Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning

BACKGROUND

Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings.

PURPOSE

To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy.

METHODS

A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking.

RESULTS

Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions.

CONCLUSION

A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

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.

Dosimetry impact of distinct gating strategies in cine MR image-guided breath-hold pancreatic cancer radiotherapy

PURPOSE

To investigate the dosimetry effects of different gating strategies in cine magnetic resonance imaging (MRI)-guided breath-hold pancreatic cancer radiotherapy.

METHODS

Two cine MRI-based gating strategies were investigated： a tumor contour-based gating strategy at a gating threshold of 0-5% and a tumor displacement-based gating strategy at a gating threshold of 3-5 mm. The cine MRI videos were obtained from 17 pancreatic cancer patients who received MRI-guided radiation therapy. We calculated the tumor displacement in each cine MR frame that satisfied the gating threshold and obtained the proportion of frames with different displacements. We generated IMRT and VMAT plans using a 33 Gy prescription, and motion plans were generated by adding up all isocenter-shift plans corresponding to different tumor displacements. The dose parameters of GTV, PTV, and organs at risk (OAR) were compared between the original and motion plans.

RESULTS

In both gating strategies, the difference was significant in PTV coverage but not in GTV coverage between the original and motion plans. OAR dose parameters deteriorate with increasing gating threshold. The beam duty cycle increased from 19.5±14.3% (median 18.0%) to 60.8±15.6% (61.1%) for gating thresholds from 0% to 5% in tumor contour-based gating and from 51.7±11.5% (49.7%) to 67.3±12.4% (67.1%) for gating thresholds from 3 to 5 mm in tumor displacement-based gating.

CONCLUSION

In tumor contour-based gating strategy, the dose delivery accuracy deteriorates while the dose delivery efficiency improves with increasing gating thresholds. To ensure treatment efficiency, the gating threshold might be no less than 3%. A threshold up to 5% may be acceptable in terms of the GTV coverage. The displacement-based gating strategy may serve as a potential alternative to the tumor contour based gating strategy, in which the gating threshold of approximately 4 mm might be a good choice for reasonably balancing the dose delivery accuracy and efficiency.

Online prediction for respiratory movement compensation: a patient-specific gating control for MRI-guided radiotherapy

BACKGROUND

This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer.

MATERIALS AND METHODS

We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals.

RESULTS

In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively.

CONCLUSION

A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics.

Experimental Validation of a Real-Time Adaptive 4D-Optimized Particle Radiotherapy Approach to Treat Irregularly Moving Tumors

PURPOSE

Treatment of locally advanced lung cancer is limited by toxicity and insufficient local control. Particle therapy could enable more conformal treatment than intensity modulated photon therapy but is challenged by irregular tumor motion, associated range changes, and tumor deformations. We propose a new strategy for robust, online adaptive particle therapy, synergizing 4-dimensional optimization with real-time adaptive beam tracking. The strategy was tested and the required motion monitoring precision was determined.

METHODS AND MATERIALS

In multiphase 4-dimensional dose delivery (MP4D), a dedicated quasistatic treatment plan is delivered to each motion phase of periodic 4-dimensional computed tomography (4DCT). In the new extension, "MP4D with residual tracking" (MP4DRT), lateral beam tracking compensates for the displacement of the tumor center-of-mass relative to the current phase in the planning 4DCT. We implemented this method in the dose delivery system of a clinical carbon facility and tested it experimentally for a lung cancer plan based on a periodic subset of a virtual lung 4DCT (planned motion amplitude 20 mm). Treatments were delivered in a quality assurance-like setting to a moving ionization chamber array. We considered variable motion amplitudes and baseline drifts. The required motion monitoring precision was evaluated by adding noise to the motion signal. Log-file-based dose reconstructions were performed in silico on the entire 4DCT phantom data set capable of simulating nonperiodic motion. MP4DRT was compared with MP4D, rescanned beam tracking, and internal target volume plans. Treatment quality was assessed in terms of target coverage (D95), dose homogeneity (D5-D95), conformity number, and dose to heart and lung.

