Quantification and localization of contrast agents using delta relaxation enhanced magnetic resonance at 1.5?T

Object

Delta relaxation enhanced magnetic resonance (dreMR) is a new imaging technique based on the idea of cycling the magnetic field B ₀ during an imaging sequence. The method determines the field dependency of the relaxation rate (relaxation dispersion dR ₁/dB). This quantity is of particular interest in contrast agent imaging because the parameter can be used to determine contrast agent concentrations and increases the ability to localize the contrast agent.

Materials and methods

In this paper dreMR imaging was implemented on a clinical 1.5?T MR scanner combining conventional MR imaging with fast field-cycling. Two improvements to dreMR theory are presented describing the quantification of contrast agent concentrations from dreMR data and a correction for field-cycling with finite ramp times.

Results

Experiments demonstrate the use of the extended theory and show the measurement of contrast agent concentrations with the dreMR method. A second experiment performs localization of a contrast agent with a significant improvement in comparison to conventional imaging.

Conclusion

dreMR imaging has been extended by a method to quantify contrast agent concentrations and improved for field-cycling with finite ramp times. Robust localization of contrast agents using dreMR imaging has been performed in a sample where conventional imaging delivers inconclusive results.     

Comparison of two quantitative proton density mapping methods in multiple sclerosis

Objective

Proton density (PD) mapping requires correction for the receive profile (RP), which is frequently performed via bias-field correction. An alternative RP-mapping method utilizes a comparison of uncorrected PD-maps and a value ρ(T1) directly derived from T1-maps via the Fatouros equation. This may be problematic in multiple sclerosis (MS), if respective parameters are only valid for healthy brain tissue. We aimed to investigate whether the alternative method yields correct PD values in MS patients.

Materials/methods

PD mapping was performed on 27 patients with relapsing-remitting MS and 27 healthy controls, utilizing both methods, yielding reference PD values (PD_ref, bias-field method) and PD_alt (alternative method).

Results

PD_alt-values closely matched PD_ref, both for patients and controls. In contrast, ρ(T1) differed by up to 3 % from PD_ref, and the voxel-wise correlation between PD_ref and ρ(T1) was reduced in a patient subgroup with a higher degree of disability. Still, discrepancies between ρ(T1) and PD_ref were almost identical across different tissue types, thus translating into a scaling factor, which cancelled out during normalization to 100 % in CSF, yielding a good agreement between PD_alt and PD_ref.

Conclusion

RP correction utilizing the auxiliary parameter ρ(T1) derived via the Fatouros equation provides accurate PD results in MS patients, in spite of discrepancies between ρ(T1) and actual PD values.

     

Induction of rabbit renal mixed-function oxidases by phenobarbital: cell specific ultrastructural changes in the proximal tubule

Administration of phenobarbital (60 mg/kg) daily for 4 days to male rabbits resulted in induction of renal cytochrome P-450 (3.5-fold) and a corresponding increase in ethoxycoumarin-O-deethylase and benzphetamine-N-demethylase activity (17- and 4-fold, respectively). Kidney weight to body weight ratio and renal ethoxyresorufin-O-deethylase were not affected by phenobarbital pretreatment. Numerous focal areas of proliferation of smooth endoplasmic reticulum (SER) were evident in proximal tubule cells from phenobarbital treated rabbits while proximal tubular cells from control rabbits had only small and sparcely located aggregates of SER. Phenobarbital-induced SER proliferation was specifically localized to the S3 segment of the proximal tubule. Proliferation was not observed in S2 cells of the proximal tubule, cells of Henle's loop, distal tubules, or collecting tubules in rabbits pretreated with phenobarbital. These data demonstrate the biochemical heterogeneity of cell types within the proximal tubules of rabbits. Furthermore, induction of mixed-function oxidases specifically in S3 cells of the proximal tubule may be of toxicological significance in the metabolic activation of certain nephrotoxicants.     

Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.     

Detecting and preventing replay attacks in industrial automation networks operated with profinet IO

Modern industrial facilities consist of controllers, actuators and sensors that are connected via traditional IT equipment. The ongoing integration of these systems into the communication network yields to new threats and attack possibilities. In industrial networks, often distinct communication protocols like Profinet IO (PNIO) are used. These protocols are often not supported by typical network security tools. In this work, we present two attack techniques that allow to take over the control of a PNIO device, enabling an attacker to replay previously recorded traffic. We model attack detection rules and propose an intrusion detection system (IDS) for industrial networks which is capable of detecting those replay attacks by correlating alerts from traditional IT IDS with specific PNIO alarms. As an additional effort, we introduce defense in depth mechanisms in order to prevent those attacks from taking effect in the physical world. Thereafter, we evaluate our IDS in a physical demonstrator and compare it with another IDS dedicated to securing PNIO networks. In a conceptual design, we show how network segmentation with flow control allows for preventing some, but not all of the attacks.     

Dynamic and Formal Verification of Embedded Systems: A Comparative Survey

Embedded Systems, by their nature, constitute a meeting point for communities with extremely different background. In particular, the high demands for quality and reliability for embedded systems have led to complementary quality assurance efforts: hardware engineers have developed techniques for dynamic verification in terms of co-simulation, which, in particular, addresses the different nature of hardware and software components. Thus these techniques are tailored for the transactional level, which comprises dedicated models for the hardware and the software parts. On the other hand, there is a bulk of work on formal verification techniques, which typically address higher levels of abstraction. These techniques are exhaustive in the sense that they cover all the infinite possible paths of their models, however at the price of neglecting many of the low-level aspects treated by co-simulation. It is the goal of this paper to increase the mutual understanding between these communities and to animate research at this exciting borderline.     

Model-driven development of mobile applications for Android and iOS supporting role-based app variability

Rapidly increasing numbers of applications and users make the development of mobile applications to one of the most promising fields in software engineering. Due to short time to market, differing platforms, and fast emerging technologies, mobile application development faces typical challenges where model-driven development (MDD) can help. We present a modeling language and an infrastructure for the MDD of native apps in Android and iOS. Our approach allows a flexible app development on different abstraction levels: compact modeling of standard app elements such as standard data management and increasingly detailed modeling of individual elements to cover, for example, specific behavior. Moreover, a kind of variability modeling is supported such that mobile apps with variants can be developed. We demonstrate our MDD approach with several apps including a conference app, a museum guide with augmented reality functionality, and a SmartPlug.