Quality Control Protocols Research Articles

Does the Relationship between Age and Brain Structure Differ in Youth with Conduct Disorder?

Conduct disorder (CD) is characterised by persistent antisocial and aggressive behaviour and typically emerges in childhood or adolescence. Although several authors have proposed that CD is a neurodevelopmental disorder, very little evidence is available about brain development in this condition. Structural brain alterations have been observed in CD, and some indirect evidence for delayed brain maturation has been reported. However, no detailed analysis of age-related changes in brain structure in youth with CD has been conducted. Using cross-sectional MRI data, this study aimed to explore differences in brain maturation in youth with CD versus healthy controls to provide further understanding of the neurodevelopmental processes underlying CD. 291 CD cases (153 males) and 379 healthy controls (160 males) aged 9–18 years (Mage = 14.4) were selected from the European multisite FemNAT-CD study. Structural MRI scans were analysed using surface-based morphometry followed by application of the ENIGMA quality control protocols. An atlas-based approach was used to investigate group differences and test for group-by-age and group-by-age-by-sex interactions in cortical thickness, surface area and subcortical volumes. Relative to healthy controls, the CD group showed lower surface area across frontal, temporal and parietal regions as well as lower total surface area. No significant group-by-age or group-by-age-by-sex interactions were observed on any brain structure measure. These findings suggest that CD is associated with lower surface area across multiple cortical regions, but do not support the idea that CD is associated with delayed brain maturation, at least within the age bracket considered here.

Genetically determined metabolites in allergic conjunctivitis: A Mendelian randomization study

BackgroundAllergic conjunctivitis (AC) afflicts a significant portion of the global populace. Yet, its metabolic foundations remain largely unexplored. MethodsWe applied Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) to scrutinize a cohort comprising 20 958 AC cases and 356 319 controls. Data were amalgamated from the metabolomics GWAS server and the FinnGen project, under strict quality control protocols. ResultsUsing two-sample MR analysis, 486 blood metabolites were investigated in relation to AC. The IVW approach highlighted 18 metabolites as closely tied to AC risk; of these, 16 retained significance post sensitivity assessments for heterogeneity and horizontal pleiotropy. LDSC analysis, adopted to bolster our findings and negate confounders from shared genetic markers, revealed 8 metabolites with marked heritability, including: palmitate (OR = 0.614), 3-methoxytyrosine (OR = 0.657), carnitine (OR = 1.368), threonate (OR = 0.828), N-[3-(2-Oxopyrrolidin-1-yl)propyl]acetamide (OR = 1.257), metoprolol acid metabolite (OR = 0.982), oleoylcarnitine (OR = 0.635), and 2-palmitoylglycerophosphocholine (OR = 1.351). ConclusionAC is precipitated by ocular responses to environmental allergens. Our study unveils a causal link between 8 blood metabolites and AC. This insight accentuates the role of metabolites in AC onset, suggesting novel avenues for its early prediction, targeted prevention, and tailored therapeutic interventions.

A statistical approach to system suitability testing for mass spectrometry imaging.

Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]). A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary. The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning. This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

The Research Progress of the Application of Bioinformatics in the Diagnosis and Treatment of Alzheimer's Disease

AbstractAlzheimer's disease (AD) is characterized by a complex pathogenesis, limited diagnostic methods, and a lack of effective therapeutic drugs in clinical settings, posing significant challenges in modern medical research. Bioinformatics offers new perspectives for identifying key pathological biomarkers of AD, analyzing differentially expressed genes in AD, screening for effective drug targets against AD, studying the mechanisms of AD pathogenesis, and discovering novel anti-AD drugs. However, data preprocessing and statistical analysis methods in bioinformatics research can significantly impact results, and there is a lack of consistency and coordination in analysis methods across platforms and laboratories in practical studies, making it difficult to compare data between studies. Therefore, it is crucial to establish standardized operating procedures and quality control protocols, improve the reproducibility of methods across platforms, and promote data comparison between studies.