RESULTS

For all considered motion scenarios and metrics, MP4DRT produced the most favorable metrics among the tested motion mitigation strategies and delivered high-quality treatments. The conformity was similar to static treatments. The motion monitoring precision required for D95 >95% was 1.9 mm.

CONCLUSIONS

With clinically feasible motion monitoring, MP4DRT can deliver highly conformal dose distributions to irregularly moving targets.

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.

Intra-fraction motion of pelvic oligometastases and feasibility of PTV margin reduction using MRI guided adaptive radiotherapy

Purpose

This study assesses the impact of intra-fraction motion and PTV margin size on target coverage for patients undergoing radiation treatment of pelvic oligometastases. Dosimetric sparing of the bowel as a function of the PTV margin is also evaluated.

Materials and methods

Seven patients with pelvic oligometastases previously treated on our MR-linac (35 Gy in 5 fractions) were included in this study. Retrospective adaptive plans were created for each fraction on the daily MRI datasets using PTV margins of 5 mm, 3 mm, and 2 mm. Dosimetric constraint violations and GTV coverage were measured as a function of PTV margin size. The impact of intra-fraction motion on GTV coverage was assessed by tracking the GTV position on the cine MR images acquired during treatment delivery and creating an intra-fraction dose distribution for each IMRT beam. The intra-fraction dose was accumulated for each fraction to determine the total dose delivered to the target for each PTV size.

Results

All OAR constraints were achieved in 85.7%, 94.3%, and 100.0% of fractions when using 5 mm, 3 mm, and 2 mm PTV margins while scaling to 95% PTV coverage. Compared to plans with a 5 mm PTV margin, there was a 27.4 ± 12.3% (4.0 ± 2.2 Gy) and an 18.5 ± 7.3% (2.7 ± 1.4 Gy) reduction in the bowel D_0.5cc dose for 2 mm and 3 mm PTV margins, respectively. The target dose (GTV V_{35 Gy}) was on average 100.0 ± 0.1% (99.6 - 100%), 99.6 ± 1.0% (97.2 - 100%), and 99.0 ± 1.4% (95.0 - 100%), among all fractions for the 5 mm, 3 mm, and 2 mm PTV margins on the adaptive plans when accounting for intra-fraction motion, respectively.

Conclusion

A 2 mm PTV margin achieved a minimum of 95% GTV coverage while reducing the dose to the bowel for all patients.

Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning

PURPOSE

To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.

METHODS

A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms.

RESULTS

Across the seven patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution.

CONCLUSION

Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision.

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

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

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Displacements were investigated using cine magnetic resonance imaging.

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Motion was separated into breathing and drift components.

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

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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.

AI-based optimization for US-guided radiation therapy of the prostate

OBJECTIVES

Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation therapy of lesions in the abdomen. However, the placement of the ultrasound probe close to the target structures leads to blocking some beam directions.

METHODS

To handle the combinatorial complexity of searching for the ultrasound-robot pose and the subset of optimal treatment beams, we combine CNN-based candidate beam selection with simulated annealing for setup optimization of the ultrasound robot, and linear optimization for treatment plan optimization into an AI-based approach. For 50 prostate cases previously treated with the CyberKnife, we study setup and treatment plan optimization when including robotic ultrasound guidance.

RESULTS

The CNN-based search substantially outperforms previous randomized heuristics, increasing coverage from 93.66 to 97.20% on average. Moreover, in some cases the total MU was also reduced, particularly for smaller target volumes. Results after AI-based optimization are similar for treatment plans with and without beam blocking due to ultrasound guidance.

CONCLUSIONS

AI-based optimization allows for fast and effective search for configurations for robotic ultrasound-guided radiation therapy. The negative impact of the ultrasound robot on the plan quality can successfully be mitigated resulting only in minor differences.

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.

Time-resolved MRI for off-line treatment robustness evaluation in carbon-ion radiotherapy of pancreatic cancer

PURPOSE

In this study, we investigate the use of magnetic resonance imaging (MRI) for the clinical evaluation of gating treatment robustness in carbon-ion radiotherapy (CIRT) of pancreatic cancer. Indeed, MRI allows radiation-free repeated scans and fast dynamic sequences for time-resolved (TR) imaging (cine-MRI), providing information on inter- and intra-fraction cycle-to-cycle variations of respiratory motion. MRI can therefore support treatment planning and verification, overcoming the limitations of the current clinical standard, that is, four-dimensional computed tomography (4DCT), which describes an "average" breathing cycle neglecting breathing motion variability.

