Novel algorithms for improved detection and analysis of fluorescent signal fluctuations

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. Using advanced imaging technologies, fluorescence indicators are a prerequisite for the analysis of physiological molecular signaling. Automated detection and analysis of fluorescence signals require to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection, and extraction of events themselves as well as proper segmentation of neighboring events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. Here, we present two algorithms (PBasE and CoRoDe) for accurate baseline estimation and automated detection and segmentation of fluorescence fluctuations.

Retinex-qDPC: Automatic background-rectified quantitative differential phase contrast imaging

BACKGROUND AND OBJECTIVE

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. The background mismatch reduces the experimental robustness of qDPC and largely limits its applications. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC.

METHODS

In Retinex-qDPC, we replace the data fidelity term of the previous cost function for qDPC inverse problem, by the images' edge features yielding L₂-Retinex-qDPC and L₁-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L₁-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data.

RESULTS

Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L₁-Retinex-qDPC is better than L₂-Retinex and other state-of-the-art qDPC algorithms.

CONCLUSIONS

The Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications.

protGear: A protein microarray data pre-processing suite

Protein microarrays are versatile tools for high throughput study of the human proteome, but systematic and non-systematic sources of bias constrain optimal interpretation and the ultimate utility of the data. Published guidelines to limit technical variability whilst maintaining important biological variation favour DNA-based microarrays that often differ fundamentally in their experimental design. Rigorous tools to guide background correction, the quantification of within-sample variation, normalisation, and batch correction specifically for protein microarrays are limited, require extensive investigation and are not centrally accessible.

Here, we develop a generic one-stop-shop pre-processing suite for protein microarrays that is compatible with data from the major protein microarray scanners. Our graphical and tabular interfaces facilitate a detailed inspection of data and are coupled with supporting guidelines that enable users to select the most appropriate algorithms to systematically address bias arising in customized experiments. The localization and distribution of background signal intensities determine the optimal correction strategy. A novel function overcomes the limitations in the interpretation of the coefficient of variation when signal intensities are at the lower end of the detection threshold. We demonstrate essential considerations in the experimental design and their impact on a range of algorithms for normalization and minimization of batch effects.

Our user-friendly interactive web-based platform eliminates the need for prowess in programming. The open-source R interface includes illustrative examples, generates an auditable record, enables reproducibility, and can incorporate additional custom scripts through its online repository. This versatility will enhance its broad uptake in the infectious disease and vaccine development community.

Evaluation of self-calibrated non-linear phase-contrast correction in pediatric and congenital cardiovascular magnetic resonance imaging

BACKGROUND

The need for background error correction in phase-contrast flow analysis has historically posed a challenge in cardiac magnetic resonance (MR) imaging. While previous studies have shown that phantom correction improves flow measurements, it impedes scanner workflow.

OBJECTIVE

To evaluate the efficacy of self-calibrated non-linear phase-contrast correction on flows in pediatric and congenital cardiac MR compared to phantom correction as the standard.

MATERIALS AND METHODS

We retrospectively identified children who had great-vessel phase-contrast and static phantom sequences acquired between January 2015 and June 2015. We applied a novel correction method to each phase-contrast sequence post hoc. Uncorrected, non-linear, and phantom-corrected flows were compared using intraclass correlation. We used paired t-tests to compare how closely non-linear and uncorrected flows approximated phantom-corrected flows. In children without intra- or extracardiac shunts or significant semilunar valvular regurgitation, we used paired t-tests to compare how closely the uncorrected pulmonary-to-systemic flow ratio (Qp:Qs) and non-linear Qp:Qs approximated phantom-corrected Qp:Qs.

RESULTS

We included 211 diagnostic-quality phase-contrast sequences (93 aorta, 74 main pulmonary artery [MPA], 21 left pulmonary artery [LPA], 23 right pulmonary artery [RPA]) from 108 children (median age 15 years, interquartile range 11-18 years). Intraclass correlation showed strong agreement between non-linear and phantom-corrected flow measurements but also between uncorrected and phantom-corrected flow measurements. Non-linear flow measurements did not more closely approximate phantom-corrected measurements than did uncorrected measurements for any vessel. In 39 children without significant shunting or regurgitation, mean non-linear Qp:Qs (1.07; 95% confidence interval [CI] = 1.01, 1.13) was no closer than mean uncorrected Qp:Qs (1.06; 95% CI = 1.00, 1.13) to mean phantom-corrected Qp:Qs (1.02; 95% CI = 0.98, 1.06).

