ArticleJournal of applied clinical medical physics2026
Automated Analysis for MR Coil QA.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhantom-based quality assurance (QA) of magnetic resonance imaging (MRI) coils is a standard practice for assessing signal-to-noise ratio (SNR), image uniformity, and magnetic field homogeneity. Manual slice selection and region-of-interest (ROI) placements introduce operator-dependent variability and workflow inefficiencies. PURPOSE: To develop a unified, fully automated desktop-based workflow for MRI coil QA that eliminates manual slice selection and ROI placement. MATERIALS AND
methodsA Python desktop app was built to automate MRI coil QA workflows for body, torso, and head-and-neck coils. It takes DICOM image sets and raw free-induction decay data and performs slice selection, phantom detection, and fixed-geometry ROI placement automatically to calculate standard QA metrices: SNR, percent image uniformity, and frequency-domain magnetic field homogeneity. Thus, obtained results are directly saved in structured reports and plots.
resultsThe automated workflow carried out all implemented QA tasks, without manual ROI placement, and consistently produced QA metrices for body, torso, head-and-neck, weekly QA, and magnetic field homogeneity analyses. The standardization of the outputs facilitated an objective evaluation of the coils and channel performance by eliminating the subjective bias.
conclusionsThis study presents an automated MRI quality assurance workflow that operates through standardized testing methods and delivers consistent results while decreasing the impact of human error in standard clinical quality control procedures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.