METHODS

We integrated a technique to generate a virtual CT (vCT) from 3D MRI with a method for 3D reconstruction from 2D cine-MRI, to produce TR vCTs for dose recalculations. For eight patients, the method allowed evaluating inter-fraction variations at end-exhale and intra-fraction cycle-to-cycle variability within the gating window in terms of tumor displacement and dose to the target and organs at risk.

RESULTS

The median inter-fraction tumor motion was in the range 3.33-12.16 mm, but the target coverage was robust (-0.4% median D95% variation). Concerning cycle-to-cycle variations, the gating technique was effective in limiting tumor displacement (1.35 mm median gating motion) and corresponding dose variations (-3.9% median D95% variation). The larger exposure of organs at risk (duodenum and stomach) was caused by inter-fraction motion, whereas intra-fraction cycle-to-cycle dose variations were limited.

CONCLUSIONS

This study proposed a method for the generation of TR vCTs from MRI, which enabled an off-line evaluation of gating treatment robustness and suggested its feasibility to support treatment planning of pancreatic tumors in CIRT.

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning

Purpose

The purpose of this study is to propose algorithms and methods for achieving high accuracy in tracking and interception irradiation technology for tumors that move by respiration using MR-linac (MRIdian®, ViewRay Inc.) and to use deep learning to predict the movement of moving tumors in real time during radiation therapy and reconstruct cine magnetic resonance imaging (cine-MRI) into four-dimensional (4D) movies.

Methods

In this study, we propose a reconstruction algorithm using 4DCT for treatment planning taken before irradiation as training data in consideration of the actual treatment flow. In the algorithm, two neural networks made before treatment are used to reconstruct 4D movies that predict tumor movement in real time during treatment. Cycle GAN (generative adversarial network) was used to convert MR images to CT images, and long short-term memory was used to convert cine-MRI to 4D movies and predict tumor movement.

Results

We succeeded in predicting the time including the imaging time of the MR images, the lag until irradiation, and the calculation time in the algorithm. In addition, the conversion and prediction results at each phase of reconstruction were generally good so that they could be clinically applied.

Conclusions

The reconstruction algorithm proposed in this study enables high-precision radiotherapy while predicting the volume information of the tumor and the actual tumor position, which could not be obtained during radiotherapy.

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated.Results.Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations.Conclusion.The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.

Sarcoma of the Heart Treated with Stereotactic MR-Guided Online Adaptive Radiation Therapy

We present the first case in the literature of a patient with a histology-proven intimal sarcoma of the heart, recurrent after surgery, treated with stereotactic MR-guided online adaptive radiation therapy on an MR-Linac machine. The treatment was feasible and well tolerated. The CT scan 6 months after the last treatment showed stable disease.

Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs

Real-time motion monitoring of lung tumors with low-field magnetic resonance imaging-guided linear accelerators (MR-Linacs) is currently limited to sagittal 2D cine magnetic resonance imaging (MRI). To provide input data for improved intrafractional and interfractional adaptive radiotherapy, the 4D anatomy has to be inferred from data with lower dimensionality. The purpose of this study was to experimentally validate a previously proposed propagation method that provides continuous time-resolved estimated 4D-MRI based on orthogonal cine MRI for a low-field MR-Linac. Ex vivo porcine lungs were injected with artificial nodules and mounted in a dedicated phantom that allows for the simulation of periodic and reproducible breathing motion. The phantom was scanned with a research version of a commercial 0.35 T MR-Linac. Respiratory-correlated 4D-MRI were reconstructed and served as ground truth images. Series of interleaved orthogonal slices in sagittal and coronal orientation, intersecting the injected targets, were acquired at 7.3 Hz. Estimated 4D-MRI at 3.65 Hz were created in post-processing using the propagation method and compared to the ground truth 4D-MRI. Eight datasets at different breathing frequencies and motion amplitudes were acquired for three porcine lungs. The overall median (95[Formula: see text] percentile) deviation between ground truth and estimated deformation vector fields was 2.3 mm (5.7 mm), corresponding to 0.7 (1.6) times the in-plane imaging resolution (3.5 × 3.5 mm²). Median (95[Formula: see text] percentile) estimated nodule position errors were 1.5 mm (3.8 mm) for nodules intersected by orthogonal slices and 2.1 mm (7.1 mm) for nodules located more than 2 cm away from either of the orthogonal slices. The estimation error depended on the breathing phase, the motion amplitude and the location of the estimated position with respect to the orthogonal slices. By using the propagation method, the 4D motion within the porcine lung phantom could be accurately and robustly estimated. The method could provide valuable information for treatment planning, real-time motion monitoring, treatment adaptation, and post-treatment evaluation of MR-guided radiotherapy treatments.

