Impact of MRI RF coil design on the RF-induced heating of medical implants: fixedB1+rmsexposure versus normal operating mode.

A direct comparison of the impact of RF coil design under specific absorption rate andB1+rmslimitations are investigated and quantified using RF coils of different geometries and topologies at 64 MHz and 128 MHz. The RF-inducedin vivoelectric field and power deposition of a 50 cm long pacemaker and 55 cm long deep brain stimulator (DBS) are evaluated within two anatomical models exposed with these RF coils. The associated uncertainty is quantified and analyzed under a fixedB1+rmsincident and normal operating mode. For a fixedB1+rmsincident, thein vivoincident field shows a much higher uncertainty (>5.6 dB) to the RF coil diameter compared to other design parameters (e.g. <2.2 dB for coil length and topology), while the associated uncertainty reduced greatly (e.g. <1.5 dB) under normal operating mode exposure. Similar uncertainties are observed in the power deposition near the pacemaker and DBS electrode. Compared to the normal operating mode, applying a fixedB1+rmsfield to the untested implant will lead to a large variation in the induced incident and power deposition of the implant, as a result, a larger safe margin when different coil designs (e.g. coil diameter) are considered.

A machine learning-based approach for RF transfer function modeling of active implantable medical electrodes at 3T MRI.

Objective.The objective of this work is to propose a machine learning-based approach to rapidly and efficiently model the radiofrequency (RF) transfer function of active implantable medical (AIM) electrodes, and to overcome the limitations and drawbacks of traditional measurement methods when applied to heterogeneous tissue environments.Approach.AIM electrodes with different geometries and proximate tissue distributions were considered, and their RF transfer functions were modeled numerically. Machine learning algorithms were developed and trained with the simulated transfer function datasets for homogeneous and heterogeneous tissue distributions. The performance of the method was analyzed statistically and validated experimentally and numerically. A comprehensive uncertainty analysis was performed and uncertainty budgets were derived.Main results.The proposed method is able to predict the RF transfer function of AIM electrodes under different tissue distributions, with mean correlation coefficientsrof 0.99 and 0.98 for homogeneous and heterogeneous environments, respectively. The results were successfully validated by experimental measurements (e.g. the uncertainty of less than 0.9 dB) and numerical simulation (e.g. transfer function uncertainty <1.6 dB and power deposition uncertainty <1.9 dB). Up to 1.3 dBin vivopower deposition underestimation was observed near generic pacemakers when using a simplified homogeneous tissue model.Significance.Provide an efficient alternative of transfer function modeling, which allows a more realistic tissue distribution and the potential underestimation ofin vivoRF-induced power deposition near the AIM electrode can be reduced.

Classification of chinese fragrant rapeseed oil based on sensory evaluation and gas chromatography-olfactometry.

Fragrant rapeseed oils and traditional pressed oils are increasingly popular in China owing to their sensory advantages. Many fragrant rapeseed oils are labeled by different fragrance types; however, the scientific basis for these differences is lacking. To identify the distinctive aroma and achieve fragrance classification, the sensory characteristics and aroma components of nine different fragrant rapeseed oils were analyzed via sensory evaluation and gas-chromatography-mass spectrometry-olfactometry. A total of 35 aroma compounds were found to contribute to the overall aroma. By using chemometrics methods, rapeseed oils were categorized into three fragrance styles: "strong fragrance," "umami fragrance," and "delicate fragrance." In total, 10 aroma compounds were predicted to be the most effective compounds for distinguishing sensory characteristics of fragrant rapeseed oil. According to our results, this approach has excellent potential for the fragrance classification and quality control of rapeseed oil.

Sensory lexicon and aroma volatiles analysis of brewing malt.

Malt is an important raw material in brewing beer. With the increasing development of craft beer, brewing malt has contributed diverse colours and abundant flavours to beer. While "malty" and "worty" were commonly used to describe the malt flavour of beer, they are still inadequate. This study focused on developing of a sensory lexicon and a sensory wheel for brewing malt. Here, a total of 22 samples were used for sensory evaluation. The panels identified 53 attributes to form the lexicon of brewing malt, including appearance, flavour, taste, and mouthfeel. After consulting with the experts from the brewing industry, 46 attributes were selected from the lexicon list to construct the sensory wheel. Based on the lexicon, rate-all-that-apply analysis was used to discriminate between six samples of different malt types. The principal component analysis results showed that malt types were significantly correlated with sensory features. To further understand the chemical origin of sensory attributes, partial least squares regression analysis was used to determine the association between the aroma compounds and sensory attributes. According to the colour range and malt types, 18 samples were used for sensory descriptive analysis and volatile compounds identification. Seven main flavours were selected from the brewing malt sensory wheel. 34 aroma compounds were identified by headspace solid phase microextraction gas chromatography-mass spectrometry-olfactometry. According to the partial least squares regression results, the aroma compounds were highly correlated with the sensory attributes of the brewing malt. This approach may have practical applications in the sensory studies of other products.

Exposure Optimization Trial for Patients With Medical Implants During MRI Exposure: Balance Between the Completeness and Efficiency.

Elongated conductors, such as pacemaker leads, can couple to the MRI radio-frequency (RF) field during MRI scan and cause dangerous tissue heating. By selecting proper RF exposure conditions, the RF-induced power deposition can be suppressed. As the RF-induced power deposition is a complex function of multiple clinical factors, the problem remains how to perform the exposure selection in a comprehensive and efficient way. The purpose of this work is to demonstrate an exposure optimization trail that allows a comprehensive optimization in an efficient and traceable manner. The proposed workflow is demonstrated with a generic 40 cm long cardio pacemaker, major components of the clinical factors are decoupled from the redundant data set using principle component analysis, the optimized exposure condition can not only reduce the in vivo power deposition but also maintain good image quality.

Identification of characteristic aroma components of butter from Chinese butter hotpot seasoning.

The potent aroma compounds in butter from four kinds of Chinese butter hotpot seasoning were first investigated by analyzing the isolates from solid-phase microextraction. A total of 49 aroma compounds were identified, and 23 of them were highly correlated with the aroma profiles of the butter by partial least squares regression analysis. Aroma extract dilution analysis and odor activity value calculations were applied to further reveal the dominant odorants. Fifty-three odorants with flavor dilution factors between 1 and 1024 were identified and OAVs of 17 odorants were greater than 1. Finally, an aroma recombination experiment was prepared by mixing the aroma-active compounds (OAVs > 1), and the aroma profile of the recombination showed good agreement with that of the original sample. Omission tests showed that 2-furfurylthiol, 2-acetylthiazole, anethole, (E)-2-decenal, and 1,8-cineole were the key odorants for the overall aroma of butter.