Report on a multicenter fMRI quality assurance protocol

By L. Friedman, Gary H. Glover. Journal of Magnetic Resonance Imaging, 2006

388 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9ec6f2591a57c0637eec7cdc4a9b8e4853a073d4

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Human Connectome Project Informatics: quality control, database services, and data visualization (2013, methodology): “These data are used to assess scanner stability and are automatically processed within the IntraDB using the BIRN Agar Phantom QA tool (fmriqa_phantomqa.pl 1 , using a 30x30 voxel ROI in the center of the middle slice of the volume) (Friedman and Glover, 2006).”
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry (2012, background): “Without monitoring and ongoing open error reporting, errors will be perpetuated and their impact potentially magnified (Friedman and Glover, 2006).”
Test-retest reliability of evoked BOLD signals from a cognitive-emotive fMRI test battery (2012, methodology): “Quality assurance (QA) measures were conducted on every measurement day according to an established QA protocol (Friedman and Glover, 2006) quantifying scanner magnet stability using a phantom.”
Intermediate phenotypes and genetic mechanisms of psychiatric disorders (2006, background): “For the field as a whole, reaching consensus on questions of standardization of tasks and comparability of data across centre”
Using high-resolution quantitative mapping of R1 as an index of cortical myelination (2014, positive): “standards for fMRI (Friedman and Glover, 2006) will improve the sensitivity of qMRI in multi-center studies (Levesque et al.”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, background): “In a similar vein, Friedman and Glover (2006) explore an interesting set of quality metrics, but they focus on stability, signal-to-noise ratio (SNR), drift, and other hardware performance issues related to MR scanners, not speciﬁcally on the type of artefacts 2 that can be found in MR imaging even…”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, neutral): “Acquisition-related variations Scanner make and model (Friedman and Glover, 2006b), sequence vs. conventional acquisition (Feinberg et al., 2010; Lin et al., 2005) of repetitions, flip angle, echo time, and acquisition volume (field o 2.”
Simultaneous PET-MRI: a new approach for functional and morphological imaging (2008, methodology): “We further verified the capability to perform BOLD imaging with the PET installed within the MRI scanner by implementing the Function Biomedical Informatics Research Network (FBIRN) procedure”
Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom (2018, background): “Friedman and Glover present the advantages of a quality-control standard for evaluation and acceptance of a new scanner, benchmarks for comparisons with other MRI centers, monitoring system constancy through hardware and software upgrades, and planning of multicenter studies (45).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “For most advanced MRI centers, regular quality assurance protocols ensuring scanner performance stability are performed daily (Friedman and Glover, 2006; Ihalainen et al., 2004; Simmons et al., 1999).”
A guide for concurrent TMS-fMRI to investigate functional brain networks (2022, methodology): “To investigate the impact of TMS on each of these EPI phases, we delivered single-pulse TMS to a Functional Biomedical Informatics Research Network (FBIRN) standard gel phantom (Friedman and Glover, 2006; Greve et al., 2011).”