Characterization of radiotherapy component impact on MR imaging quality for an MRgRT system

Radiotherapy components of an magnetic resonnace-guided radiotherapy (MRgRT) system can alter the magnetic fields, causing spatial distortion and image deformation, altering imaging and radiation isocenter coincidence and the accuracy of dose calculations. This work presents a characterization of radiotherapy component impact on MR imaging quality in terms of imaging isocenter variation and spatial integrity changes on a 0.35T MRgRT system, pre- and postupgrade of the system. The impact of gantry position, MLC field size, and treatment table power state on imaging isocenter and spatial integrity were investigated. A spatial integrity phantom was used for all tests. Images were acquired for gantry angles 0-330° at 30° increments to assess the impact of gantry position. For MLC and table power state tests all images were acquired at the home gantry position (330°). MLC field sizes ranged from 1.66 to 27.4 cm edge length square fields. Imaging isocenter shift caused by gantry position was reduced from 1.7 mm at gantry 150° preupgrade to 0.9 mm at gantry 120° postupgrade. Maximum spatial integrity errors were 0.5 mm or less pre- and postupgrade for all gantry angles, MLC field sizes, and treatment table power states. However, when the treatment table was powered on, there was significant reduction in SNR. This study showed that gantry position can impact imaging isocenter, but spatial integrity errors were not dependent on gantry position, MLC field size, or treatment table power state. Significant isocenter variation, while reduced postupgrade, is cause for further investigation.

Nonrigid 3D motion estimation at high temporal resolution from prospectively undersampled k-space data using low-rank MR-MOTUS

Purpose

With the recent introduction of the MR‐LINAC, an MR‐scanner combined with a radiotherapy LINAC, MR‐based motion estimation has become of increasing interest to (retrospectively) characterize tumor and organs‐at‐risk motion during radiotherapy. To this extent, we introduce low‐rank MR‐MOTUS, a framework to retrospectively reconstruct time‐resolved nonrigid 3D+t motion fields from a single low‐resolution reference image and prospectively undersampled k‐space data acquired during motion.

Theory

Low‐rank MR‐MOTUS exploits spatiotemporal correlations in internal body motion with a low‐rank motion model, and inverts a signal model that relates motion fields directly to a reference image and k‐space data. The low‐rank model reduces the degrees‐of‐freedom, memory consumption, and reconstruction times by assuming a factorization of space‐time motion fields in spatial and temporal components.

Methods

Low‐rank MR‐MOTUS was employed to estimate motion in 2D/3D abdominothoracic scans and 3D head scans. Data were acquired using golden‐ratio radial readouts. Reconstructed 2D and 3D respiratory motion fields were, respectively, validated against time‐resolved and respiratory‐resolved image reconstructions, and the head motion against static image reconstructions from fully sampled data acquired right before and right after the motion.

Results

Results show that 2D+t respiratory motion can be estimated retrospectively at 40.8 motion fields per second, 3D+t respiratory motion at 7.6 motion fields per second and 3D+t head‐neck motion at 9.3 motion fields per second. The validations show good consistency with image reconstructions.

Conclusions

The proposed framework can estimate time‐resolved nonrigid 3D motion fields, which allows to characterize drifts and intra and inter‐cycle patterns in breathing motion during radiotherapy, and could form the basis for real‐time MR‐guided radiotherapy.

