Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS

BACKGROUND

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time.

PURPOSE

We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS.

METHODS

The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising.

RESULTS

With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels.

CONCLUSIONS

The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

Dual Mass Spectrometric Tissue Imaging of Nanocarrier Distributions and Their Biochemical Effects

Nanomaterial-based drug delivery vehicles are able to deliver therapeutics in a controlled, targeted manner. Currently, however, there are limited analytical methods that can detect both nanomaterial distributions and their biochemical effects concurrently. In this study, we demonstrate that matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and laser ablation inductively coupled plasma mass spectrometry imaging (LA-ICP-MSI) can be used together to obtain nanomaterial distributions and biochemical consequences. These studies employ nanoparticle-stabilized capsules (NPSCs) loaded with siRNA as a testbed. MALDI-MSI experiments on spleen tissues from intravenously injected mice indicate that NPSCs loaded with anti-TNF-α siRNA cause changes to the lipid composition in white pulp regions of the spleen, as anticipated, based on pathways known to be affected by TNF-α, whereas NPSCs loaded with scrambled siRNA do not cause the predicted changes. Interestingly, LA-ICP-MSI experiments reveal that the NPSCs primarily localize in the red pulp, suggesting that the observed changes in lipid composition are due to diffusive rather than localized effects on TNF-α production. Such information is only accessible by combining data from the two modalities, which we accomplish by using the heme signals from MALDI-MSI and iron signals from LA-ICP-MSI to overlay the images. Several unexpected changes in lipid composition also occur in regions where the NPSCs are found, suggesting that the NPSCs themselves can influence tissue biochemistry as well.