Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra

Object

This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.

Materials and methods

Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.

Results

Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.

Conclusion

These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T ¹H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.     

Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.     

Georgius Agricola,Begründer der Montanwissenschaften

Georgius Agricola had not only for centuries been an authoritative figure in the earth sciences but he was also deeply involved in political and cultural reorganisation at the beginning of the 16th century. He strove for a new understanding of science attaching to it the quality of observing reality and the necessity of putting these observations into a systematic order, thus previewing the encyclopedic movement of the enlightenment. He could do this in a frame-work which was lent from the De-re-rustica-literature which still acceptedvenatio, cursiositas andvirtuositas as preconditions for scientific research as well as for political activity.     

Placental maturation disorders in relation to the quality of metabolic control during pregnancy in insulin-dependent and gestational diabetes

In 1969/70 and 1979/80, 100 placentas each of diabetics were morphologically examined. The findings obtained were evaluated against the background of metabolic monitoring and typical phenomena of perinatal morbidity. The studies were conducted for the purpose of finding out the extent to which maintenance of normal glycaemic metabolism throughout pregnancy could affect the severity of diabetic disorders of placental maturation. Medium to severe placental maturation disorders were found to be closely correlated with quality of metabolic control (P less than 0.01). The findings thus obtained have produced evidence to the effect that maintenance of normal glycaemic metabolism throughout pregnancy would be of favourable impact not only upon perinatal mortality and morbidity. It would also cause significant reduction of placental maturation disorders.     

Flow-compensated self-gating

OBJECTIVE: Self-gating (SG) is a method to record cardiac movement during MR imaging. It uses information from an additional short, non-spatially encoded data acquisition. This usually lengthens TE and increases the sensitivity to flow artifacts. A new flow compensation scheme optimized for self-gating sequences is introduced that has very little or no time penalty over self-gating sequences without flow compensation. MATERIALS AND METHODS: Three variants of a self-gated 2D spoiled gradient echo or fast low angle shot (FLASH) sequence were implemented: without (noFC), with a conventional, serial (cFC), and with a new, time-efficient flow compensation (sFC). In experiments on volunteers and small animals, the sequence variants were compared with regard to the SG signal and the flow artifacts in the images. RESULTS: Both cFC and sFC reduce flow artifacts in cardiac images. The SG signal of the sFC is more sensitive to physiological motion, so that a cardiac trigger can be extracted more precisely as in cFC. In a typical setting for small animal imaging, sFC technique reduces the echo/repetition time over cFC by about 23%/14%. CONCLUSION: The time-efficient sFC technique provides flow-compensated images with cardiac triggering in both volunteers and small animals.     

Solving nonlinear dynamic models by iterative dynamic programming

The paper presents a discrete-time dynamic programming algorithm that is suitable to track nonlinearities in intertemporal optimization problems. In contrast to using linearization methods for solving intertemporal models, the proposed algorithm operates globally by iteratively computing the value function and the controls in feedback form on a grid. A conjecture of how the trajectories might behave is analytically obtained by letting the discount rate approach infinity. The dynamic found serves as a useful device for computing the trajectories for finite discount rates employing the algorithm. Commencing with a large step and grid size, and then pursuing time step and grid refinements allows for the replication of the nonlinear dynamics for various finite discount rates. As the time step and grid size shrink, the discretization errors vanish. The algorithm is applied to three economic examples. Two examples are of deterministic type; the third is stochastic. In the deterministic cases limit cycles are detected.The present paper draws on joint work with Malte Sieveking whom I want to thank for many discussions.     

Hierarchies in nucleation transitions

We discuss the hierarchy of subphase transitions in first-order-like nucleation processes for an exemplified aggregation transition of heteropolymers. We perform an analysis of the microcanonical entropy, i.e., the density of states is considered as the central statistical system quantity since it connects system-specific entropic and energetic information in a natural and unique way.     

A Nash Equilibrium for Differential Games with Moving-Horizon Strategies

Computational Economics - Our paper aims at introducing a moving-horizon interaction in a strategic context. We assume that, in each instant of time, players can predict the effects of their...