CONCLUSION

Despite strong agreement between self-calibrated non-linear and phantom correction, cardiac flows and shunt calculations with non-linear correction were no closer to phantom-corrected measurements than those without background correction. However, phantom-corrected flows also demonstrated minimal differences from uncorrected flows. These findings suggest that in the current era, more accurate phase-contrast flow measurements might limit the need for background correction. Further investigation of the clinical impact and optimal methods of background correction in the pediatric and congenital cardiac population is needed.

Validation of the physiological background correction method for the suppression of the spill-in effect near highly radioactive regions in positron emission tomography

Background

Positron emission tomography (PET) imaging has a wide applicability in oncology, cardiology and neurology. However, a major drawback when imaging very active regions such as the bladder is the spill-in effect, leading to inaccurate quantification and obscured visualisation of nearby lesions. Therefore, this study aims at investigating and correcting for the spill-in effect from high-activity regions to the surroundings as a function of activity in the hot region, lesion size and location, system resolution and application of post-filtering using a recently proposed background correction technique. This study involves analytical simulations for the digital XCAT2 phantom and validation acquiring NEMA phantom and patient data with the GE Signa PET/MR scanner. Reconstructions were done using the ordered subset expectation maximisation (OSEM) algorithm. Dedicated point-spread function (OSEM+PSF) and a recently proposed background correction (OSEM+PSF+BC) were incorporated into the reconstruction for spill-in correction. The standardised uptake values (SUV) were compared for all reconstruction algorithms.

Results

The simulation study revealed that lesions within 15–20 mm from the hot region were predominantly affected by the spill-in effect, leading to an increased bias and impaired lesion visualisation within the region. For OSEM, lesion SUV_max converged to the true value at low bladder activity, but as activity increased, there was an overestimation as much as 19% for proximal lesions (distance around 15–20 mm from the bladder edge) and 2–4% for distant lesions (distance larger than 20 mm from the bladder edge). As bladder SUV increases, the % SUV change for proximal lesions is about 31% and 6% for SUV_max and SUV_mean, respectively, showing that the spill-in effect is more evident for the SUV_max than the SUV_mean. Also, the application of post-filtering resulted in up to 65% increment in the spill-in effect around the bladder edges. For proximal lesions, PSF has no major improvement over OSEM because of the spill-in effect, coupled with the blurring effect by post-filtering. Within two voxels around the bladder, the spill-in effect in OSEM is 42% (32%), while for OSEM+PSF, it is 31% (19%), with (and without) post-filtering, respectively. But with OSEM+PSF+BC, the spill-in contribution from the bladder was relatively low (below 5%, either with or without post-filtering). These results were further validated using the NEMA phantom and patient data for which OSEM+PSF+BC showed about 70–80% spill-in reduction around the bladder edges and increased contrast-to-noise ratio up to 36% compared to OSEM and OSEM+PSF reconstructions without post-filtering.

Conclusion

The spill-in effect is dependent on the activity in the hot region, lesion size and location, as well as post-filtering; and this is more evident in SUV_max than SUV_mean. However, the recently proposed background correction method facilitates stability in quantification and enhances the contrast in lesions with low uptake.

Electronic supplementary material

The online version of this article (10.1186/s40658-018-0233-8) contains supplementary material, which is available to authorized users.

A practical background correction method for an immediately repeated first-pass radionuclide angiography

BACKGROUND

A satisfactory bolus injection is essential for a successful first-pass radionuclide angiography (FPRNA). Rescheduling the FPRNA study is usually needed due to high background interference caused by an unsatisfactory bolus injection. We developed a protocol to correct the pre-existing background activity subsequent to immediately repeating the study.

