Following the discovery of superconductivity in polycrystalline magnesium diboride a tremendous effort is now focused on thin films of this material. Contrary to expectations, the penetration of magnetic flux in such films is found dominated by large dendritic structures abruptly created when small fields are applied. The dendritic instability, observed below 10 K using magneto-optical imaging, has a temperature dependent morphology ranging from quasi-1D dendrites to beautiful tree-like structures. This behavior is responsible for the anomalous noise in magnetization curves, and strongly suppresses the apparent critical current. Simulations of vortex dynamics incorporating local heating effect reproduce the observed dendritic scenario.The new superconductor, MgB 2 , discovered (1) in January this year has already proved to be a promising candidate for technological applications due to success in fabrication of thin films (2) and wires (3) with high current carrying capabilities. At the same time, such films and wires, as well as polycrystalline MgB 2 are reported to show exceptional magnetic behavior displaying numerous and "noise-like" jumps in the magnetization as a function of applied field (3-5). Magnetization jumps in type-II superconductors are usually associated with a thermo-magnetic instability of the quantized flux lines (vortices) penetrating the material. When the vortices move they leave a trail of elevated temperature facilitating motion of nearby vortices, which eventually leads to a large-scale avalanche invasion of depinned flux lines (6). This thermal runaway, where the magnetic energy stored in the superconductor suddenly converts to thermal energy, can cause instability of the superconducting properties and have catastrophic consequences for practical applications. To which extent the thermomagnetic instability will affect the technological potential of MgB 2 is today a vitally important question. In high temperature superconductors (HTSs) the flux jumps occur only in bulk materials, the first one at applied fields of typically one Tesla and then nearly periodically as the field increases. In MgB 2 the jumps are omnipresent, with the first one occurring already at a few milliTesla and subsequent jumps coming at random. Thus, MgB 2 appears not only far more susceptible to thermal runaways than HTSs, but the flux jumps exhibit also qualitatively new features. All this motivated the present study of MgB 2 films using magneto-optical (MO) imaging to visualize and characterize the nature of the magnetic instability.Thin films of MgB 2 were fabricated on (1  102) Al 2 O 3 substrates using pulsed laser deposition. An amorphous B film was first deposited, and then sintered at high temperature in a Mg atmosphere. Details of the preparation are reported elsewhere (2). Typical films had a sharp superconducting transition (∆T c ~ 0.7 K) at T c = 39 K, and a high degree of c-axis alignment perpendicular to the film plane.Magnetic characterization of the films was first done by measuring the magnetization...

We report a detailed comparison of experimental data and theoretical predictions for the dendritic flux instability, believed to be a generic behavior of type-II superconducting films. It is shown that a thermomagnetic model published very recently [Phys. Rev. B 73, 014512 (2006)10.1103/PhysRevB.73.014512] gives an excellent quantitative description of key features like the stability onset (first dendrite appearance) magnetic field, and how the onset field depends on both temperature and sample size. The measurements were made using magneto-optical imaging on a series of different strip-shaped samples of MgB2. Excellent agreement is also obtained by reanalyzing data previously published for Nb.

Purpose:To evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for early prediction of response to neoadjuvant chemotherapy (NAC) and 5-year survival in patients with locally advanced breast cancer.Materials and Methods: DCE-MRI was performed in patients scheduled for NAC (n ϭ 24) before and after the first treatment cycle. Clinical response was evaluated after completed NAC. Relative signal intensity (RSI) and area under the curve (AUC) were calculated from the DCE-curves and compared to clinical treatment response. Kohonen and probabilistic neural network (KNN and PNN) analysis were used to predict 5-year survival.Results: RSI and AUC were reduced after only one cycle of NAC in patients with clinical treatment response (P ϭ 0.02 and P ϭ 0.08). The mean and 10th percentile RSI values before NAC were significantly lower in patients surviving more than 5 years compared to nonsurvivors (P ϭ 0.05 and 0.02). This relationship was confirmed using KNN, which demonstrated that patients who remained alive clustered in separate regions from those that died. Calibration of contrast enhancement curves by PNN for patient survival at 5 years yielded sensitivity and specificity for training and testing ranging from 80%-92%.Conclusion: DCE-MRI in locally advanced breast cancer has the potential to predict 5-year survival in a small patient cohort. In addition, changes in tumor vascularization after one cycle of NAC can be assessed.

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.