Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

Effects of divergent selection for growth on egg quality traits in Japanese quail

The experiment was designed to investigate the impact of selection for increased body mass on external and internal egg quality traits of Japanese quail. Three hundred and sixty Japanese quail, divergently selected over three generations for different body mass at 4 weeks of age, were used. Quail were homogeneously divided into three groups each consisting of 120 birds: high body mass (HBM), low body mass (LBM) and Control. ANOVA was used to detect the effect of selection on egg quality. In addition, correlation between external and internal egg quality traits was measured. Our results revealed thatHBMquail laid heavier eggs (P = 0.03 compared with LBM but not significantly different with Control quail) with a higher external (shell thickness, shell weight, eggshell ratio and eggshell density, P = 0.0001) and internal egg quality score (albumen weight, P = 0.003; albumen ratio, P = 0.01; albumen height, yolk height, yolk index and Haugh unit, P = 0.0001) when compared with both the Control and LBM. The egg surface area and yolk diameter were significantly higher in HBM when compared with the LBM but not with the Control line. Egg weight was positively correlated with albumen weight (r = 0.54, P = 0.0001), albumen ratio (r = 0.14, P = 0.05), yolk height (r = 0.27, P = 0.0001), yolk weight (r = 0.23, P = 0.002), yolk diameter (r = 0.14, P = 0.05) and yolk index (r = 0.21, P = 0.005) but was negatively correlated with yolk ratio (r = –0.16, P = 0.03). Our results indicate that selection for higher body mass might result in heavier eggs and superior egg quality.