A motion prediction confidence estimation framework for prediction-based radiotherapy gating

PURPOSE

Motion prediction can compensate for latency in image-guided radiotherapy and has been an active area of research. However, motion predictions are subject to error and variations. We have developed and evaluated a novel motion prediction confidence estimation framework to improve the efficacy and robustness of prediction-based radiotherapy gating decision-making. The specific scenario of adaptive gating in magnetic resonance imaging (MRI)-guided radiotherapy is studied as an example, but the method generalizes to other modalities and motion management setups.

METHODS

The proposed prediction confidence estimator is based on a generic training/testing paradigm and consists of a weighted combination of three components: the prediction model's goodness of fit, variation in the prediction using a leave-one-out process and the velocity of the tracked target. Roughly, these terms quantify respectively the consistency between prediction and the training data, the robustness of model inference, and the stability due to target speed. The weight parameters and the action level in triggering beam-off decision are optimized. The method is assessed and validated in 8 healthy volunteer and 13 patient studies using a 0.35T MRI-guided radiotherapy system predicting 0.25-0.33 s ahead. The effect of the action level on the predicted gating decision accuracy, beam-on positive predictive value (PPV) and median distance between the predicted and ground-truth target centroids were evaluated. Statistical significance was evaluated using a paired t-test. The tradeoff between these performance metrics and gating duty cycle was assessed.

RESULTS

Use of the confidence estimator threshold increased gating accuracy by up to 2.42%, increased PPV by up to 3.00%, and reduced the median centroid distance up to 0.28 mm. The confidence estimator threshold on average increased gating accuracy to 96.5% (P = 2.08 × 10^-4 ), increased PPV to 96.7% (P = 1.46 × 10^-5 ), reduced the median centroid distance to 0.54 mm (P = 1.71 × 10^-5 ) at the cost of reducing the gating duty cycle by 14.3% to 48.5%. Hyperparameter tuning revealed that contrary to intuition, the velocity term offered only minimal performance improvement in some cases but also introduced potential stability issues. The combination of goodness of fit and leave-one-out prediction variation provided the most effective confidence estimator, yielding universally better performance in gating decisions.

CONCLUSION

Confidence estimation utilizing prediction model fitness criterion and validation principles can complement prediction methods to guide MRI-guided radiotherapy gating. Results from both volunteer and patient studies showed improved gating quality.

Organ motion quantification and margins evaluation in carbon ion therapy of abdominal lesions

PURPOSE

In image-guided particle radiotherapy of abdominal lesions, respiratory motion hinders treatment accuracy. In this study, 2D cineMRI data were used to quantify the tumor (GTV) motion and to evaluate the clinical approach based on deriving an internal target volume (ITV) from a planning 4DCT for gating treatments.

METHODS

Seven patients with abdominal lesions were treated with carbon-ion therapy at the National Centre of Oncological Hadron-therapy (Italy). The MR scan was performed on the same day of the 4DCT acquisition. For four patients, an additional MR was acquired approximately after 1 week. The cineMRI combined with deformable image registration algorithm was used to quantify tumor motion. Afterwards, two ITVs were defined considering (1) all phases (ITVFB) and (2) only phases within the gating window (ITVG), and then compared with the clinical (4DCT-derived) ITVs (ITVCG and ITVCFB).

RESULTS

Tumor residual motion estimated by cineMRI data in the two MRI sessions resulted not significantly different from 4DCT, although cineMRI accounted for cycle-to-cycle variations. The ITV normalized for the GTV median values were higher for ITVFB with respect to ITVG, ITVCFB and ITVCG. The Hausdorff distances with respect to the GTV were up to 10.55 mm, 3.13 mm, 5.56 mm and 2.51 mm, for ITVFB, ITVG, ITVCFB and ITVCG, respectively. According to both metrics, ITVCG and ITVG were not found significantly different.

CONCLUSIONS

CineMRI acquisitions allowed to quantify organ motion without delivering additional dose to the patient and to verify treatment margins in gated carbon-ion therapy of abdominal lesions.

