scholarly journals Quality Control of Structural MRI Images Applied Using FreeSurfer—A Hands-On Workflow to Rate Motion Artifacts

2016 ◽  
Vol 10 ◽  
Author(s):  
Lea L. Backhausen ◽  
Megan M. Herting ◽  
Judith Buse ◽  
Veit Roessner ◽  
Michael N. Smolka ◽  
...  
Keyword(s):  
Quality Control ◽  
Structural Mri ◽  
Motion Artifacts ◽  
Hands On

Hands-On Laboratory Class for Biopharmaceutical pDNA Quality Control

2022 ◽  
Author(s):  
Ângela Sousa ◽  
Ana Margarida Almeida ◽  
Joana Valente ◽  
João Queiroz ◽  
Fani Sousa
Keyword(s):  
Quality Control ◽  
Laboratory Class ◽  
Hands On

scholarly journals Creating And Working With Control Charts In Excel

2016 ◽  
Vol 10 (1) ◽  
pp. 23-44
Author(s):  
Paul F. Schikora
Keyword(s):  
Distance Education ◽  
Quality Control ◽  
Control Charts ◽  
Theoretical Basis ◽  
Effective Control ◽  
Interactive Mode ◽  
Quality Control Charts ◽  
Hands On ◽  
Problem Data ◽  
The Web

With the growth in distance education offerings, instructors who now teach quantitative material via the web have been faced with many challenges.  Foremost has been the need to develop appropriate methods for teaching such material to students who are not physically in the classroom.  Methodologies that have traditionally been taught in a highly interactive mode in the classroom must now be presented effectively in a far more asynchronous environment.  Tutorials and detailed handouts are one way to accomplish this. We present a written tutorial for creating quality control charts using Excel.  The tutorial guides students through the process of creating X-bar and R charts in such a way as to reinforce the theoretical basis of quality control already taught.  Students apply their knowledge in hands-on activity, learn how to improve Excel’s default charts to create visually effective control charts, and learn to reuse/recycle their work to easily create additional charts for different sets of problem data.

scholarly journals Is it Time to Switch Your T1W Sequence? Assessing the Impact of Prospective Motion Correction on the Reliability and Quality of Structural Imaging

2019 ◽  
Author(s):  
Lei Ai ◽  
R. Cameron Craddock ◽  
Nim Tottenham ◽  
Jonathan P Dyke ◽  
Ryan Lim ◽  
...  
Keyword(s):  
Quality Control ◽  
Motion Correction ◽  
Intraclass Correlation ◽  
Brain Network ◽  
Motion Artifacts ◽  
Head Motion ◽  
Morphometric Measurements ◽  
Human Connectome Project ◽  
Quality Control Metrics ◽  
The Impact

AbstractNew large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N=348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N=71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.

scholarly journals Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

GigaScience ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Nikhil Bhagwat ◽  
Amadou Barry ◽  
Erin W Dickie ◽  
Shawn T Brown ◽  
Gabriel A Devenyi ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Biological Effects ◽  
Software Tool ◽  
Structural Mri ◽  
Control Procedure ◽  
Comparative Performance ◽  
Human Connectome Project ◽  
The Individual ◽  
The Impact

Abstract Background The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Methods Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. Results Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. Conclusions This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.

scholarly journals Resource Modelling for the QC Laboratory at XYZ Pharmaceuticals in Southern Africa

2021 ◽  
Author(s):  
Ngonidzashe Portia Munhuweyi ◽  
Zita Ekeocha ◽  
Stephen Robert Byrn ◽  
Kari L Clase
Keyword(s):  
Quality Control ◽  
Southern Africa ◽  
Cost Effective ◽  
Calculation Model ◽  
Resource Model ◽  
Product Families ◽  
Hands On ◽  
Drug Manufacturing ◽  
Critical Components ◽  
Resource Modelling

Quality control (QC) laboratories are critical components in drug manufacturing and running them efficiently contributes to better, consistent supply of cost-effective quality products, while also and preventing deaths due to untimely delivery or unavailability of medicines. Having a resource modelling tool to estimate resources needed to handle a particular demand in a given system is essential for efficient running of QC laboratory. This study was done to establish such a model at XYZ Pharmaceuticals. The list of all products manufactured by XYZ Pharmaceuticals Southern Africa was reviewed; and product families for all products were identified. Analysts’ hands on time (HOT) to process one sample of each of the product families was estimated. The number of analysts required to support the workload at XYZ Pharmaceuticals was calculated using the HOTs for the different product families and the Maslaton’s Calculation Model. A baseline resource model was established.

The Instructional SEM Laboratory at Iowa State University

1996 ◽  
Vol 54 ◽  
pp. 396-397
Author(s):  
L. S. Chumbley ◽  
M. Meyer ◽  
K. Fredrickson ◽  
F.C. Laabs
Keyword(s):  
Materials Science ◽  
Computer Network ◽  
Energy Dispersive Spectrometer ◽  
Computer Control ◽  
State University ◽  
Iowa State University ◽  
Internet Server ◽  
Schematic Layout ◽  
Hands On ◽  
Improve Instruction

The Materials Science Department at Iowa State University has developed a laboratory designed to improve instruction in the use of the scanning electron microscope (SEM). The laboratory makes use of a computer network and a series of remote workstations in a classroom setting to provide students with increased hands-on access to the SEM. The laboratory has also been equipped such that distance learning via the internet can be achieved.A view of the laboratory is shown in Figure 1. The laboratory consists of a JEOL 6100 SEM, a Macintosh Quadra computer that acts as a server for the network and controls the energy dispersive spectrometer (EDS), four Macintosh computers that act as remote workstations, and a fifth Macintosh that acts as an internet server. A schematic layout of the classroom is shown in Figure 2. The workstations are connected directly to the SEM to allow joystick and computer control of the microscope. An ethernet connection between the Quadra and the workstations allows students seated there to operate the EDS. Control of the microscope and joystick is passed between the workstations by a switch-box assembly that resides at the microscope console. When the switch-box assembly is activated a direct serial line is established between the specified workstation and the microscope via the SEM’s RS-232.

Students Involvement in Multilingual and Multicultural Community Services

2015 ◽  
Vol 22 (2) ◽  
pp. 77-89 ◽  
Author(s):  
Ying-Chiao Tsao
Keyword(s):  
Service Learning ◽  
Cultural Competence ◽  
Linguistically Diverse ◽  
Community Services ◽  
Cultural Humility ◽  
Self Assessment ◽  
Multicultural Community ◽  
Raising Awareness ◽  
Hands On ◽  
Teaching Learning

Promoting cultural competence in serving diverse clients has become critically important across disciplines. Yet, progress has been limited in raising awareness and sensitivity. Tervalon and Murray-Garcia (1998) believed that cultural competence can only be truly achieved through critical self-assessment, recognition of limits, and ongoing acquisition of knowledge (known as “cultural humility”). Teaching cultural humility, and the value associated with it remains a challenging task for many educators. Challenges inherent in such instruction stem from lack of resources/known strategies as well as learner and instructor readiness. Kirk (2007) further indicates that providing feedback on one's integrity could be threatening. In current study, both traditional classroom-based teaching pedagogy and hands-on community engagement were reviewed. To bridge a gap between academic teaching/learning and real world situations, the author proposed service learning as a means to teach cultural humility and empower students with confidence in serving clients from culturally/linguistically diverse backgrounds. To provide a class of 51 students with multicultural and multilingual community service experience, the author partnered with the Tzu-Chi Foundation (an international nonprofit organization). In this article, the results, strengths, and limitations of this service learning project are discussed.

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