Performance evaluation of six digital mammography systems

The strong advance in digital mammography as a tool for breast cancer screening requires that the new systems be characterized. Quality control protocols describe the methodology to evaluate systems performance. Detective quantum efficiency (DQE) is the most suitable parameter for describing the imaging performance of an X-ray imaging device. The objective of this study was to characterize in terms of DQE six digital mammography systems, at different detector air kerma (DAK) levels for a variety of beam qualities. A Carestream EHR-M3 CR detector, a small field digital mammography system Siemens Opdima, and full field digital mammography systems Planmed Clarity, GE Essential, GE Pristina and GE Crystal Nova were characterized using the European Guidelines protocol. Modulation transfer function (MTF), normalized noise power spectrum (NNPS) and DQE were determined, for an extended detector air kerma range. Attenuated beam qualities (obtained by adding 2 mm Al to the corresponding beam qualities) used were 28 kV with anode/filter combination Mo/Mo for the EHR-M3, Opdima, Essential and Pristina systems; 28 kV W/Ag for the Clarity; 28 kV W/Rh for the Crystal Nova; 28 kV Mo/Rh for the EHR-M3; and 34 kV Rh/Ag for the Pristina. Two groups were identified in MTF analysis, and the group composed of Clarity, Opdima and Essential presented the highest MTF values. In NNPS analysis, three main groups were identified, in which the systems Crystal Nova and Essential presented lower and higher NNPS, respectively. Crystal Nova system also exhibited higher DQE values for low spatial frequencies, while Opdima and Clarity presented higher DQE values for medium to high spatial frequency. Agreement with data from literature indicates that the systems evaluated are operating under typical conditions. Evidence of improvement in detectors performance was found.

Evaluation of a Digital Mammography System with the MAM-162 phantom

Quality controls are performed to verify the performance status of X-ray equipment in relation to image quality and patient dose. Originally, the Laboratory of Radiation Physics and Metrology (LAF-RAM) in Nicaragua had only one phantom to evaluate mammography image quality subjectively; however, under a cooperation project with the International Atomic Energy Agency (IAEA), the MAM-162 phantom was acquired for the evaluation of physical image quality. In this work a CR type digital mammography system was evaluated using the MAM-162 phantom with adaptation to a single quality control protocol based on international standards such as the German Institute for Standardization (DIN) DIN 6868-162:2013-06, the IAEA technical document TECDOC protocol No. 1958 and the European Federation of Organizations for Medical Physics (EFOMP) mammo protocol. Among the main tests performed are the evaluation of the lateral image limit of the chest wall with satisfactory results, the automatic exposure control, the Signal-to-Noise difference ratio that complies with the tolerances, in the case Ghosting and dynamic range the results of both are satisfactory, the presence of artifacts was observed when applying variance mapping, a base value of the dynamic range was also obtained. For the dosimetry, the half value layer and the determination of the Mean Glandular Dose were evaluated, which does not comply with the tolerances. The final protocol document could be validated and implemented as part of the management and service documents of the Quality Control and Monitoring Laboratory (LCM) of LAF-RAM.

Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China

Abstract. In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s−1 yr−1 (R2 = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments.

A Retrospective Study to Evaluate the Quality Assurance of Thyroid Profile In A Tertiary Care Clinical Biochemistry Laboratory

Background: The Six Sigma methodology has become a standard in quality assessment and management, widely adopted in various industries since the mid-1980s. This methodology is applicable wherever the outcome of a process needs measurement. One area where Sigma metrics find utility is in laboratory services, particularly in evaluating the quality assurance of biochemical parameters. Aims: This study aimed to measure and analyze the sigma metrics of thyroid hormones T3, T4, and TSH within the framework of NABL accreditation (National Accreditation Board for Testing and Calibration Laboratories). Materials & Methods: The study was designed as a retrospective observational study, utilizing quality control data from the Clinical Biochemistry section spanning from January 2021 to December 2021 retrieved from laboratory records. The data considered included Bio-Rad Lyphochek Immunoassay Internal Quality Control (IQC) Control and Bio-Rad External Quality Assurance Scheme (EQAS) Immunoassay monthly program data. Calculations for Coefficient of variation (CV %), Bias %, and Total Error allowable (TEa) were made to derive the sigma metric. The formula used for sigma calculation was Sigma Σ(σ) = (TEa% - Bias%) / CV%, while the Quality Goal Index (QGI) ratio was determined using the formula QGI = Bias / 1.5CV. Results: The results categorized the assay performance based on sigma levels: >6 denoted world-class performance, 5-6 indicated excellence, 4-5 represented good performance, 3-4 was deemed acceptable, and 2-3 signified poor performance. All analytes demonstrated sigma levels below 6 (2.61-5.85) varying from poor to excellent and the QGI was below 0.8, indicating a need to enhance the precision of assays. Conclusion: The Six Sigma metric analysis serves as a benchmark tool that can aid laboratories in enhancing assay performance, formulating protocols for Internal Quality Control (IQC), and evaluating existing laboratory procedures. Keywords Six sigma, Quality Goal Index, Internal Quality Control