Commissioning of a 1.5T Elekta Unity MR-linac: A single institution experience

MR image-guided radiotherapy has the potential to improve patient care, but integration of an MRI scanner with a linear accelerator adds complexity to the commissioning process. This work describes a single institution experience of commissioning an Elekta Unity MR-linac, including mechanical testing, MRI scanner commissioning, and dosimetric validation. Mechanical testing included multileaf collimator (MLC) positional accuracy, measurement of radiation isocenter diameter, and MR-to-MV coincidence. Key MRI tests included magnetic field homogeneity, geometric accuracy, image quality, and the accuracy of navigator-triggered imaging for motion management. Dosimetric validation consisted of comparison between measured and calculated PDDs and profiles, IMRT measurements, and end-to-end testing. Multileaf collimator positional accuracy was within 1.0 mm, the measured radiation isocenter walkout was 0.20 mm, and the coincidence between MR and MV isocenter was 1.06 mm, which is accounted for in the treatment planning system (TPS). For a 350-mm-diameter spherical volume, the peak-to-peak deviation of the magnetic field homogeneity was 4.44 ppm and the geometric distortion was 0.8 mm. All image quality metrics were within ACR recommendations. Navigator-triggered images showed a maximum deviation of 0.42, 0.75, and 3.0 mm in the target centroid location compared to the stationary target for a 20 mm motion at 10, 15, and 20 breaths per minute, respectively. TPS-calculated PDDs and profiles showed excellent agreement with measurement. The gamma passing rate for IMRT plans was 98.4 ± 1.1% (3%/ 2 mm) and end-to-end testing of adapted plans showed agreement within 0.4% between ion-chamber measurement and TPS calculation. All credentialing criteria were satisfied in an independent end-to-end test using an IROC MRgRT phantom.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data

Time-resolved motion estimation from MRI data has received an increasing amount of interest due to the advent of the MR-Linac. The combination of an MRI scanner and a linear accelerator enables radiation plan adaptation based on internal organ motion estimated from MRI data. However, time-resolved estimation of this motion from MRI data still remains a challenge. In light of this application, we propose MR-MOTUS, a framework to estimate non-rigid 3D motion from minimal k-space data. MR-MOTUS consists of two main components: (1) a signal model that explicitly relates the k-space signal of a deforming object to non-rigid motion-fields and a reference image, and (2) model-based reconstructions of the non-rigid motion-fields directly from k-space data. Using an a priori available reference image and the fact that internal body motion exhibits a high level of spatial correlation, we represent the motion-fields in a low-dimensional space and reconstruct them from minimal k-space data that can be acquired very rapidly. The signal model is validated through numerical experiments with a digital 3D phantom and motion-fields are reconstructed from retrospectively undersampled in vivo head and abdomen data using various undersampling strategies. A comparison is made with state-of-the-art image registration performed on images reconstructed from the same undersampled data. Results show that MR-MOTUS reconstructs in vivo 3D rigid head motion from 474-fold retrospectively downsampled k-space data, and in vivo non-rigid 3D respiratory motion from 63-fold retrospectively undersampled k-space data. Preliminary results on prospectively undersampled data acquired with a 2D golden angle acquisition during free-breathing demonstrate the practical feasibility of the method.

Generalized simultaneous multi-orientation 2D imaging

PURPOSE

Flexibility in slice prescription is critical for precise motion monitoring during MR-guided therapies. Adding more slices to improve spatial coverage during rapid 2D cine imaging often hampers temporal resolution. This work describes a framework to simultaneously acquire multiple arbitrarily oriented slices which share a common frequency encoding axis. This framework allows for higher frame rates for a given number of slices compared to conventional interleaved-slice multi-orientation cine imaging.

THEORY AND METHODS

A framework to calculate zeroth gradient moments to be played out between sequentially excited slices with multiple orientations is described here. Experiments were performed in phantom, and in vivo in the head/neck and abdomen of patients.

RESULTS

Images arbitrarily rotated relative to one another were successfully obtained in phantom and in vivo. Simultaneous multi-orientation (SMO) images were also acquired with additional in-plane acceleration to demonstrate the capability of this method to rapidly image objects moving with physiological motion.

CONCLUSIONS

The technical feasibility of the generalized SMO imaging framework was tested in this study. It shows promise for continued development for motion monitoring during MR-guided therapies.

