Polynomial fitting method of background correction for electron backscatter diffraction patterns

A raw electron backscatter diffraction (EBSD) signal can be empirically decomposed into a Kikuchi diffraction pattern and a smooth background. For pattern indexing, the latter is generally undesirable but can reveal topographical, compositional, or diffraction contrast. In this study, we proposed a new background correction method using polynomial fitting (PF) algorithm to obtain clear Kikuchi diffraction patterns for some applications in nonconductive materials due to coating problems, at low accelerated voltage and at rough sample surfaces and for the requirement of high pattern quality in HR-EBSD. To evaluate the quality metrics of the Kikuchi patterns, we initially used three indices, namely, pattern quality, Tenengrad variance, and spatial–spectral entropy-based quality to detect the clarity, contrast, and noise of Kikuchi patterns obtained at 5 and 15 kV. Then, we examined the performance of PF method by comparing it with pattern averaging and Fourier transform-based methods. Finally, this PF background correction is demonstrated to extract the background images from the blurred diffraction patterns of EBSD measurements at low kV accelerating voltage and with coating layer, and to provide clear Kikuchi patterns successfully.

A framework for multiplex imaging optimization and reproducible analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, jinxif, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

Analyzing the Estrogen Receptor Status of Liver Metastases with [18F]-FES-PET in Patients with Breast Cancer

Background: Positron emission tomography (PET) with 16α-[18F]-fluoro-17β-estradiol ([18F]-FES) can visualize estrogen receptor (ER) expression, but it is challenging to determine the ER status of liver metastases, due to high physiological [18F]-FES uptake. We evaluated whether [18F]-FES-PET can be used to determine the ER status of liver metastases, using corresponding liver biopsies as the gold standard. Methods: Patients with metastatic breast cancer (n = 23) were included if they had undergone a [18F]-FES-PET, liver metastasis biopsy, CT-scan, and [18F]-FDG-PET. [18F]-FES-PET scans were assessed by visual and quantitative analysis, tracer uptake was correlated with ER expression measured by immunohistochemical staining and the effects of region-of-interest size and background correction were determined. Results: Visual analysis allowed ER assessment of liver metastases with 100% specificity and 18% sensitivity. Quantitative analysis improved the sensitivity. Reduction of the region-of-interest size did not further improve the results, but background correction improved ER assessment, resulting in 83% specificity and 77% sensitivity. Using separate thresholds for ER+ and ER– metastases, positive and negative predictive values of 100% and 75%, respectively, could be obtained, although 30% of metastases remained inconclusive. Conclusion: In the majority of liver metastases, ER status can be determined with [18F]-FES-PET if background correction and separate thresholds are applied.

Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

Background Since three-dimensional segmentation of cardiac region in 123I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with 123I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. Methods We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late 123I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of 123I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. Results The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R2 = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R2 = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. Conclusions The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.

PIIKA 2.5: Enhanced quality control of peptide microarrays for kinome analysis

Peptide microarrays consisting of defined phosphorylation target sites are an effective approach for high throughput analysis of cellular kinase (kinome) activity. Kinome peptide arrays are highly customizable and do not require species-specific reagents to measure kinase activity, making them amenable for kinome analysis in any species. Our group developed software, Platform for Integrated, Intelligent Kinome Analysis (PIIKA), to enable more effective extraction of meaningful biological information from kinome peptide array data. A subsequent version, PIIKA2, unveiled new statistical tools and data visualization options. Here we introduce PIIKA 2.5 to provide two essential quality control metrics and a new background correction technique to increase the accuracy and consistency of kinome results. The first metric alerts users to improper spot size and informs them of the need to perform manual resizing to enhance the quality of the raw intensity data. The second metric uses inter-array comparisons to identify outlier arrays that sometimes emerge as a consequence of technical issues. In addition, a new background correction method, background scaling, can sharply reduce spatial biases within a single array in comparison to background subtraction alone. Collectively, the modifications of PIIKA 2.5 enable identification and correction of technical issues inherent to the technology and better facilitate the extraction of meaningful biological information. We show that these metrics demonstrably enhance kinome analysis by identifying low quality data and reducing batch effects, and ultimately improve clustering of treatment groups and enhance reproducibility. The web-based and stand-alone versions of PIIKA 2.5 are freely accessible at via http://saphire.usask.ca.

Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon (BC) in human exposure characterization and mobile air quality monitoring. However, the field lacks information about this instrument's performance under various settings. This study evaluated the real-time performance of the MA200 in an urban area, Augsburg, Germany. Noise reduction and negative value mitigation were explored using different data processing methods: local polynomial regression (LPR), optimized noise reduction averaging (ONA), and centered moving average (CMA) under different interval time (5 s, 10 s, and 30 s). After noise reduction, the data were evaluated and compared by (1) the relative number of negative values; (2) more detailed microenvironmental change information retained after noise reduction; (3) the reduction of the peak values and number of peak samples; (4) more detailed microenvironmental change retained after the background correction. Our results showed that CMA showed a good prospect to analyze the raw BC concentration data in terms of the interval time due to its proportions of negative values and the detail microenvironmental change. Moreover, the CMA method has the highest reduction peak values and the number of peak samples compared to ONA and LPR. Furthermore, after background correction, the CMA treatment results remained more detailed microenvironmental changes in pollutants than others. Therefore, based on a comprehensive comparison, CMA offered a good approach to post-process the raw BC concentration data. These findings provide new insight for the noise reduction approach that applied in mobile monitoring campaign using BC instruments.