40 LONI MR images.

<p>40 LONI MR images.</p

30 IXI MR images.

<p>30 IXI MR images.</p

Dice ratios of three ROIs by three methods.

<p>Dice ratios of three ROIs by three methods.</p

The combined deformations by three methods.

<p>(A) Diffeomorphic Demons. (B) Symmetric-log-Demons. (C) our method. (D) Symmetric-log-Demons in 10 times magnification. (E) Our method in 10 times magnification.</p

Dice ratios of 83 ROIs by three methods.

<p>Dice ratios of 83 ROIs by three methods.</p

Dice ratios of 56 ROIs by three methods.

<p>(A) Dice ratios from 1st to 28th ROIs. (B) Dice ratios from 29th to 56th ROIs.</p

Fixed image and the registration results by three methods.

<p>(A) Fixed image. (B) Registration result by Diffeomorphic Demons. (C) Registration result by Symmetric-log-Demons. (D) Registration result by our method.</p

Solvent effect in polymer analysis by MALDI-TOF mass spectrometry

<p>Solvent effect is one of the important factors in sample preparation which may affect matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra of synthetic polymers. MALDI imaging, a useful imaging tool for discovering biomarkers in tissues, is applied here for better comprehension of solvent effect in polymer analysis by MALDI-TOF mass spectrometry. Nylon-6 was chosen as a model polymer for the study of solvent effect. Its MALDI mass spectra in different solvents were performed. MALDI imaging analysis was performed for studying the incorporation of analytes into matrix crystals in different solvent combinations. Specifically, the colocalization of matrix and analyte was obtained through Pearson’s correlation (PC) coefficient analysis of their MALDI images. The results demonstrated that satisfactory spectra were obtained in higher PC value conditions. PC decreased along with an increase in the ratio of poor solvent, which suggested that we should minimize the poor solvent ratio to obtain better MALDI spectra.</p

Cross-sectional views of data and registration results by three methods.

<p>(A) Cross-sectional views of moving image. (B) Cross-sectional views of fixed image. (C) Cross-sectional views of the result of Diffeomorphic Demons. (D) Cross-sectional views of the result of Symmetric-log-Demons. (E) Cross-sectional views of the result of our method.</p