Characteristics and quantification of small microplastics (

Small microplastics (SMPs < 100 µm) can easily be transported over long distances far from their sources through the atmospheric pathways and reach even remote regions, including the Arctic. However, these sizes of MPs are mostly overlooked due to different analytical challenges; besides, their pathways through atmospheric depositions, such as snow depositions, are mostly unknown. The spatial variability in bulk snow samples was investigated for the first time in distinct sites (e.g., glaciers) near Ny Ålesund, the world-known northernmost permanent research settlement in the Svalbard Islands, to better comprehend the presence of SMP pollution in snow. Seasonal snow deposited over the tundra and the summits of different glaciers were also sampled. A sampling procedure was designed to obtain representative samples while minimizing plastic contamination, thanks to rigorous quality assurance and quality control protocol. SMPs’ weight (µg SMP L-1) and deposition load (mg SMPs m-2) result from being lower in the remote glaciers, where they may be subject to long-range transport. The SMPs’ minimum length was 20 µm, with the majority less than 100 µm. Regarding their size distribution, there was an increase in the size length deriving from the local input of the human presence near the scientific settlement. The presence of some polymers might be site-specific in relation to the pathways that affect their distribution at the sites studied. Also, from the snow surface layer collected at the same sites to evaluate the variability of SMPs during specific atmospheric deposition events, the results confirmed their higher weight and load in surface snow near the scientific settlement compared to the glaciers. The results will enhance the limited knowledge of the SMPs in polar atmospheric compartments and deposition processes.

SPATIOTEMPORAL PATTERNS ON THE APPEARANCE OF THE FIRST TRAPEZE INDUSTRIES IN THE LATE MESOLITHIC OF THE IBERIAN PENINSULA

ABSTRACTThe spread of trapeze industries (the creation of trapeze-shaped flint tips) during Late Mesolithic is one of the most disruptive phenomena of technological change documented in the European Prehistory. Understanding the chronological patterns of this process requires (i) a critical evaluation of stratigraphic relationship between trapeze assemblages and radiocarbon samples, and (ii) considering different levels of chronological uncertainty according to the inbuilt age of the samples and the calibration process. In this paper, we critically evaluate and analyze the radiocarbon record of the first trapeze industries in the Iberian Peninsula. A dataset of 181 radiocarbon dates from 67 sites dated to 8800–8200 cal BP was collected and evaluated following a strict data quality control protocol, from which 135 dates of 53 sites were retained and classified according to a reliability index. Then, three different phase Bayesian chronological models were created to estimate the duration of the first spread of trapezes across Iberia, considering different levels of chrono-stratigraphic resolution. We find that trapeze industries appeared in the eastern half of Iberia, over an area of 330,000 km2 between 8505–8390 and 8425–8338 cal BP, spanning 0–85 yr (95.4% CI). When the oldest evidence of trapezes from Portugal are considered, the probability distribution expands (8943–8457 and 8686–7688 cal BP), due to the chronological uncertainty of human samples with marine diet and regional ΔR values applied. For the eastern half of Iberia, the current evidence indicates a very rapid spread of trapeze industries initiated in the Central-Western Pyrenees, suggesting cultural diffusion within Mesolithic social networks as the main driving mechanism.

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition.

Hyperpolarized (HP) xenon magnetic resonance imaging (129Xe MRI) is a recently federal drug administration (FDA)-approved imaging modality that produces high-resolution images of an inhaled breath of xenon gas for investigation of lung function. However, implementing 129Xe MRI is uniquely challenging as it requires specialized hardware and equipment for hyperpolarization, procurement of xenon imaging coils and coil software, development and compilation of multinuclear MR imaging sequences, and reconstruction/analysis of acquired data. Without proper expertise, these tasks can be daunting, and failure to acquire high-quality images can befrustrating, and expensive. Here, we present some quality control (QC) protocols, troubleshooting practices, and helpful tools for129Xe MRI sites, which may aid in the acquisition of optimized, high-quality data and accurate results. The discussion will begin with an overview of the process for implementing HP 129Xe MRI, including requirements for a hyperpolarizer lab, the combination of 129Xe MRI coil hardware/software, data acquisition and sequence considerations, data structures, k-space and image properties, and measured signal and noise characteristics. Within each of these necessary steps lies opportunities for errors, challenges, and unfavorable occurrences leading to poor image quality or failed imaging, and this presentation aims to address some of the more commonly encountered issues. In particular, identification and characterization of anomalous noise patterns in acquired data are necessary to avoid image artifacts and low-quality images; examples will be given, and mitigation strategies will be discussed. We aim to make the 129Xe MRI implementation process easier for new sites while providing some guidelines and strategies for real-time troubleshooting.

PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia

Western Patagonia (40–56°S) is a clear example of how the systematic lack of publicly available data and poor quality control protocols have hindered further hydrometeorological studies. To address these limitations, we present PatagoniaMet (PMET), a compilation of ground-based hydrometeorological data (PMET-obs; 1950–2020), and a daily gridded product of precipitation and temperature (PMET-sim; 1980–2020). PMET-obs was developed considering a 4-step quality control process applied to 523 hydrometeorological time series obtained from eight institutions in Chile and Argentina. Following current guidelines for hydrological datasets, several climatic and geographic attributes were derived for each catchment. PMET-sim was developed using statistical bias correction procedures, spatial regression models and hydrological methods, and was compared against other bias-corrected alternatives using hydrological modelling. PMET-sim was able to achieve Kling-Gupta efficiencies greater than 0.7 in 72% of the catchments, while other alternatives exceeded this threshold in only 50% of the catchments. PatagoniaMet represents an important milestone in the availability of hydro-meteorological data that will facilitate new studies in one of the largest freshwater ecosystems in the world.

Multilabel SegSRGAN-A framework for parcellation and morphometry of preterm brain in MRI.

Magnetic resonance imaging (MRI) is a powerful tool for observing and assessing the properties of brain tissue and structures. In particular, in the context of neonatal care, MR images can be used to analyze neurodevelopmental problems that may arise in premature newborns. However, the intrinsic properties of newborn MR images, combined with the high variability of MR acquisition in a clinical setting, result in complex and heterogeneous images. Segmentation methods dedicated to the processing of clinical data are essential for obtaining relevant biomarkers. In this context, the design of quality control protocols for the associated segmentation is a cornerstone for guaranteeing the accuracy and usefulness of these inferred biomarkers. In recent work, we have proposed a new method, SegSRGAN, designed for super-resolution reconstruction and segmentation of specific brain structures. In this article, we first propose an extension of SegSRGAN from binary segmentation to multi-label segmentation, leading then to a partitioning of an MR image into several labels, each corresponding to a specific brain tissue/area. Secondly, we propose a segmentation quality control protocol designed to assess the performance of the proposed method with regard to this specific parcellation task in neonatal MR imaging. In particular, we combine scores derived from expert analysis, morphometric measurements and topological properties of the structures studied. This segmentation quality control can enable clinicians to select reliable segmentations for clinical analysis, starting with correlations between perinatal risk factors, regional volumes and specific dimensions of cognitive development. Based on this protocol, we are investigating the strengths and weaknesses of SegSRGAN and its potential suitability for clinical research in the context of morphometric analysis of brain structure in preterm infants, and to potentially design new biomarkers of neurodevelopment. The proposed study focuses on MR images from the EPIRMEX dataset, collected as part of a national cohort study. In particular, this work represents a first step towards the design of 3-dimensional neonatal brain morphometry based on segmentation. The (free and open-source) code of multilabel SegSRGAN is publicly available at the following URL: https://doi.org/10.5281/zenodo.12659424.

Preparation and characterization of inactivated tick-borne encephalitis virus samples for single-particle imaging at the European XFEL.

X-ray imaging of virus particles at the European XFEL could eventually allow their complete structures to be solved, potentially approaching the resolution of other structural virology methods. To achieve this ambitious goal with today's technologies, about 1 ml of purified virus suspension containing at least 1012 particles per millilitre is required. Such large amounts of concentrated suspension have never before been obtained for enveloped viruses. Tick-borne encephalitis virus (TBEV) represents an attractive model system for the development of enveloped virus purification and concentration protocols, given the availability of large amounts of inactivated virus material provided by vaccine-manufacturing facilities. Here, the development of a TBEV vaccine purification and concentration scheme is presented combined with a quality-control protocol that allows substantial amounts of highly concentrated non-aggregated suspension to be obtained. Preliminary single-particle imaging experiments were performed for this sample at the European XFEL, showing distinct diffraction patterns.

Quality Control Assessment of Human Adipose-Derived Hydrogels.

