Velan SS, Said N, Durst C, Frisbee S, Frisbee J, Raylman RR, Thomas MA, Rajendran VM, Spencer RG, Alway SE. Distinct patterns of fat metabolism in skeletal muscle of normal-weight, overweight, and obese humans. Am J Physiol Regul Integr Comp Physiol 295: R1060 -R1065, 2008. First published July 30, 2008 doi:10.1152/ajpregu.90367.2008The link between body weight, lipid metabolism, and health risks is poorly understood and difficult to study. Magnetic resonance spectroscopy (MRS) permits noninvasive investigation of lipid metabolism. We extended existing two-dimensional MRS techniques to permit quantification of intra-and extramyocellular lipid (IMCL and EMCL, respectively) compartments and their degree of unsaturation in human subjects and correlated these results with body mass index (BMI). Using muscle creatine for normalization, we observed a statistically significant (P Ͻ 0.01) increase in the IMCL-to-creatine ratio with BMI (n ϭ 8 subjects per group): 5.9 Ϯ 1.7 at BMI Ͻ 25, 10.9 Ϯ 1.82 at 25 Ͻ BMI Ͻ 30, and 13.1 Ϯ 0.87 at BMI Ͼ 30. Similarly, the degree of IMCL unsaturation decreased significantly (P Ͻ 0.01) with BMI: 1.51 Ϯ 0.08 at BMI Ͻ 25, 1.30 Ϯ 0.11 at 25 Ͻ BMI Ͻ 30, and 0.90 Ϯ 0.14 at BMI Ͼ 30. We conclude that important aspects of lipid metabolism can be evaluated by two-dimensional MRS and propose that degree of unsaturation measured noninvasively may serve as a biomarker for lipid metabolic defects associated with obesity. magnetic resonance spectroscopy; lipid unsaturation; intramyocellular lipid; extramyocellular lipid THE AMOUNT OF BODY FAT is a risk factor for several obesityrelated disorders. Obesity is a known risk factor for the development of insulin resistance and diabetes and is a key component of metabolic syndrome. The causal relationship between increased dyslipidemia and adiposity and impaired glucose homeostasis is unclear, although it is known that lipid oversupply to the organs primarily involved in glucose homeostasis, that is, muscle, liver, and pancreas, leads to impaired insulin function in those tissues (22).Previous studies showed that intramyocellular lipid (IMCL) is increased with obesity and in non-insulin-dependent diabetes mellitus (9,11,15). It has been suggested that increased visceral adiposity and reduced lipid oxidation might contribute to the increase in IMCL (18). Thus the ability to monitor the IMCL pool and its properties noninvasively by magnetic resonance spectroscopy (MRS) has been an important development (4, 28). The correlation between the magnitude of the IMCL pool, as determined by MRS studies, and insulin resistance, diabetes, and disorders of lipid metabolism has been previously demonstrated (2,3,32,33). Nevertheless, quantification of IMCL and extramyocellular lipid (EMCL) by MRS remains highly problematic (32). The ability to distinguish IMCL from EMCL is based on their different bulk magnetic susceptibility effects due to their geometric arrangements within muscle, which leads to a spectroscopic frequency separation between the two pools; this sepa...

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Introduction Extremity lipomas and well-differentiated liposarcomas (WDLs) are difficult to distinguish on MR imaging. We sought to evaluate the accuracy of MRI interpretation using MDM2 amplification, via fluorescence in-situ hybridization (FISH), as the gold standard for pathologic diagnosis. Furthermore, we aimed to investigate the utility of a diagnostic formula proposed in the literature. Methods We retrospectively collected 49 patients with lipomas or WDLs utilizing MDM2 for pathologic diagnosis. Four expert readers interpreted each patient's MRI independently and provided a diagnosis. Additionally, a formula based on imaging characteristics (i.e. tumor depth, diameter, presence of septa, and internal cystic change) was used to predict the pathologic diagnosis. The accuracy and reliability of imaging-based diagnoses were then analyzed in comparison to the MDM2 pathologic diagnoses. Results The accuracy of MRI readers was 73.5% (95% CI 61–86%) with substantial interobserver agreement (κ=0.7022). The formula had an accuracy of 71%, which was not significantly different from the readers (p=0.71). The formula and expert observers had similar sensitivity (83% versus 83%) and specificity (64.5% versus 67.7%; p=0.659) for detecting WDLs. Conclusion The accuracy of both our readers and the formula suggests that MRI remains unreliable for distinguishing between lipoma and WDLs.

Introduction Diffusion-MRI is a rapidly evolving research field that has produced a wealth of algorithms for the analysis of white matter fibre architecture and disorders in the brain. Camino is a free, open-source toolkit designed to make a selection of this technology available and convenient to use for the diffusion MRI research community. Camino implements a data processing pipeline, which allows for easy scripting and flexible integration with other software. This abstract summarises the features of Camino at each stage of the pipeline from the raw data to the statistics used by clinicians and researchers.Design Camino is written in Java, and designed for a Unix-style interface. The user documentation is in the form of Unix manual pages, and each program has a shell wrapper, so users do not require any knowledge of Java. The data pipeline provides flexibility, by allowing data to be imported and exported to other software, and transparency, because the output of each Camino program can be analyzed in detail. Fig. 1 illustrates the pipeline. Camino processes all data in voxel order, where the measurements for each voxel are stored together. This ordering facilitates the data pipeline model and allows each voxel to be processed independently, which simplifies parallel processing. All of Camino's output is in a documented raw binary format. The tractography module optionally outputs Analyze images for easy integration with visualisation software. DataThe data source for Camino can be raw data from a scanner or from Camino's data synthesiser. The data synthesiser emulates scanner sequences and provides synthetic data from a range of customisable test functions. Data from scanners is not typically in voxel order, so Camino contains tools for rearranging data into the correct format. Associated with each data file is the scheme file, which is a text file that describes the acquisition parameters for each measurement.Reconstruction The reconstruction stage takes as input raw data and reconstructs information about the diffusion in each voxel. Camino supports a range of standard and advanced reconstruction algorithms. The simplest and fastest inversion is the linear diffusion tensor fit, in which the elements of the diffusion tensor are calculated from a standard linear least-squares fit to the log of the measurements. With the dtfit program, a linear diffusion tensor fit takes about two minutes for a 128 × 128 × 60 voxel data set, on a standard Pentium IV 2.4 GHz workstation. Nonlinear fitting is slower but potentially more accurate. Camino supports nonlinear least-squares diffusion tensor fitting and the RESTORE method [1]. Camino also supports non-tensor based diffusion reconstruction, including mixture modelling [2], PAS-MRI [3], Q-ball [4] and spherical deconvolution [5]. Spherical harmonic voxel classification [6] detects non-Gaussian diffusion. The thresholds used in classification can be set interactively (Fig. 2) and the classification can be used to drive reconstruction, fitting multi-fibre models only wh...

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