The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.
The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.
BackgroundFew studies have used quantitative polymerase chain reaction (qPCR) as an approach to measure virus neutralization assay endpoints. Its lack of use may not be surprising considering that sample nucleic acid extraction and purification can be expensive, labor-intensive, and rate-limiting.MethodsVirus/antibody mixtures were incubated for one hour at 37°C and then transferred to Vero cell monolayers in a 96-well plate format. At 24 (or 48) hours post-infection, we used a commercially available reagent to prepare cell lysates amenable to direct analysis by one-step SYBR Green quantitative reverse transcription PCR using primers specific for the RSV-N gene, thereby obviating the need for cumbersome RNA extraction and purification. The neutralization titer was defined as the reciprocal of the highest dilution needed to inhibit the PCR signal by 90% when compared with the mean value observed in virus control wells in the absence of neutralizing antibodies.ResultsWe have developed a qPCR-based neutralization assay for human respiratory syncytial virus. Due to the sensitivity of qPCR in detecting virus replication, endpoints may be assessed as early as 24 hours post-infection. In addition, the dynamic range of qPCR provides a basis for the assay to be relatively robust to perturbations in input virus dose (i.e., the assay is in compliance with the Percentage Law).ConclusionsThis qPCR-based neutralization assay is suitable for automated high-throughput applications. In addition, our experimental approach may be generalizable for the rapid development of neutralization assays for other virus families.
Synopsis:The MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these quality metrics along with anonymized metadata and manual quality ratings. This new resource will allow a better understanding of the normative values and distributions of these quality metrics, help determine the relationships between image quality and metadata such as acquisition parameters and finally, provide a cost-effective, easy way to annotate the quality of a large number of cross-site MR scans.
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