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Record W4392762061 · doi:10.1002/mp.17024

Experimental demonstration of real‐time cardiac physiology‐based radiotherapy gating for improved cardiac radioablation on an MR‐linac

2024· article· en· W4392762061 on OpenAlexaboutno aff
Osman Akdag, Pim Borman, Stefano Mandija, P. Woodhead, Prescilla Uijtewaal, Bas W. Raaymakers, Martin F. Fast

Bibliographic record

VenueMedical Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsLinear particle acceleratorRadiation therapyMedical physicsMedicineNuclear medicineCardiac imagingGatingDosimetryRadiologyPhysicsOpticsBeam (structure)

Abstract

fetched live from OpenAlex

Abstract Background Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high‐dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. Purpose We demonstrate real‐time cardiac physiology‐based radiotherapy beam gating within a preset cardiac phase on an MR‐linac. Methods MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart‐rate (HR) = 56.2 bpm) on the 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single‐slice balance steady‐state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48, SENSE = 1.5, , voxel size = , partial Fourier factor = 0.65, frame rate = 13.3 Hz). In parallel, a 4‐lead ECG‐signal was acquired using MR‐compatible equipment. The feasibility of ECG‐based beam gating was demonstrated with a prototype gating workflow using a Quasar MRI4D motion phantom (IBA Quasar, London, ON, Canada), which was deployed in the bore of the MR‐linac. Two volunteer‐derived combined ECG‐motion traces (n = 2, mean age = 26 years, mean HR = 57.4 bpm, peak‐to‐peak amplitude = 14.7 mm) were programmed into the phantom to mimic dose delivery on a cardiac target in breath‐hold. Clinical ECG‐equipment was connected to the phantom for ECG‐voltage‐streaming in real‐time using research software. Treatment beam gating was performed in the quiescent phase (end‐diastole). System latencies were compensated by delay time correction. A previously developed MRI‐based gating workflow was used as a benchmark in this study. A 15‐beam intensity‐modulated radiotherapy (IMRT) plan ( Gy) was delivered for different motion scenarios onto radiochromic films. Next, cardiac motion was then estimated at the basal anterolateral myocardial wall via normalized cross‐correlation‐based template matching. The estimated motion signal was temporally aligned with the ECG‐signal, which were then used for position‐ and ECG‐based gating simulations in the cranial–caudal (CC), anterior–posterior (AP), and right–left (RL) directions. The effect of gating was investigated by analyzing the differences in residual motion at 30, 50, and 70% treatment beam duty cycles. Results ECG‐based (MRI‐based) beam gating was performed with effective duty cycles of 60.5% (68.8%) and 47.7% (50.4%) with residual motion reductions of 62.5% (44.7%) and 43.9% (59.3%). Local gamma analyses (1%/1 mm) returned pass rates of 97.6% (94.1%) and 90.5% (98.3%) for gated scenarios, which exceed the pass rates of 70.3% and 82.0% for nongated scenarios, respectively. In average, the gating simulations returned maximum residual motion reductions of 88%, 74%, and 81% at 30%, 50%, and 70% duty cycles, respectively, in favor of MRI‐based gating. Conclusions Real‐time ECG‐based beam gating is a feasible alternative to MRI‐based gating, resulting in improved dose delivery in terms of high rates, decreased dose deposition outside the PTV and residual motion reduction, while by‐passing cardiac MRI challenges.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.312
Teacher spread0.303 · 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".

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Citations12
Published2024
Admission routes1
Has abstractyes

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