METHODS

Seventy-four consecutive patients who had their bone scan and FPRNA scheduled on the same day were included for analysis. The initial 51 cases constituted the "validation-only" group. In the other 23 cases, the "validation plus clearance constants" group, a 5-min dynamic acquisition was performed during the 5-min equilibrium to obtain the background clearance curve and the clearance constants. For all included 74 cases ejection fraction (EF) analysis was proceeded using the images from the first injection, second injection, and second injection with the corrected background to yield EF1, EF2, and EF2', respectively. EF2 and EF2' were then compared to the ejection fraction without background interference, the EF1.

RESULTS

For the LV, the mean difference between the EF1 and the uncorrected EF2 (|LVEF1-LVEF2| in mean ± SD) was 3.1 ± 2.0% and the difference between the EF1 and the corrected EF2' (|LVEF1-LVEF2'|) was 1.6 ± 2.1%, while the mean differences for RV are 2.2 ± 1.9% and 1.8 ± 1.8%, respectively. A significant difference (p < 0.05) was observed between the uncorrected and the corrected data for both the LV and RV.

CONCLUSION

In FPRNA, when a bolus injection is immediately readministered, both LVEF and RVEF can be underestimated. With our correction method, the results are superior to those without correction.

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.

A robust background correction algorithm for forensic bloodstain imaging using mean-based contrast adjustment

Background correction algorithm (BCA) is useful in enhancing the visibility of images captured in crime scenes especially those of untreated bloodstains. Successful implementation of BCA requires all the images to have similar brightness which often proves a problem when using automatic exposure setting in a camera. This paper presents an improved background correction algorithm (BCA) that applies mean-based contrast adjustment as a pre-correction step to adjust the mean brightness of images to be similar before implementing BCA. The proposed modification, namely mean-based adaptive BCA (mABCA) was tested on various image samples captured under different illuminations such as 385 nm, 415 nm and 458 nm. We also evaluated mABCA of two wavelengths (415 nm and 458 nm) and three wavelengths (415 nm, 380 nm and 458 nm) in enhancing untreated bloodstains on different surfaces. The proposed mABCA is found to be more robust in processing images captured in different brightness and thus overcomes the main issue faced in the original BCA.

Analyzing Peptide Microarray Data with the R pepStat Package

In this chapter we demonstrate the use of R Bioconductor packages pepStat and Pviz on a set of paired peptide microarrays generated from vaccine trial data. Data import, background correction, normalization, and summarization techniques are presented. We introduce a sliding mean method for amplifying signal and reducing noise in the data, and show the value of gathering paired samples from subjects. Useful visual summaries are presented, and we introduce a simple method for setting a decision rule for subject/peptide responses that can be used with a set of control peptides or placebo subjects.

A Straightforward Approach to the Analysis of Double Electron-Electron Resonance Data

Double electron-electron resonance (DEER) is now widely utilized to measure distance distributions in the 20-70Å range. DEER is frequently applied to biological systems that have multiple conformational states leading to complex distance distributions. These complex distributions raise issues regarding the best approach to analyze DEER data. A widely used method utilizes a priori background correction followed by Tikhonov regularization. Unfortunately, the underlying assumptions of this approach can impact the analysis. In this chapter, a method of analyzing DEER data is presented that is ideally suited to obtain these complex distance distributions. The approach allows the fitting of raw experimental data without a priori background correction as well as the rigorous determination of uncertainties for all fitting parameters. This same methodological approach can be used for the simultaneous or global analysis of multiple DEER data sets using variable ratios of a common set of components, thus allowing direct correlation of distance components with functionally relevant conformational and biochemical states. Examples are given throughout to highlight this robust fitting approach.

Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments

A case study is presented to illustrate a methodology for mobile monitoring in urban environments. A dataset of UFP, PM2.5 and BC concentrations was collected. We showed that repeated mobile measurements could give insight in spatial variability of pollutants at different micro-environments in a city. Streets of contrasting traffic intensity showed increased concentrations by a factor 2-3 for UFP and BC and by <10% for PM2.5. The first quartile (P25) of the mobile measurements at an urban background zone seems to be good estimate of the urban background concentration. The local component of the pollutant concentrations was determined by background correction. The use of background correction reduced the number of runs needed to obtain representative results. The results presented, are a first attempt to establish a methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments.