ArticleMedical physics2020
Rapid multicontrast brain imaging on a 0.35T MR-linac.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features.Technology in cancer research & treatmentPooled it
- Simultaneous T1, T2, R2*, PDFF, and triglyceride quantification in the head and neck using magnetic resonance fingerprinting on a 1.5T MR-Linac.Medical physics · 2026Article
- Feasibility of longitudinal relaxation rate mapping with non-Cartesian sampling and compressed sensing on a 1.5 T magnetic resonance linear accelerator.Physics and imaging in radiation oncology · 2026Article
- Feasibility and Tumor Dynamics of Daily MRI-Guided Online Adaptive Radiotherapy for Brain Glioma.CNS neuroscience & therapeutics · 2026Observational
- Magnetic Resonance Imaging Sequences and Technologies in Adaptive Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- Magnetic Resonance Imaging Relaxometry for Glioblastoma Response Assessment During Radiation Therapy on a 0.35 T Magnetic Resonance Imaging Linear Accelerator.International journal of radiation oncology, biology, physics · 2025Article
- Applications of Advanced Imaging for Radiotherapy Planning and Response Assessment in the Central Nervous System.Tomography (Ann Arbor, Mich.) · 2025Review
- Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Article
- Evolving concepts in margin strategies and adaptive radiotherapy for glioblastoma: A new future is on the horizon.Neuro-oncology · 2024Review
- Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2024Article
- A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma.Cancers · 2023Article
- Multi-parametric MRI for radiotherapy simulation.Medical physics · 2023Review
- Ventilation and perfusion MRI at a 0.35 T MR-Linac: feasibility and reproducibility study.Radiation oncology (London, England) · 2023Article
- Low-rank inversion reconstruction for through-plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T.Magnetic resonance in medicine · 2022Article
- Integrated MRI-guided radiotherapy - opportunities and challenges.Nature reviews. Clinical oncology · 2022Review
- Feasibility of cardiac-synchronized quantitative TPhysics and imaging in radiation oncology · 2022Article
- Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians.CA: a cancer journal for clinicians · 2022Review
- Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy.Medical physics · 2021Article
- Feasibility of MR fingerprinting using a high-performance 0.55 T MRI system.Magnetic resonance imaging · 2021Article
- MR-Guided Radiotherapy for Brain and Spine Tumors.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeMagnetic resonance-guided radiation therapy (MRgRT) has shown great promise for localization and real-time tumor monitoring. However, to date, quantitative imaging has been limited for low field MRgRT. This work benchmarks quantitative T1, R2*, and Proton Density (PD)mapping in a phantom on a 0.35 T MR-linac and implements a novel acquisition method, STrategically Acquired Gradient Echo (STAGE). To further validate STAGE in a clinical setting, a pilot study was undertaken in a cohort of brain tumor patients to elucidate opportunities for longitudinal functional imaging with an MR-linac in the brain.
methodsSTAGE (two triple-echo gradient echo (GRE) acquisitions) was optimized for a 0.35T low-field MR-linac. Simulations were performed to choose two flip angles to optimize signal-to-noise ratio (SNR) and T1-mapping precision. Tradeoffs between SNR, scan time, and spatial resolution for whole-brain coverage were evaluated in healthy volunteers. Data were inputted into a STAGE processing pipeline to yield four qualitative images (T1-weighted, enhanced T1-weighted, proton-density (PD) weighted, and simulated FLuid-Attenuated Inversion Recovery (sFLAIR)), and three quantitative datasets (T1, PD, and R2*). A benchmarking ISMRM/NIST phantom consisting of vials with variable NiCl
resultsIn the phantom, RE of measured VFA T1 and STAGE relative to IR reference values were 7.0 ± 2.5% and 9.5 ± 2.2% respectively. RE for the PD vials was 8.1 ± 6.8% and CV for phantom R2* measurements was 10.1 ± 9.9%. Simulations and volunteer experiments yielded final STAGE parameters of FA = 50°/10°, 1 × 1 × 3 mm
conclusionQuantitative T1, R2*, and PD mapping are promising at 0.35 T agreeing well with reference data. STAGE phantom data offer quantitative representations comparable to traditional methods in a fraction of the acquisition time. Initial feasibility of implementing STAGE at 0.35 T in a patient brain tumor cohort suggests that detectable changes can be observed over time. With confirmation in a larger cohort, results may be implemented to identify areas of recurrence and facilitate adaptive radiation therapy.
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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.