Decellularized human-adipose tissue (hDAT) can serve as an alternative to two-dimensional monolayer culture and current ECM hydrogels due to its unlimited availability and cytocompatibility. A major hurdle in the clinical translation and integration of hDAT and other hydrogels into current in vitro culture processes is adherence to current good manufacturing practices (cGMP). Transferring of innovative technologies, including hydrogels, requires the establishing standardized protocols for quality assurance and quality control (QA/QC) of the material.Integration of basic characterization techniques, including physiochemical characterization, structural/morphological characterization, thermal and mechanical characterization, and biological characterization, in addition to the reduction of batch-to-batch variability and establishment of proper sterilization, storage, and fabrication processes verifies the integrity of the hydrogel. Obatala Sciences has established a characterization protocol that involves a series of assays including the evaluation of gelation properties, protein content, glycosaminoglycan content, soluble collagen content, and DNA content of hDAT.

Intranasally Delivered Adenoviral Vector Protects Chickens against Newcastle Disease Virus: Vaccine Manufacturing and Stability Assessments for Liquid and Lyophilized Formulations.

Newcastle disease (ND) remains a critical disease affecting poultry in sub-Saharan Africa. In some countries, repeated outbreaks have a major impact on local economies and food security. Recently, we developed an adenovirus-vectored vaccine encoding the Fusion protein from an Ethiopian isolate of Newcastle disease virus (NDV). The adenoviral vector was designed, and a manufacturing process was developed in the context of the Livestock Vaccine Innovation Fund initiative funded by the International Development Research Centre (IDRC) of Canada. The industrially relevant recombinant vaccine technology platform is being transferred to the National Veterinary Institute (Ethiopia) for veterinary applications. Here, a manufacturing process using HEK293SF suspension cells cultured in stirred-tank bioreactors for the vaccine production is proposed. Taking into consideration supply chain limitations, options for serum-free media selection were evaluated. A streamlined downstream process including a filtration, an ultrafiltration, and a concentration step was developed. With high volumetric yields (infectious titers up to 5 × 109 TCID50/mL) in the culture supernatant, the final formulations were prepared at 1010 TCID50/mL, either in liquid or lyophilized forms. The liquid formulation was suitable and safe for mucosal vaccination and was stable for 1 week at 37 °C. Both the liquid and lyophilized formulations were stable after 6 months of storage at 4 °C. We demonstrate that the instillation of the adenoviral vector through the nasal cavity can confer protection to chickens against a lethal challenge with NDV. Overall, a manufacturing process for the adenovirus-vectored vaccine was developed, and protective doses were determined using a convenient route of delivery. Formulation and storage conditions were established, and quality control protocols were implemented.

Automated FreeSurfer segmentation and visual quality control in 10,000 MRI scans from a large memory clinic cohort

AbstractBackgroundAutomated image segmentation methods together with increasing computer power allow quantifying brain alterations in great detail in large neuroimaging datasets. The increasing size of data complicates visual quality control (QC), which is the gold standard. Increasingly often, research relies on automated QC methods. Here, we aimed to investigate the performance of automated FreeSurfer segmentation through visual QC in a large memory clinic cohort with more than ten thousand images, and to compare an automated QC measure to the visual QC.Method10,400 T1‐weighted MRI scans from the Amsterdam Dementia Cohort were segmented with FreeSurfer(v7.1). Quality control was performed using an adapted version of the Enigma QC protocol[https://doi.org/10.1038/ng.2250]. Twenty‐six individuals visually assessed cortical segmentations, rating each as “fail”, “moderate”, or “pass”, identifying failure reasons and affected lobes. We investigated the error occurrence in failed or moderate quality scans, and whether segmentation failures depend on clinical diagnosis in a subset of 4990 baseline scans with most common diagnoses (i.e., SCD, MCI, AD, FTD, VaD and DLB). We compared an automated QC measure (i.e. median ± 3*IQR thresholded SurfaceHoles) with the visual QC output.ResultThe majority (78.3%) of 10,400 cortical segmentations were rated as having “pass”‐quality, 16.2% were “moderate”‐quality and 5.4% segmentations were rated as “fail”. Concordance between Automated QC and Visual QC was high for pass ratings(84%), but low for fail ratings(22.5%) (Table‐1). Within failed segmentations, most common reasons were processing errors(51.7%), image artifacts(14.3%) and underestimation(13.3%); for moderate ratings reasons were inclusion of meninges (47.8%) and underestimation of cortical thickness(33.2%) (Figure‐1). Stratified per diagnosis, segmentation failed in 2.3% of the SCD scans, 3% of MCI, 4.7% of DLB, 5.4% of AD, 7% of FTD and 14.5% of VaD. Further, we observed variation in affected lobes among diagnostic groups (Figure‐2b).ConclusionThe majority of scans passed visual QC, in high concordance with the automated QC. Images with moderate or failed quality, occurred more often in VaD and FTD. The most common mis‐segmentations were overestimation and underestimation of cortical thickness. This very large visually quality controlled data could be used as a benchmark to test future automated QC pipelines on.

