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Record W1981139923 · doi:10.1118/1.4815216

MO‐A‐WAB‐01: MRI‐Guided Radiation Therapy

2013· article· en· W1981139923 on OpenAlexaff
David A. Jaffray, Sasa Mutic, B. G. Fallone, Bas W. Raaymakers

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsRadiation therapyComputer scienceWorkflowMagnetic resonance imagingMedical physicsMedical imagingImage-guided radiation therapyMedicineRadiologyArtificial intelligence

Abstract

fetched live from OpenAlex

The concept of integrating a magnetic resonance imaging (MRI) scanner with a therapy radiation source, either a therapy linear accelerator (linac) or 60 Co, emerged as a feasible novel approach for MRI‐guided radiation therapy (i.e. MRIgRT or MRgRT) in recent years. The main motivation for the technological development of such radiotherapy systems is to provide state of the art MR imaging inside the treatment room to guide the treatment delivery. MRI offers the capability to visualize soft‐tissue as well as to perform physiological assessment of healthy and tumor tissues. The aim of MRIgRT is to facilitate adaptive radiotherapy by using on demand MRI data to update and personalize patient treatments.The MRI‐linac/ 60 Co system integration is a challenging task due to the intrinsic default incompatibility between the sub‐components. Multiple interdependent issues need to be resolved in order to achieve the optimal operation of the MRI scanner and the radiation source(s) such as: a) radiofrequency (RF) interference, b) magnetic field coupling, c) perturbation of the dose deposited in tissue due to the presence of an external magnetic field, and d) escalation of patient skin dose. This translates into solving a complex optimization problem which drives the overall system architecture. The session will provide an overview of the MRI‐guided radiation therapy systems. The speakers will present the specifics of their proposed designs and the latest technological developments (hardware and software). They will also discuss key aspects related to the clinical implementation of their systems such as safety, applications, workflows, quality control, staffing models for supporting the infrastructure, and preliminary data. Learning Objectives: 1. Understand the main concepts of MRI‐guided radiation therapy; 2. Understand the issues and proposed solutions related to the integration of MRI‐guided radiotherapy systems; 3. Understand the advantages and limitations of MRI‐guided radiotherapy systems.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0220.015

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 head0.011
GPT teacher head0.289
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2013
Admission routes1
Has abstractyes

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