Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

SummaryObjectiveThe purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.MethodAn approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.ResultsThe binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.ConclusionMRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis

Denoising for improved parametric MRI of the kidney: protocol for nonlocal means filtering

In order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T(2)* and T(2)) due to low SNR a number of strategies can be followed. One approach is postprocessing of the acquired data with a filter. The basic idea is that MR images possess a local spatial structure that is characterized by equal, or at least similar, noise-free signal values in vicinities of a location. Then, local averaging of the signal reduces the noise component of the signal. In contrast, nonlocal means filtering defines the weights for averaging not only within the local vicinity, bur it compares the image intensities between all voxels to define "nonlocal" weights. Furthermore, it generally compares not only single-voxel intensities but small spatial patches of the data to better account for extended similar patterns. Here we describe how to use an open source NLM filter tool to denoise 2D MR image series of the kidney used for parametric mapping of the relaxation times T(2)* and T(2).This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers

Parsimonious discretization for characterizing multi‐exponential decay in magnetic resonance

International audienceWe address the problem of analyzing noise-corrupted magnetic resonance transverse decay signals as a superposition of underlying independently decaying monoexponentials of positive amplitude. First, we indicate the manner in which this is an ill-conditioned inverse problem, rendering the analysis unstable with respect to noise. Second, we define an approach to this analysis, stabilized solely by the nonnegativity constraint without regularization. This is made possible by appropriate discretization, which is coarser than that often used in practice. Thirdly, we indicate further stabilization by inspecting the plateaus of cumulative distributions. We demonstrate our approach through analysis of simulated myelin water fraction measurements, and compare the accuracy with more conventional approaches. Finally, we apply our method to brain imaging data obtained from a human subject, showing that our approach leads to maps of the myelin water fraction which are much more stable with respect to increasing noise than those obtained with conventional approaches

The NESMA Filter: Nonlocal Estimation of Multi-Spectral Magnitudes for Noise Reduction in Magnetic Resonance Images

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MUTANT : a new image filtering paradigm for improved parameters determination. Application to myelin water fraction mapping and sodium concentrations imaging

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Properties of K,Rb-intercalated C60 encapsulated inside carbon nanotubes called peapods derived from nuclear magnetic resonance

We present a detailed experimental study on how magnetic and electronic properties of Rb, K-intercalated C-60 encapsulated inside carbon nanotubes called peapods can be derived from C-13 nuclear magnetic resonance investigations. Ring currents do play a basic role in those systems; in particular, the inner cavities of nanotubes offer an ideal environment to investigate the magnetism at the nanoscale. We report the largest diamagnetic shifts down to -68.3 ppm ever observed in carbon allotropes, which is connected to the enhancement of the aromaticity of the nanotube envelope upon intercalation. The metallization of intercalated peapods is evidenced from the chemical shift anisotropy and spin-lattice relaxation (T-1) measurements. The observed relaxation curves signal a three-component model with two slow and one fast relaxing components. We assigned the fast component to the unpaired electrons charged C-60 that show a phase transition near 100 K. The two slow components can be rationalized by the two types of charged C-60 at two different positions with a linear regime following Korringa behavior, which is typical for metallic system and allow us to estimate the density of sate at Fermi level n(E-F)

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

Myelin loss and iron accumulation are cardinal features of aging and various neurodegenerative diseases. Oligodendrocytes incorporate iron as a metabolic substrate for myelin synthesis and maintenance. An emerging hypothesis in Alzheimer's disease research suggests that myelin breakdown releases substantial stores of iron that may accumulate, leading to further myelin breakdown and neurodegeneration. We assessed associations between iron content and myelin content in critical brain regions using quantitative magnetic resonance imaging (MRI) on a cohort of cognitively unimpaired adults ranging in age from 21 to 94 years. We measured whole-brain myelin water fraction (MWF), a surrogate of myelin content, using multicomponent relaxometry, and whole-brain iron content using susceptibility weighted imaging in all individuals. MWF was negatively associated with iron content in most brain regions evaluated indicating that lower myelin content corresponds to higher iron content. Moreover, iron content was significantly higher with advanced age in most structures, with men exhibiting a trend towards higher iron content as compared to women. Finally, relationship between MWF and age, in all brain regions investigated, suggests that brain myelination continues until middle age, followed by degeneration at older ages. This work establishes a foundation for further investigations of the etiology and sequelae of myelin breakdown and iron accumulation in neurodegeneration and may lead to new imaging markers for disease progression and treatment

Electromagnetic Properties of Inner Double Walled Carbon Nanotubes Investigated by Nuclear Magnetic Resonance

The nuclear magnetic resonance (NMR) analytical technique was used to investigate the double walled carbon nanotubes (DWNTs) electromagnetic properties of inner walls. The local magnetic and electronic properties of inner nanotubes in DWNTs were analyzed using 25% 13C enriched C60 by which the effect of dipolar coupling could be minimized. The diamagnetic shielding was determined due to the ring currents on outer nanotubes in DWNTs. The NMR chemical shift anisotropy (CSA) spectra and spin-lattice relaxation studies reveal the metallic properties of the inner nanotubes with a signature of the spin-gap opening below 70 K

Structural properties of carbon nanotubes derived from C-13 NMR

We present a detailed experimental and theoretical study on how structural properties of carbon nanotubes can be derived from 13C NMR investigations. Magic angle spinning solid state NMR experiments have been performed on single-and multiwalled carbon nanotubes with diameters in the range from 0.7 to 100 nm and with number of walls from 1 to 90. We provide models on how diameter and the number of nanotube walls influence NMR linewidth and line position. Both models are supported by theoretical calculations. Increasing the diameter D, from the smallest investigated nanotube, which in our study corresponds to the inner nanotube of a double-walled tube to the largest studied diameter, corresponding to large multiwalled nanotubes, leads to a 23.5 ppm diamagnetic shift of the isotropic NMR line position d. We show that the isotropic line follows the relation d = 18.3/D + 102.5 ppm, where D is the diameter of the tube and NMR line position d is relative to tetramethylsilane. The relation asymptotically tends to approach the line position expected in graphene. A characteristic broadening of the line shape is observed with the increasing number of walls. This feature can be rationalized by an isotropic shift distribution originating from different diamagnetic shielding of the encapsulated nanotubes together with a heterogeneity of the samples. Based on our results, NMR is shown to be a nondestructive spectroscopic method that can be used as a complementary method to, for example, transmission electron microscopy to obtain structural information for carbon nanotubes, especially bulk samples