Automated FreeSurfer segmentation and visual quality control in 10,000 MRI scans from a large memory clinic cohort

AbstractBackgroundAutomated image segmentation methods together with increasing computer power allow quantifying brain alterations in great detail in large neuroimaging datasets. The increasing size of data complicates visual quality control (QC), which is the gold standard. Increasingly often, research relies on automated QC methods. Here, we aimed to investigate the performance of automated FreeSurfer segmentation through visual QC in a large memory clinic cohort with more than ten thousand images, and to compare an automated QC measure to the visual QC.Method10,400 T1‐weighted MRI scans from the Amsterdam Dementia Cohort were segmented with FreeSurfer(v7.1). Quality control was performed using an adapted version of the Enigma QC protocol[https://doi.org/10.1038/ng.2250]. Twenty‐six individuals visually assessed cortical segmentations, rating each as “fail”, “moderate”, or “pass”, identifying failure reasons and affected lobes. We investigated the error occurrence in failed or moderate quality scans, and whether segmentation failures depend on clinical diagnosis in a subset of 4990 baseline scans with most common diagnoses (i.e., SCD, MCI, AD, FTD, VaD and DLB). We compared an automated QC measure (i.e. median ± 3*IQR thresholded SurfaceHoles) with the visual QC output.ResultThe majority (78.3%) of 10,400 cortical segmentations were rated as having “pass”‐quality, 16.2% were “moderate”‐quality and 5.4% segmentations were rated as “fail”. Concordance between Automated QC and Visual QC was high for pass ratings(84%), but low for fail ratings(22.5%) (Table‐1). Within failed segmentations, most common reasons were processing errors(51.7%), image artifacts(14.3%) and underestimation(13.3%); for moderate ratings reasons were inclusion of meninges (47.8%) and underestimation of cortical thickness(33.2%) (Figure‐1). Stratified per diagnosis, segmentation failed in 2.3% of the SCD scans, 3% of MCI, 4.7% of DLB, 5.4% of AD, 7% of FTD and 14.5% of VaD. Further, we observed variation in affected lobes among diagnostic groups (Figure‐2b).ConclusionThe majority of scans passed visual QC, in high concordance with the automated QC. Images with moderate or failed quality, occurred more often in VaD and FTD. The most common mis‐segmentations were overestimation and underestimation of cortical thickness. This very large visually quality controlled data could be used as a benchmark to test future automated QC pipelines on.

Leaning out the Small Component Paint Process: Using Lean Six Sigma for Process Improvement at Tobyhanna Army Depot

TYAD repairs and refurbishes a variety of Department of Defense systems that are essential for our military effectiveness and daily operations. Employees at TYAD have noticed a tall number of overrun hours for projects in their small component paint branch. These high number of overrun hours have led to TYAD steadily losing revenue and profitability on their projects. This capstone group utilized Lean Six Sigma’s (LSS) DMAIC (Define, Measure, Analyze, Improve, and Control) process to decrease the number of overrun hours experienced and hopefully allow a way for greater throughput to go through the system. The efforts of this group led to discoveries of issues with the implementation of quality control tools. Our discoveries led to a revamped quality control protocol that will decrease the number of overrun hours experienced by TYAD and ultimately save them an estimated $478,000.00 over the course of the next three years.

Large-scale Normative Modeling of Brain Microstructure.

Normative models of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in patients with dementia, psychiatric, or developmental conditions. Here we present the first large-scale normative model of the brain's white matter (WM) microstructure derived from 18 international diffusion MRI (dMRI) datasets covering almost the entire lifespan (totaling N=51,830 individuals; age: 3-80 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol, and used Hierarchical Bayesian Regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex, while modeling the site effect. HBR overcomes known weaknesses of some data harmonization methods that simply scale and shift residual distributions at each site. To illustrate the method, we applied it to detect and visualize profiles of WM microstructural deviations in cohorts of patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease and in carriers of 22q11.2 copy number variants, a rare neurogenetic condition that confers increased risk for psychosis. The resulting large-scale model offers a common reference to identify disease effects in individuals or groups, as well as to compare disorders and discover factors that influence these abnormalities.