Estimation of pancreatic R2* for iron overload assessment in the presence of fat: a comparison of different approaches.

OBJECTIVES: To propose a method for estimating pancreatic relaxation rate, R2*, from conventional multi-echo MRI, based on the nonlinear fitting of the acquired magnitude signal decay to MR signal models that take into account both the signal oscillations induced by fat and the different R2* values of pancreatic parenchyma and fat. MATERIALS AND METHODS: Single-peak fat (SPF) and multi-peak fat (MPF) models were introduced. Single-R2* and dual-R2* assumptions were considered as well. Analyses were conducted on simulated data and 20 thalassemia major patients. RESULTS: Simulations revealed the ability of the MPF model to correctly estimate the R2* value in a large range of fat fractions and R2* values. From the comparison between the results obtained with a single R2* value for water and fat and the dual-R2* approach, the latter is more accurate in both water R2* and fat fraction estimation. In patient's data analysis, a strong concordance was found between SPF and MPF estimated data with measurements done with manual signal correction and from fat-saturated images. The MPF method showed better reproducibility. CONCLUSION: The MPF dual-R2* approach improves reproducibility and reduces image analysis time in the assessment of pancreatic R2* value in patients with iron overload.

Non-compact myocardium assessment by cardiac magnetic resonance: dependence on image analysis method.

To compare image analysis methods for the assessment of left ventricle non-compaction from cardiac magnetic resonance (CMR) imaging. CMR images were analyzed in 20 patients and 10 normal subjects. A reference model of the MR signal was introduced and validated based on image data. Non-compact (NC) myocardium size and distribution were assessed by tracing a single, continuous contour delimiting trabeculated region (Jacquier) or by one-by-one selection of trabeculae (Grothoff). The global non-compact/compact (NC/C) ratio, the NC mass, and the segmental NC/C ratio were assessed. Results were compared with the reference model. A significant difference between Grothoff and Jacquier approaches in the estimation of NC/C ratio (32.08 ± 6.63 vs. 19.81 ± 5.72, p < 0.0001) and NC mass (26.59 ± 8.36 vs. 14.15 ± 5.73 g/m2, p < 0.0001) was found. The Grothoff approach better matches the expected signal distribution. Inter-observer reproducibility of both Grothoff and Jacquier methods was adequate (9.71 and 8.22%, respectively) with no significant difference between observers. Jacquier and Grothoff approaches are not interchangeable so that specific diagnostic thresholds should be used for different image analysis methods. Grothoff method seems to better capture the true extension of trabeculated tissue.

Assessment of real-time myocardial uptake and enzymatic conversion of hyperpolarized [1-¹³C]pyruvate in pigs using slice selective magnetic resonance spectroscopy.

Hyperpolarization of ¹³C-labeled energy substrates enables the noninvasive detection and mapping of metabolic activity, in vivo, with magnetic resonance spectroscopy (MRS). Therefore, hyperpolarization and ¹³C MRS can potentially become a powerful tool to study the physiology of organs such as the heart, through the quantification of kinetic patterns under both normal and pathological conditions. In this study we assessed myocardial uptake and metabolism of hyperpolarized [1-¹³C]pyruvate in anesthetized pigs. Pyruvate metabolism was studied at baseline and during dobutamine-induced stimulation. We applied a numerical approach for spectral analysis and kinetic fitting (LSFIT/KIMOfit), making a comparison with a well-known jMRUI/AMARES analysis and γ-variate function, and we estimated the apparent conversion rate of hyperpolarized [1-¹³C]pyruvate into its downstream metabolites [1-¹³C]lactate, [1-¹³C]alanine and [¹³C]bicarbonate in a 3 T MR scanner. We detected an increase in the apparent kinetic constants (k(PX) ) for bicarbonate and lactate of two-fold during dobutamine infusion. These data correlate with the double product (rate-pressure product), an indirect parameter of cardiac oxygen consumption: we observed an increase in value by 46 ± 11% during inotropic stress. The proposed approach might be applied to future studies in models of cardiac disease and/or for the assessment of the pharmacokinetic properties of suitable ¹³C-enriched tracers for MRS.

How the signal-to-noise ratio influences hyperpolarized 13C dynamic MRS data fitting and parameter estimation.

