Experimental demonstration of real‐time cardiac physiology‐based radiotherapy gating for improved cardiac radioablation on an MR‐linac
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".