Cellulose Nanoparticles are a Biodegradable Photoacoustic Contrast Agent for Use in Living Mice.

Molecular imaging with photoacoustic ultrasound is an emerging field that combines the spatial and temporal resolution of ultrasound with the contrast of optical imaging. However, there are few imaging agents that offer both high signal intensity and biodegradation into small molecules. Here we describe a cellulose-based nanoparticle with peak photoacoustic signal at 700 nm and an in vitro limit of detection of 6 pM (0.02 mg/mL). Doses down to 0.35 nM (1.2 mg/mL) were used to image mouse models of ovarian cancer. Most importantly, the nanoparticles were shown to biodegrade in the presence of cellulase both through a glucose assay and electron microscopy

A Hybrid Least Squares and Principal Component Analysis Algorithm for Raman Spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles

Limited view tomography

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Replication Data for: Comparison of Deconvolution Filters for Photoacoustic Tomography

Raw scan data for mouse brai

Replication Data for: Comparison of Deconvolution Filters for Photoacoustic Tomography

Raw scan data for subcutaneous mouse tumo

Replication Data for: Comparison of Deconvolution Filters for Photoacoustic Tomography

Raw scan data for pencil point phanto

Replication Data for: Comparison of Deconvolution Filters for Photoacoustic Tomography

Raw scan data for vessel phanto

Replication Data for: Comparison of Deconvolution Filters for Photoacoustic Tomography

Raw scan data for dot pattern phanto