MRS of hyperpolarized (13) C-labeled compounds represents a promising technique for in vivo metabolic studies. However, robust quantification and metabolic modeling are still important areas of investigation. In particular, time and spatial resolution constraints may lead to the analysis of MRS signals with low signal-to-noise ratio (SNR). The relationship between SNR and the precision of quantitative analysis for the evaluation of the in vivo kinetic behavior of metabolites is unknown. In this article, this topic is addressed by Monte Carlo simulations, covering the problem of MRS signal model parameter estimation, with strong emphasis on the peak amplitude and kinetic model parameters. The results of Monte Carlo simulation were confirmed by in vivo experiments on medium-sized animals injected with hyperpolarized [1-(13) C]pyruvate. The results of this study may be useful for the establishment of experimental planning and for the optimization of kinetic model estimation as a function of the SNR value.

Improved T2* assessment in liver iron overload by magnetic resonance imaging.

In the clinical MRI practice, it is common to assess liver iron overload by T2* multi-echo gradient-echo images. However, there is no full consensus about the best image analysis approach for the T2* measurements. The currently used methods involve manual drawing of a region of interest (ROI) within MR images of the liver. Evaluation of a representative liver T2* value is done by fitting an appropriate model to the signal decay within the ROIs vs. the echo time. The resulting T2* value may depend on both ROI placement and choice of the signal decay model. The aim of this study was to understand how the choice of the analysis methodology may affect the accuracy of T2* measurements. A software model of the iron overloaded liver was inferred from MR images acquired from 40 thalassemia major patients. Different image analysis methods were compared exploiting the developed software model. Moreover, a method for global semiautomatic T2* measurement involving the whole liver was developed. The global method included automatic segmentation of parenchyma by an adaptive fuzzy-clustering algorithm able to compensate for signal inhomogeneities. Global liver T2* value was evaluated using a pixel-wise technique and an optimized signal decay model. The global approach was compared with the ROI-based approach used in the clinical practice. For the ROI-based approach, the intra-observer and inter-observer coefficients of variation (CoVs) were 3.7% and 5.6%, respectively. For the global analysis, the CoVs for intra-observers and inter-observers reproducibility were 0.85% and 2.87%, respectively. The variability shown by the ROI-based approach was acceptable for use in the clinical practice; however, the developed global method increased the accuracy in T2* assessment and significantly reduced the operator dependence and sampling errors. This global approach could be useful in the clinical arena for patients with borderline liver iron overload and/or requiring follow-up studies.

Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI.

PURPOSE: To demonstrate that unsupervised assessment of abdominal adipose tissue distribution by magnetic resonance imaging (MRI) can be improved by integrating automatic correction of signal inhomogeneities. MATERIALS AND METHODS: Twenty subjects (body mass index [BMI] 23.7-44.0 kg/m(2)) underwent abdominal (32 slices) MR imaging with a 1.9T Elscint Prestige scanner. Many images were affected by relevant intensity distortions. Unsupervised segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) was performed by a previously validated algorithm exploiting standard fuzzy clustering segmentation. Images were also processed by an improved version of the software, including automatic correction of intensity inhomogeneities. To assess the effectiveness of the two methods SAT and VAT volumes were compared with manual analysis performed by a trained operator. RESULTS: Coefficient of variation between manual and unsupervised analysis was significantly improved by inhomogeneities correction in SAT evaluation. Systematic underestimation of SAT was also corrected. A less important performance improvement was found in VAT measurement. CONCLUSION: The results of this study suggest that the compensation of signal inhomogeneities greatly improves the effectiveness of the unsupervised assessment of abdominal fat. Correction of intensity distortions is important in SAT evaluation and less significant in VAT measurement.

An accurate and robust method for unsupervised assessment of abdominal fat by MRI.

PURPOSE: To describe and evaluate an automatic and unsupervised method for assessing the quantity and distribution of abdominal adipose tissue by MRI. MATERIAL AND METHODS: A total of 20 patients underwent whole-abdomen MRI. A total of 32 transverse T1-weighted images were acquired from each subject. The data collected were transferred to a dedicated workstation and analyzed by both our unsupervised method and a manual procedure. The proposed methodology allows the automatic processing of MRI axial images, segmenting the adipose tissue by fuzzy clustering approach. The use of an active contour algorithm on image masks provided by the fuzzy clustering algorithm allows the separation of subcutaneous fat from visceral fat. Finally, an automated procedure based on automatic image histogram analysis identifies the visceral fat. RESULTS: The accuracy, reproducibility, and speed of our automatic method were compared with the state-of-the-art manual approach. The unsupervised analysis correlated well with the manual analysis, and was significantly faster than manual tracing. Moreover, the unsupervised method was not affected by intraobserver and interobserver variability. CONCLUSION: The results obtained demonstrate that the proposed method can provide the volume of subcutaneous adipose tissue, visceral adipose tissue, global adipose tissue, and the ratio between subcutaneous and visceral fat in an unsupervised and effective manner.