MétaCan
Menu
← Back to cohort
Record W4408026205 · doi:10.1002/mp.17720

Real‐time radiation beam imaging on an MR linear accelerator using quantitative <i>T</i> <sub>1</sub> mapping

2025· article· en· W4408026205 on OpenAlexafffund
Liam Lawrence, Shawn Binda, Ryan T. Oglesby, Brige Chugh, Angus Lau

Bibliographic record

VenueMedical Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan UniversityHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLinear particle acceleratorMedical imagingPhysicsBeam (structure)Nuclear medicineRadiationImage-guided radiation therapyOpticsMedical physicsNuclear physicsComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Background Direct three‐dimensional imaging of radiation beams could enable more accurate radiation dosimetry. It has been previously reported that changes in T 1 ‐weighted magnetic resonance imaging (MRI) intensity could be observed during radiation due to radiochemical oxygen depletion. Quantitative T 1 mapping could increase sensitivity for dosimetry applications. Purpose We use an MRI linear accelerator (MR‐Linac) to visualize radiation delivery through the real‐time effects of dose on the spin‐lattice magnetic relaxation time ( T 1 ) of water. We quantify the relationships between dose, spin‐lattice relaxation rates ( R 1 ) and dissolved oxygen concentration to further investigate the mechanisms of T 1 change. Methods An ultrapure water phantom and a 1% agarose gel phantom were irradiated and imaged on a 1.5 T Elekta Unity MR‐Linac. Radiation plans were created using the Monaco treatment planning system. Images were acquired before, during and after radiation. A dual‐echo Look‐Locker inversion recovery pulse sequence was used for simultaneous dynamic T 1 / B 0 mapping. The change in R 1 with respect to dose (∆ R 1 /∆Dose) and the radiochemical oxygen depletion (ROD = ∆O 2 /∆Dose) were measured. The relaxivity of oxygen ( r 1,O2 = ∆ R 1 /∆O 2 ) in water was also measured in a separate experiment with samples of various dissolved oxygen concentrations. The minimum measurable dose over a 20‐min period was estimated using a single‐tailed 99th quantile Student's t ‐distribution. Results Changes to R 1 were found to be spatiotemporally correlated to the predicted delivered radiation dose and persisted for at least 1 h after radiation. A complex dose plan could be imaged in the 1% agarose gel phantom, as the gel limits diffusion and convective mixing. In water, the ∆ R 1 /∆Dose was found to be −1.0 × 10 −4 s −1 /Gy, the r 1,O2 was found to be 5.4 × 10 −3 s −1 /(mg/L), and the ROD was found to be −0.010 (mg/L)/Gy. Both r 1,O2 and ROD agree with published values. However, combining these two values yields a predicted ∆ R 1 /∆Dose of −5.4 × 10 −5 s −1 /Gy, indicating that radiochemical oxygen depletion alone under‐predicts the MRI effect. The detection limit of R 1 was 1.1 × 10 −3 s −1 which corresponded to a single‐voxel minimum detectable dose of 11.1 Gy for this specific sequence. Conclusion Quantitative T 1 mapping was used to image radiation dose patterns in real‐time in water and agarose gel. Radiochemical oxygen depletion only partially explains the T 1 changes measured. Agarose gel could be used as a simple system for three‐dimensional patient‐specific quality assurance. Future applications may include in vivo dosimetry for FLASH radiotherapy, though improvements in acquisition methods and hardware are likely needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.319
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueMedical Physics→Same topicAdvanced Radiotherapy Techniques→French-language works237,207→