ArticleJournal of applied clinical medical physics2022
Longitudinal assessment of quality assurance measurements in a 1.5 T MR-linac: Part II-Magnetic resonance imaging.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Automated Analysis for MR Coil QA.Journal of applied clinical medical physics · 2026Article
- Patterns of response in head and neck cancer subregions using daily Quantitative MRI from MR-guided radiation therapy.Clinical and translational radiation oncology · 2026Article
- Diffusion-weighted imaging-derived parameters as quantitative imaging biomarkers in magnetic resonance-guided radiotherapy: a systematic review.Physics and imaging in radiation oncology · 2025Review
- Long-term performance evaluation of a 1.5T MR-Linac using statistical process control techniques.Radiation oncology (London, England) · 2025Article
- Quantifying multi-institutional ADC measurement variability of 1.5 T MR-Linacs: A phantom and in vivo study.Medical physics · 2025Article
- Feasibility of quantitative relaxometry for prostate target localization and response assessment in magnetic resonance-guided online adaptive stereotactic body radiotherapy.Physics and imaging in radiation oncology · 2024Article
- A model for gastrointestinal tract motility in a 4D imaging phantom of human anatomy.Medical physics · 2023Article
- Longitudinal assessment of quality assurance measurements in a 1.5 T MR-linac: Part II-Magnetic resonance imaging.Journal of applied clinical medical physics · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo describe and report longitudinal quality assurance (QA) measurements for the magnetic resonance imaging (MRI) component of the Elekta Unity MR-linac during the first year of clinical use in our institution. MATERIALS AND
methodsThe performance of the MRI component of Unity was evaluated with daily, weekly, monthly, and annual QA testing. The measurements monitor image uniformity, signal-to-noise ratio (SNR), resolution/detectability, slice position/thickness, linearity, central frequency, and geometric accuracy. In anticipation of routine use of quantitative imaging (qMRI), we characterize B0/B1 uniformity and the bias/reproducibility of longitudinal/transverse relaxation times (T1/T2) and apparent diffusion coefficient (ADC). Tolerance levels for QA measurements of qMRI biomarkers are derived from weekly monitoring of T1, T2, and ADC.
resultsThe 1-year assessment of QA measurements shows that daily variations in each MR quality metric are well below the threshold for failure. Routine testing procedures can reproducibly identify machine issues. The longitudinal three-dimensional (3D) geometric analysis reveals that the maximum distortion in a diameter of spherical volume (DSV) of 20, 30, 40, and 50 cm is 0.4, 0.6, 1.0, and 3.1 mm, respectively. The main source of distortion is gradient nonlinearity. Maximum peak-to-peak B0 inhomogeneity is 3.05 ppm, with gantry induced B0 inhomogeneities an order of magnitude smaller. The average deviation from the nominal B1 is within 2%, with minimal dependence on gantry angle. Mean ADC, T1, and T2 values are measured with high reproducibility. The median coefficient of variation for ADC, T1, and T2 is 1.3%, 1.1%, and 0.5%, respectively. The median bias for ADC, T1, and T2 is -0.8%, -0.1%, and 3.9%, respectively.
conclusionThe MRI component of Unity operates within the guidelines and recommendations for scanner performance and stability. Our findings support the recently published guidance in establishing clinically acceptable tolerance levels for image quality. Highly reproducible qMRI measurements are feasible in Unity.
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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.