcDESPOT in the setting of the modest signal‐to‐noise range of high‐resolution mapping, required for the depiction of small structures and to reduce partial volume effects. Recently, we showed that a new Bayesian Monte Carlo (BMC) analysis substantially improved determination of MWF from mcDESPOT imaging data. However, our previous study was limited in that it did not discuss determination of relaxation times. Here, we extend the BMC analysis to the simultaneous determination of whole‐brain MWF and relaxation times using the two‐component mcDESPOT signal model. Simulation analyses and in‐vivo human brain studies indicate the overall greater performance of this approach compared to the stochastic region contraction (SRC) algorithm, conventionally used to derive parameter estimates from mcDESPOT data. SRC estimates of the transverse relaxation time of the long T2 fraction, T2,l, and the longitudinal relaxation time of the short T1 fraction, T1,s, clustered towards the lower and upper parameter search space limits, respectively, indicating failure of the fitting procedure. We demonstrate that this effect is absent in the BMC analysis. Our results also showed improved parameter estimation for BMC as compared to SRC for high‐resolution mapping. Overall we find that the combination of BMC analysis and mcDESPOT, BMC‐mcDESPOT, shows excellent performance for accurate high‐resolution whole‐brain mapping of MWF and bi‐component transverse and longitudinal relaxation times within a clinically realistic acquisition time. HIGHLIGHTSBayesian Monte Carlo (BMC) analysis of mcDESPOT is extended to relaxation times.Simulation and in‐vivo studies indicate superiority of BMC‐mcDESPOT to least squares.BMC‐mcDESPOT permits high‐resolution mapping of myelin water and relaxation times.

Incorporation of nonzero echo times in the SPGR and bSSFP signal models used in mcDESPOT

To analyze the effect of neglecting nonzero echo times (TEs) in the conventional model of multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT).

Bayesian analysis of transverse signal decay with application to human brain

Transverse relaxation analysis with several signal models has been used extensively to determine tissue and material properties. However, the derivation of corresponding parameter values is notoriously unreliable. We evaluate improvements in the quality of parameter estimation using Bayesian analysis and incorporating the Rician noise model, as appropriate for magnitude MR images.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty‐eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi‐slice T2‐weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for “progression to symptomatic OA” using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND‐CHRM). WND‐CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy. Clinical significance: Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA.