Differentiation of focal liver lesions: usefulness of parametric imaging with contrast-enhanced US.

Purpose: To evaluate whether parametric imaging with contrast material-enhanced ultrasonography (US) is superior to visual assessment for the differential diagnosis of focal liver lesions (FLLs). Materials and Methods: This study had institutional review board approval, and verbal patient informed consent was obtained. Between August 2005 and October 2008, 146 FLLs in 145 patients (63 women, 82 men; mean age, 62.5 years; age range, 22-89 years) were imaged with real-time low-mechanical-index contrast-enhanced US after a bolus injection of 2.4 mL of a second-generation contrast agent. Clips showing contrast agent uptake kinetics (including arterial, portal, and late phases) were recorded and subsequently analyzed off-line with dedicated image processing software. Analysis of the dynamic vascular patterns (DVPs) of lesions with respect to adjacent parenchyma allowed mapping DVP signatures on a single parametric image. Cine loops of contrast-enhanced US and results from parametric imaging of DVP were assessed separately by three independent off-site readers who classified each lesion as benign, malignant, or indeterminate. Sensitivity, specificity, accuracy, and positive and negative predictive values were calculated for both techniques. Interobserver agreement (κ statistics) was determined. Results: Sensitivities for visual interpretation of cine loops for the three readers were 85.0%, 77.9%, and 87.6%, which improved significantly to 96.5%, 97.3%, and 96.5% for parametric imaging, respectively (P < .05, McNemar test), while retaining high specificity (90.9% for all three readers). Accuracy scores of parametric imaging were higher than those of conventional contrast-enhanced US for all three readers (P < .001, McNemar test). Interobserver agreement increased with DVP parametric imaging compared with conventional contrast-enhanced US (change of κ from 0.54 to 0.99). Conclusion: Parametric imaging of DVP improves diagnostic performance of contrast-enhanced US in the differentiation between malignant and benign FLLs; it also provides excellent interobserver agreement.

An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer-set, andthe firm decides the best subset to offer at any given moment to maximize expected revenue. The resultingdynamic program for the firm is intractable and approximated by a deterministic linear programcalled the CDLP which has an exponential number of columns. However, under the choice-set paradigmwhen the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has beenproposed but finding an entering column has been shown to be NP-hard. In this paper, starting with aconcave program formulation based on segment-level consideration sets called SDCP, we add a class ofconstraints called product constraints, that project onto subsets of intersections. In addition we proposea natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methodson the benchmark data sets in the literature. Both the product constraints and the ?SDCP method arevery simple and easy to implement and are applicable to the case of overlapping segment considerationsets. In our computational testing on the benchmark data sets in the literature, SDCP with productconstraints achieves the CDLP value at a fraction of the CPU time taken by column generation and webelieve is a very promising approach for quickly approximating CDLP when segment consideration setsoverlap and the consideration sets themselves are relatively small.

Snapshot gradient-recalled echo-planar images of rat brains at long echo time at 9.4 T.

With improved B 0 homogeneity along with satisfactory gradient performance at high magnetic fields, snapshot gradient-recalled echo-planar imaging (GRE-EPI) would perform at long echo times (TEs) on the order of T2*, which intrinsically allows obtaining strongly T2*-weighted images with embedded substantial anatomical details in ultrashort time. The aim of this study was to investigate the feasibility and quality of long TE snapshot GRE-EPI images of rat brain at 9.4 T. When compensating for B 0 inhomogeneities, especially second-order shim terms, a 200 x 200 microm2 in-plane resolution image was reproducibly obtained at long TE (>25 ms). The resulting coronal images at 30 ms had diminished geometric distortions and, thus, embedded substantial anatomical details. Concurrently with the very consistent stability, such GRE-EPI images should permit to resolve functional data not only with high specificity but also with substantial anatomical details, therefore allowing coregistration of the acquired functional data on the same image data set.

Use of 2D Sensitivity Encoding for Slow-Infusion Contrast-Enhanced Isotropic 3-T Whole-Heart Coronary MR Angiography.

OBJECTIVE. The purpose of this study was to improve the blood-pool signal-to-noise ratio (SNR) and blood-myocardium contrast-to-noise ratio (CNR) of slow-infusion 3-T whole-heart coronary MR angiography (MRA).SUBJECTS AND METHODS. In 2D sensitivity encoding (SENSE), the number of acquired k-space lines is reduced, allowing less radiofrequency excitation per cardiac cycle and a longer TR. The former can be exploited for signal enhancement with a higher radiofrequency excitation angle, and the latter leads to noise reduction due to lower data-sampling bandwidth. Both effects contribute to SNR gain in coronary MRA when spatial and temporal resolution and acquisition time remain identical. Numeric simulation was performed to select the optimal 2D SENSE pulse sequence parameters and predict the SNR gain. Eleven patients underwent conventional unenhanced and the proposed 2D SENSE contrast-enhanced coronary MRA acquisition. Blood-pool SNR, blood-myocardium CNR, visible vessel length, vessel sharpness, and number of side branches were evaluated.RESULTS. Consistent with the numeric simulation, using 2D SENSE in contrast-enhanced coronary MRA resulted in significant improvement in aortic blood-pool SNR (unenhanced vs contrast-enhanced, 37.5 +/- 14.7 vs 121.3 +/- 44.0; p < 0.05) and CNR (14.4 +/- 6.9 vs 101.5 +/- 40.8; p < 0.05) in the patient sample. A longer length of left anterior descending coronary artery was visualized, but vessel sharpness, coronary artery coverage, and image quality score were not improved with the proposed approach.CONCLUSION. In combination with contrast administration, 2D SENSE was found effective in improving SNR and CNR in 3-T whole-heart coronary MRA. Further investigation of cardiac motion compensation is necessary to exploit the SNR and CNR advantages and to achieve submillimeter spatial resolution.

The dexamethasone-induced inhibition of proliferation, migration, and invasion in glioma cell lines is antagonized by macrophage migration inhibitory factor (MIF) and can be enhanced by specific MIF inhibitors.

Glioblastomas (GBMs) are the most frequent and malignant brain tumors in adults. Glucocorticoids (GCs) are routinely used in the treatment of GBMs for their capacity to reduce the tumor-associated edema. Few in vitro studies have suggested that GCs inhibit the migration and invasion of GBM cells through the induction of MAPK phosphatase 1 (MKP-1). Macrophage migration inhibitory factor (MIF), an endogenous GC antagonist is up-regulated in GBMs. Recently, MIF has been involved in tumor growth and migration/invasion and specific MIF inhibitors have been developed on their capacity to block its enzymatic tautomerase activity site. In this study, we characterized several glioma cell lines for their MIF production. U373 MG cells were selected for their very low endogenous levels of MIF. We showed that dexamethasone inhibits the migration and invasion of U373 MG cells, through a glucocorticoid receptor (GR)- dependent inhibition of the ERK1/2 MAPK pathway. Oppositely, we found that exogenous MIF increases U373 MG migration and invasion through the stimulation of the ERK1/2 MAP kinase pathway and that this activation is CD74 independent. Finally, we used the Hs 683 glioma cells that are resistant to GCs and produce high levels of endogenous MIF, and showed that the specific MIF inhibitor ISO-1 could restore dexamethasone sensitivity in these cells. Collectively, our results indicate an intricate pathway between MIF expression and GC resistance. They suggest that MIF inhibitors could increase the response of GBMs to corticotherapy.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Enhanced degradation of metalaxyl in Gley Humic and Dark Red Latosol

Enhanced degradation of the fungicide metalaxyl was investigated in two soils: a gley humic (GH) and a Dark Red Latosol (LE), collected at sites never exposed to the fungicide. The soil samples were treated with successive applications of metalaxyl as a commercial formulation and 14C-metalaxyl in laboratory. Metalaxyl biodegradation was analyzed during 63 days by means of radiometric techniques to verify biomineralization and degradation product formation from the applied 14C-metalaxyl. Although biomineralization (maximum of 14 and 8% in the GH and LE soils, respectively), and partial degradation (about 32 and 48%, respectively) were detected in both soils, enhanced degradation was verified only in the GH soil. Results proved that metalaxyl behaves differently in soils.

A double-quadrature radiofrequency coil design for proton-decoupled carbon-13 magnetic resonance spectroscopy in humans at 7T

Purpose Carbon-13 magnetic resonance spectroscopy (13C-MRS) is challenging because of the inherent low sensitivity of 13C detection and the need for radiofrequency transmission at the 1H frequency while receiving the 13C signal, the latter requiring electrical decoupling of the 13C and 1H radiofrequency channels. In this study, we added traps to the 13C coil to construct a quadrature-13C/quadrature-1H surface coil, with sufficient isolation between channels to allow simultaneous operation at both frequencies without compromise in coil performance. Methods Isolation between channels was evaluated on the bench by measuring all coupling parameters. The quadrature mode of the quadrature-13C coil was assessed using in vitro 23Na gradient echo images. The signal-to-noise ratio (SNR) was measured on the glycogen and glucose resonances by 13C-MRS in vitro, compared with that obtained with a linear-13C/quadrature-1H coil, and validated by 13C-MRS in vivo in the human calf at 7T. Results Isolation between channels was better than â^'30 dB. The 23Na gradient echo images indicate a region where the field is strongly circularly polarized. The quadrature coil provided an SNR enhancement over a linear coil of 1.4, in vitro and in vivo. Conclusion It is feasible to construct a double-quadrature 13C-1H surface coil for proton decoupled sensitivity enhanced 13C-NMR spectroscopy in humans at 7T. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques