Dosimetric evaluation of MRI-guided multi-leaf collimator tracking and trailing for lung stereotactic body radiation therapy

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT

BACKGROUND

Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dose delivery.

PURPOSE

The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and foster reuse.

METHODS

The study was based on 2502 breathing signals (t t o t a l ≈ 90 $t_{total} \approx 90$  h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920 ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed.

RESULTS

The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application.

CONCLUSION

The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OOD signals.

Convolutional LSTM model for cine image prediction of abdominal motion

Objective.In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.Approach.Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.Main results.The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24-3.28 s). We evaluated the model's performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.Significance. The study's significant contribution is demonstrating the convLSTM model's ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.

Performance characterization of a novel hybrid dosimetry insert for simultaneous spatial, temporal, and motion-included dosimetry for MR-linac

BACKGROUND

Several (online) adaptive radiotherapy procedures are available to maximize healthy tissue sparing in the presence of inter/intrafractional motion during stereotactic body radiotherapy (SBRT) on an MR-linac. The increased treatment complexity and the motion-delivery interplay during these treatments require MR-compatible motion phantoms with time-resolved dosimeters to validate end-to-end workflows. This is not possible with currently available phantoms.

PURPOSE

Here, we demonstrate a new commercial hybrid film-scintillator cassette, combining high spatial resolution radiochromic film with four time-resolved plastic scintillator dosimeters (PSDs) in an MRI-compatible motion phantom.

METHODS

First, the PSD's performance for consistency, dose linearity, and pulse repetition frequency (PRF) dependence was evaluated using an RW3 solid water slab phantom. We then demonstrated the MRI4D scintillator cassette's suitability for time-resolved and motion-included quality assurance for adapt-to-shape (ATS), trailing, gating, and multileaf collimator (MLC) tracking adaptations on a 1.5 T MR-linac. To do this, the cassette was inserted into the Quasar MRI4D phantom, which we used statically or programmed with artificial and patient-derived motion. Simultaneously with dose measurements, the beam-gating latency was estimated from the time difference between the target entering/leaving the gating window and the beam-on/off times derived from the time-resolved dose measurements.

RESULTS

Experiments revealed excellent detector consistency (standard deviation ≤ $\le$ 0.6%), dose linearity (R2 = 1), and only very low PRF dependence ( ≤ $\le$ 0.4%). The dosimetry cassette demonstrated a near-perfect agreement during an ATS workflow between the time-resolved PSD and treatment planning system (TPS) dose (0%-2%). The high spatial resolution film measurements confirmed this with a 1%/1-mm local gamma pass-rate of 90%. When trailing patient-derived prostate motion for a prostate SBRT delivery, the time-resolved cassette measurements demonstrated how trailing mitigated the motion-induced dose reductions from 1%-17% to 1%-2% compared to TPS dose. The cassette's simultaneously measured spatial dose distribution highlighted the dosimetric gain of trailing by improving the 3%/3-mm local gamma pass-rates from 80% to 97% compared to the static dose. Similarly, the cassette demonstrated the benefit of real-time adaptations when compensating patient-derived respiratory motion by showing how the TPS dose was restored from 2%-56% to 0%-12% (gating) and 1%-26% to 1%-7% (MLC tracking) differences. Larger differences are explainable by TPS-PSD coregistration uncertainty combined with a steep dose gradient outside the PTV. The cassette also demonstrated how the spatial dose distributions were drastically improved by the real-time adaptations with 1%/1-mm local gamma pass-rates that were increased from 8 to 79% (gating) and from 35 to 89% (MLC tracking). The cassette-determined beam-gating latency agreed within ≤ $\le$ 12 ms with the ground truth latency measurement. Film and PSD dose agreed well for most cases (differences relative to TPS dose < $<$ 4%), while film-PSD coregistration uncertainty caused relative differences of 5%-8%.

CONCLUSIONS

This study demonstrates the excellent suitability of a new commercial hybrid film-scintillator cassette for simultaneous spatial, temporal, and motion-included dosimetry.

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purpose

Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.

Material and methods

Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm.

Results

Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms.

Conclusion

It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

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

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∘ $^{\circ }$ , SENSE = 1.5,field-of-view = 400 × 207 $\text{field-of-view} = {400}\times {207}$ mm 2 ${\text{mm}}^{2}$ , voxel size =3 × 3 × 15 $3\times 3\times 15$ mm 3 ${\rm mm}^{3}$ , 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 (1 × 6.25 ${1}\times {6.25}$ 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 highγ -pass $\gamma {\text{-pass}}$ rates, decreased dose deposition outside the PTV and residual motion reduction, while by-passing cardiac MRI challenges.

Stereotactic body radiotherapy of central lung tumours using a 1.5 T MR-linac: First clinical experiences

Background

MRI-guidance may aid better discrimination between Organs at Risk (OARs) and target volumes in proximity of the mediastinum. We report the first clinical experiences with Stereotactic Body Radiotherapy (SBRT) of (ultra)central lung tumours on a 1.5 T MR-linac.

Materials and Methods

Patients with an (ultra)central lung tumour were selected for MR-linac based SBRT treatment. A T2-weighted 3D sequence MRI acquired during free breathing was used for daily plan adaption. Prior to each fraction, contours of Internal Target Volume (ITV) and OARs were deformably propagated and amended by a radiation oncologist. Inter-fractional changes in volumes and coverage of target volumes as well as doses in OARs were evaluated in offline and online treatment plans.

Results

Ten patients were treated and completed 60 Gy in 8 or 12 fractions. In total 104 fractions were delivered. The median time in the treatment room was 41 min with a median beam-on time of 8.9 min. No grade ≥3 acute toxicity was observed. In two patients, the ITV significantly decreased during treatment (58 % and 37 %, respectively) due to tumour shrinkage. In the other patients, 81 % of online ITVs were within ±15 % of the volume of fraction 1. Comparison with the pre-treatment plan showed that ITV coverage of the online plan was similar in 52 % and improved in 34 % of cases. Adaptation to meet OAR constraints, led to decreased ITV coverage in 14 %.

Conclusions

We describe the workflow for MR-guided Radiotherapy and the feasibility of using 1.5 T MR-linac for SBRT of (ultra) central lung tumours.

Influence of magnetic field on a novel scintillation dosimeter in a 1.5 T MR-linac

For commissioning and quality assurance for adaptive workflows on the MR-linac, a dosimeter which can measure time-resolved dose during MR image acquisition is desired. The Blue Physics model 10 scintillation dosimeter is potentially an ideal detector for such measurements. However, some detectors can be influenced by the magnetic field of the MR-linac. To assess the calibration methods and magnetic field dependency of the Blue Physics scintillator in the 1.5 T MR-linac. Several calibration methods were assessed for robustness. Detector characteristics and the influence of the calibration methods were assessed based on dose reproducibility, dose linearity, dose rate dependency, relative output factor (ROF), percentage depth dose profile, axial rotation and the radial detector orientation with respect to the magnetic field. The potential application of time-resolved dynamic dose measurements during MRI acquisition was assessed. A variation of calibration factors was observed for different calibration methods. Dose reproducibility, dose linearity and dose rate stability were all found to be within tolerance and were not significantly affected by different calibration methods. Measurements with the detector showed good correspondence with reference chambers. The ROF and radial orientation dependence measurements were influenced by the calibration method used. Axial detector dependence was assessed and relative readout differences of up to 2.5% were observed. A maximum readout difference of 10.8% was obtained when rotating the detector with respect to the magnetic field. Importantly, measurements with and without MR image acquisition were consistent for both static and dynamic situations. The Blue Physics scintillation detector is suitable for relative dosimetry in the 1.5 T MR-linac when measurements are within or close to calibration conditions.

Intrafraction Motion Management With MR-Guided Radiation Therapy

High quality radiation therapy requires highly accurate and precise dose delivery. MR-guided radiotherapy (MRgRT), integrating an MRI scanner with a linear accelerator, offers excellent quality images in the treatment room without subjecting patient to ionizing radiation. MRgRT therefore provides a powerful tool for intrafraction motion management. This paper summarizes different sources of intrafraction motion for different disease sites and describes the MR imaging techniques available to visualize and quantify intrafraction motion. It provides an overview of MR guided motion management strategies and of the current technical capabilities of the commercially available MRgRT systems. It describes how these motion management capabilities are currently being used in clinical studies, protocols and provides a future outlook.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac

BACKGROUND

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction.

PURPOSE

To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities.

METHODS

Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments.

RESULTS

The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%.

CONCLUSIONS

This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.

The role of artificial intelligence in radiotherapy clinical practice

This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.

A novel external/internal tumor tracking approach to compensate for respiratory motion baseline drifts

Objective.Real-time respiratory tumor tracking as implemented in a robotic treatment unit is based on continuous optical measurement of the position of external markers and a correlation model between them and internal target positions, which are established with X-ray imaging of the tumor, or fiducials placed in or around the tumor. Correlation models are created with fifteen simultaneously measured external/internal marker position pairs divided over the respiratory cycle. Every 45-150 s, the correlation model is updated by replacing the three first acquired data pairs with three new pairs. Tracking simulations for >120.000 computer-generated respiratory tracks demonstrated that this tracking approach resulted in relevant inaccuracies in internal target position predictions, especially in case of presence of respiratory motion baseline drifts.Approach.To better cope with drifts, we introduced a novel correlation model with an explicit time dependence, and we proposed to replace the currently applied linear-motion tracking (LMT) by mixed-model tracking (MMT). In MMT, the linear correlation model is extended with an explicit time dependence in case of a detected baseline drift. MMT prediction accuracies were then established for the same >120.000 computer-generated patients as used for LMT.Main results.For 150 s update intervals, MMT outperformed LMT in internal target position prediction accuracy for 93.7 ∣ 97.2% of patients with 0.25 ∣ 0.5 mm min^-1linear respiratory motion baseline drifts with similar numbers of X-ray images and similar treatment times. For the upper 25% of patients, mean 3D internal target position prediction errors reduced by 0.7 ∣ 1.8 mm, while near maximum reductions (upper 10% of patients) were 0.9 ∣ 2.0 mm.Significance.For equal numbers of acquired X-ray images, MMT greatly improved tracking accuracy compared to LMT, especially in the presence of baseline drifts. Even with almost 50% less acquired X-ray images, MMT still outperformed LMT in internal target position prediction accuracy.

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy

BACKGROUND AND PURPOSE

Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future.

MATERIALS AND METHODS

Models were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour.

RESULTS

The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models.

CONCLUSION

LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.

Radiation Dosimetry by Use of Radiosensitive Hydrogels and Polymers: Mechanisms, State-of-the-Art and Perspective from 3D to 4D

Gel dosimetry was developed in the 1990s in response to a growing need for methods to validate the radiation dose distribution delivered to cancer patients receiving high-precision radiotherapy. Three different classes of gel dosimeters were developed and extensively studied. The first class of gel dosimeters is the Fricke gel dosimeters, which consist of a hydrogel with dissolved ferrous ions that oxidize upon exposure to ionizing radiation. The oxidation results in a change in the nuclear magnetic resonance (NMR) relaxation, which makes it possible to read out Fricke gel dosimeters by use of quantitative magnetic resonance imaging (MRI). The radiation-induced oxidation in Fricke gel dosimeters can also be visualized by adding an indicator such as xylenol orange. The second class of gel dosimeters is the radiochromic gel dosimeters, which also exhibit a color change upon irradiation but do not use a metal ion. These radiochromic gel dosimeters do not demonstrate a significant radiation-induced change in NMR properties. The third class is the polymer gel dosimeters, which contain vinyl monomers that polymerize upon irradiation. Polymer gel dosimeters are predominantly read out by quantitative MRI or X-ray CT. The accuracy of the dosimeters depends on both the physico-chemical properties of the gel dosimeters and on the readout technique. Many different gel formulations have been proposed and discussed in the scientific literature in the last three decades, and scanning methods have been optimized to achieve an acceptable accuracy for clinical dosimetry. More recently, with the introduction of the MR-Linac, which combines an MRI-scanner and a clinical linear accelerator in one, it was shown possible to acquire dose maps during radiation, but new challenges arise.

First experimental demonstration of VMAT combined with MLC tracking for single and multi fraction lung SBRT on an MR-linac

BACKGROUND AND PURPOSE

VMAT is not currently available on MR-linacs but could maximize plan conformality. To mitigate respiration without compromising delivery efficiency, MRI-guided MLC tumour tracking was recently developed for the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) in combination with IMRT. Here, we provide a first experimental demonstration of VMAT+MLC tracking for several lung SBRT indications.

MATERIALS AND METHODS

We created central patient and phantom VMAT plans (8×7.5 Gy, 2 arcs) and we created peripheral phantom plans (3×18 & 1×34 Gy, 4 arcs). A motion phantom mimicked subject-recorded respiratory motion (A‾=11 mm, f‾=0.33 Hz, drift‾=0.3 mm/min). This was monitored using 2D-cine MRI at 4 Hz to continuously realign the beam with the target. VMAT+MLC tracking performance was evaluated using 2D film dosimetry and a novel motion-encoded and time-resolved pseudo-3D dosimetry approach.

RESULTS

We found an MLC leaf and jaw end-to-end latency of 328.05(±3.78) ms and 317.33(±4.64) ms, which was mitigated by a predictor. The VMAT plans required maximum MLC speeds of 12.1 cm/s and MLC tracking superimposes an additional 1.48 cm/s. A local 2%/1 mm gamma analysis with a static measurement as reference, revealed pass-rates of 28-46% without MLC tracking and 88-100% with MLC tracking for the 2D film analysis. Similarly the pseudo-3D gamma passing-rates increased from 22-77% to 92-100%. The dose area histograms show that MLC tracking increased the GTV D_98% by 5-20% and the PTV D_95% by 7-24%, giving similar target coverage as their respective static reference.

CONCLUSION

MRI-guided VMAT+MLC tracking is technically feasible on the MR-linac and results in highly conformal dose distribution.

Intrafraction motion during radiotherapy of breast tumor, breast tumor bed, and individual axillary lymph nodes on cine magnetic resonance imaging

•

Intrafraction motion of the breast and individual axillary lymph nodes was studied.

•

Displacements were investigated using cine magnetic resonance imaging.

•

Motion was separated into breathing and drift components.

•

Medians of the maximum displacements were small, <3 mm for breast and lymph nodes.

•

Intrafraction motion of the tumor (bed) was less in prone than in supine position.

Background and purpose

In (ultra-)hypofractionation, the contribution of intrafraction motion to treatment accuracy becomes increasingly important. Our purpose was to evaluate intrafraction motion and resulting geometric uncertainties for breast tumor (bed) and individual axillary lymph nodes, and to compare prone and supine position for the breast tumor (bed).

Materials and methods

During 1–3 min of free breathing, we acquired transverse/sagittal interleaved 1.5 T cine magnetic resonance imaging (MRI) of the breast tumor (bed) in prone and supine position and coronal/sagittal cine MRI of individual axillary lymph nodes in supine position. A total of 31 prone and 23 supine breast cine MRI (in 23 women) and 52 lymph node cine MRI (in 24 women) were included. Maximum displacement, breathing amplitude, and drift were analyzed using deformable image registration. Geometric uncertainties were calculated for all displacements and for breathing motion only.

Results

Median maximum displacements (range over the three orthogonal orientations) were 1.1–1.5 mm for the breast tumor (bed) in prone and 1.8–3.0 mm in supine position, and 2.2–2.4 mm for lymph nodes. Maximum displacements were significantly smaller in prone than in supine position, mainly due to smaller breathing amplitude: 0.6–0.9 mm in prone vs. 0.9–1.4 mm in supine. Systematic and random uncertainties were 0.1–0.4 mm in prone position and 0.2–0.8 mm in supine position for the tumor (bed), and 0.4–0.6 mm for the lymph nodes.

Conclusion

Intrafraction motion of breast tumor (bed) and individual lymph nodes was small. Motion of the tumor (bed) was smaller in prone than in supine position.

A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance

Purpose

Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field‐of‐view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D‐MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden).

Methods

We developed a hybrid 2D/4D‐MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory‐correlated 4D‐MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS‐bTFE and SMS‐TSE MRI sequence. For each sequence, a prebeam dataset and a beam‐on dataset were acquired simulating the two phases of MR‐linac treatments. Prebeam data were used to generate a 4D‐based motion model and a reference mid‐position volume, while beam‐on data were used for real‐time motion extraction and reconstruction of beam‐on 4D‐MRIs. In addition, an in‐silico computational phantom was used for validation of the hybrid 2D/4D‐MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real‐time SMS data reconstruction was enabled on the scanner. A 15‐beam 8× 7.5 Gy intensity‐modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV‐to‐PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage.

Results

In‐vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D‐MRI methodology. Real‐time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in‐vivo) and 0.998 (in‐silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real‐time motion estimation for in‐vivo and in‐silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in‐vivo) and 0.5 mm (in‐silico). Beam‐on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam‐derived 4D‐MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS‐TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS‐bTFE) by a linear regression prediction filter. The local gamma analysis agreed within −3.8% to 3.3% (SMS‐bTFE) and −5.3% to 10% (SMS‐TSE) with a reference MRI sequence. The DAHs revealed a relative D98% GTV coverage between 97% and 100% (SMS‐bTFE) and 100% and 101% (SMS‐TSE) compared to the static reference.

Conclusions

The presented 2D/4D‐MRI methodology demonstrated the potential for accurately extracting real‐time motion for MLC tracking in abdominothoracic radiotherapy, while simultaneously reconstructing contiguous respiratory‐correlated 4D‐MRIs for dose accumulation.

Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy

Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.

Towards mid-position based Stereotactic Body Radiation Therapy on the MR-linac for central lung tumours

Background and purpose: Central lung tumours can be treated by magnetic resonance (MR)-guided radiotherapy. Complications might be reduced by decreasing the Planning Target Volume (PTV) using mid-position (midP)-based planning instead of Internal Target Volume (ITV)-based planning. In this study, we aimed to verify a method to automatically derive patient-specific PTV margins for midP-based planning, and show dosimetric robustness of midP-based planning for a 1.5T MR-linac.

Materials and methods: Central(n = 12) and peripheral(n = 4) central lung tumour cases who received 8x7.5 Gy were included. A midP-image was reconstructed from ten phases of the 4D-Computed Tomography using deformable image registration. The Gross Tumor Volume (GTV) was delineated on the midP-image and the PTV margin was automatically calculated based on van Herk’s margin recipe, treating the standard deviation of all Deformation Vector Fields, within the GTV, as random error component. Dosimetric robustness of midP-based planning for MR-linac using automatically derived margins was verified by 4D dose-accumulation. MidP-based plans were compared to ITV-based plans. Automatically derived margins were verified with manually derived margins.

Results: The mean D95% target coverage in GTV + 2 mm was 59.9 Gy and 62.0 Gy for midP- and ITV-based central lung plans, respectively. The mean lung dose was significantly lower for midP-based treatment plans (difference:-0.3 Gy; p<0.042). Automatically derived margins agreed within one millimeter with manually derived margins.

Conclusions: This retrospective study indicates that mid-position-based treatment plans for central lung Stereotactic Body Radiation Therapy yield lower OAR doses compared to ITV-based treatment plans on the MR-linac. Patient-specific GTV-to-PTV margins can be derived automatically and result in clinically acceptable target coverage.

First experimental exploration of real-time cardiorespiratory motion management for future stereotactic arrhythmia radioablation treatments on the MR-linac

Objective.Stereotactic arrhythmia radioablation (STAR) is a novel, non-invasive treatment for refractory ventricular tachycardia (VT). The VT isthmus is subject to both respiratory and cardiac motion. Rapid cardiac motion presents a unique challenge. In this study, we provide first experimental evidence for real-time cardiorespiratory motion-mitigated MRI-guided STAR on the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) aimed at simultaneously compensating cardiac and respiratory motions.Approach.A real-time cardiorespiratory motion-mitigated radiotherapy workflow was developed on the Unity MR-linac in research mode. A 15-beam intensity-modulated radiation therapy treatment plan (1 × 25 Gy) was created in Monaco v.5.40.01 (Elekta AB) for the Quasar MRI^4Dphantom (ModusQA, London, ON). A film dosimetry insert was moved by combining either artificial (cos⁴, 70 bpm, 10 mm peak-to-peak) or subject-derived (59 average bpm, 15.3 mm peak-to-peak) cardiac motion with respiratory (sin, 12 bpm, 20 mm peak-to-peak) motion. A balanced 2D cine MRI sequence (13 Hz, field-of-view = 400 × 207 mm², resolution = 3 × 3 × 15 mm³) was developed to estimate cardiorespiratory motion. Cardiorespiratory motion was estimated by rigid registration and then deconvoluted into cardiac and respiratory components. For beam gating, the cardiac component was used, whereas the respiratory component was used for MLC-tracking. In-silico dose accumulation experiments were performed on three patient data sets to simulate the dosimetric effect of cardiac motion on VT targets.Main results.Experimentally, a duty cycle of 57% was achieved when simultaneously applying respiratory MLC-tracking and cardiac gating. Using film, excellent agreement was observed compared to a static reference delivery, resulting in a 1%/1 mm gamma pass rate of 99%. The end-to-end gating latency was 126 ms on the Unity MR-linac. Simulations showed that cardiac motion decreased the target's D98% dose between 0.1 and 1.3 Gy, with gating providing effective mitigation.Significance.Real-time MRI-guided cardiorespiratory motion management greatly reduces motion-induced dosimetric uncertainty and warrants further research and development for potential future use in STAR.

Evaluating differences in respiratory motion estimates during radiotherapy: a single planning 4DMRI versus daily 4DMRI

Background

In radiotherapy, respiratory-induced tumor motion is typically measured using a single four-dimensional computed tomography acquisition (4DCT). Irregular breathing leads to inaccurate motion estimates, potentially resulting in undertreatment of the tumor and unnecessary dose to healthy tissue. The aim of the research was to determine if a daily pre-treatment 4DMRI-strategy led to a significantly improved motion estimate compared to single planning 4DMRI (with or without outlier rejection).

Methods

4DMRI data sets from 10 healthy volunteers were acquired. The first acquisition simulated a planning MRI, the respiratory motion estimate (constructed from the respiratory signal, i.e. the 1D navigator) was compared to the respiratory signal in the subsequent scans (simulating 5–29 treatment fractions). The same procedure was performed using the first acquisition of each day as an estimate for the subsequent acquisitions that day (2 per day, 4–20 per volunteer), simulating a daily MRI strategy. This was done for three outlier strategies: no outlier rejection (NoOR); excluding 5% of the respiratory signal whilst minimizing the range (Min95) and excluding the datapoints outside the mean end-inhalation and end-exhalation positions (MeanIE).

Results

The planning MRI median motion estimates were 27 mm for NoOR, 18 mm for Min95, and 13 mm for MeanIE. The daily MRI median motion estimates were 29 mm for NoOR, 19 mm for Min95 and 15 mm for MeanIE. The percentage of time outside the motion estimate were for the planning MRI: 2%, 10% and 32% for NoOR, Min95 and MeanIE respectively. These values were reduced with the daily MRI strategy: 0%, 6% and 17%. Applying Min95 accounted for a 30% decrease in motion estimate compared to NoOR.

Conclusion

A daily MRI improved the estimation of respiratory motion as compared to a single 4D (planning) MRI significantly. Combining the Min95 technique with a daily 4DMRI resulted in a decrease of inclusion time of 6% with a 30% decrease of motion. Outlier rejection alone on a planning MRI often led to underestimation of the movement and could potentially lead to an underdosage.

Trial registration: protocol W15_373#16.007

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01915-1.