Secondary Ion Mass Spectrometry (SIMS) can provide distribution images of elements and molecular fragments with high sensitivity and spatial resolution. This study aims to exploit the potential of this modality as an imaging technique for biomedical applications. A model of image generation was developed and validated on experimental SIMS images. The model allowed for the selection of standard distance deviation (SDD) and nearest neighbor index (NNI) as suitable indices for the characterization of SIMS images, as they have been associated with sample morphology. Two regression models were proposed to correlate the SDD index and NNI with an index of effectiveness and acquisition parameters. The SDD index, due to its linear relationship with the image noise parameter, was less sensitive to noise. The model was then applied to study the effect of instrumental and analytical parameters, such as pre-sputtering time, on image generation.

Gaia, V., Luca, M., Marco, M., Claudia, K., Marco, C., Magnani, A., et al. (2010). An image formation model for Secondary Ion Mass Spectrometry imaging ofbiological tissue samples. APPLIED SURFACE SCIENCE, 257(4), 1267-1275 [10.1016/j.apsusc.2010.08.046].

An image formation model for Secondary Ion Mass Spectrometry imaging ofbiological tissue samples

MARCO CONSUMI;MAGNANI, AGNESE;
2010

Abstract

Secondary Ion Mass Spectrometry (SIMS) can provide distribution images of elements and molecular fragments with high sensitivity and spatial resolution. This study aims to exploit the potential of this modality as an imaging technique for biomedical applications. A model of image generation was developed and validated on experimental SIMS images. The model allowed for the selection of standard distance deviation (SDD) and nearest neighbor index (NNI) as suitable indices for the characterization of SIMS images, as they have been associated with sample morphology. Two regression models were proposed to correlate the SDD index and NNI with an index of effectiveness and acquisition parameters. The SDD index, due to its linear relationship with the image noise parameter, was less sensitive to noise. The model was then applied to study the effect of instrumental and analytical parameters, such as pre-sputtering time, on image generation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/33624
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