An image regression motion prediction technique for MRI-guided radiotherapy evaluated in single-plane cine imaging

PURPOSE

To develop and evaluate a novel motion prediction method for magnetic resonance image (MRI)-guided radiotherapy applications. This method, which we deem "image regression," predicts future tissue motion based on a weighted combination of previously observed motion states. Motion predictions are derived from a sliding window of recent motion states which are defined by a temporal sequence of images. A key advantage of this method compared to other motion prediction methods is that its computational complexity scales weekly with the number of spatial points predicted. Applications of gating latency reduction and improvement in deformable registration-based target tracking are demonstrated.

METHODS

The image regression (IR) motion prediction method was developed and evaluated using 26.9 h of real-time imaging acquired from eight healthy volunteers and 13 patients using a 0.35 T MRI-guided radiotherapy system. Motion predictions were performed 0.25-0.33 s into the future using a weighted sum of previously observed motion states with image similarity-derived weights. The set of previously observed motion states were continuously updated to incorporate the changes in breathing patterns. The accuracy of the predicted radiotherapy gating decision, beam-on positive predictive value (PPV), and predicted vs ground-truth target centroid position errors are reported. The IR technique was compared against no prediction, linear extrapolation, and an established autoregressive linear prediction algorithm. The usage of IR to initialize the deformable registration and enhance the target tracking was demonstrated in the healthy volunteer studies. Deformable registration with IR initialization was compared to the initialization performed by current clinical software: no initialization, previous image registration initialization and linear motion extrapolation initialization.

RESULTS

The average IR-predicted radiation beam gating decision accuracy was 95.8%, with a PPV of 95.7%, and median and 95th percentile centroid position errors of 0.63 and 2.08 mm, respectively. Compared to the autoregressive linear prediction method, gating accuracy was 1.15% greater, PPV was 1.61% greater, and median and 95th percentile centroid distances were 0.21 and 0.23 mm smaller. The IR-initialized registration on average converged within 0.50 mm of the ground-truth position in fewer than 10 iterations whereas the next best initialization method required more than 25 iterations.

CONCLUSIONS

Image regression motion prediction has the potential to reduce the gating latencies and improve the speed and accuracy of deformable registration-based target tracking in MRI-guided radiotherapy.

Commissioning and performance evaluation of RadCalc for the Elekta unity MRI-linac

Recent availability of MRI‐guided linear accelerators has introduced a number of clinical challenges, particularly in the context of online plan adaptation. Paramount among these is verification of plan quality prior to patient treatment. Currently, there are no commercial products available for monitor unit verification that fully support the newly FDA cleared Elekta Unity 1.5 T MRI‐linac. In this work, we investigate the accuracy and precision of RadCalc for this purpose, which is a software package that uses a Clarkson integration algorithm for point dose calculation. To this end, 18 IMRT patient plans (186 individual beams) were created and used for RadCalc point dose calculations. In comparison with the primary treatment planning system (Monaco), mean point dose deviations of 0.0 ± 1.0% (n = 18) and 1.7 ± 12.4% (n = 186) were obtained on a per‐plan and per‐beam basis, respectively. The dose plane comparison functionality within RadCalc was found to be highly inaccurate, however, modest improvements could be made by artificially shifting jaws and multi leaf collimator positions to account for the dosimetric shift due to the magnetic field (67.3% vs 96.5% mean 5%/5 mm gamma pass rate).

Beyond T2 and 3T: New MRI techniques for clinicians

Technological advances in Magnetic Resonance Imaging (MRI) in terms of field strength and hybrid MR systems have led to improvements in tumor imaging in terms of anatomy and functionality. This review paper discusses the applications of such advances in the field of radiation oncology with regards to treatment planning, therapy guidance and monitoring tumor response and predicting outcome.

Evaluation of the effects of motion mitigation strategies on respiration-induced motion in each pancreatic region using cine-magnetic resonance imaging

Purpose

This study aimed to quantify the respiration‐induced motion in each pancreatic region during motion mitigation strategies and to characterize the correlations between this motion and that of the surrogate signals in cine‐magnetic resonance imaging (MRI). We also aimed to evaluate the effects of these motion mitigation strategies in each pancreatic region.

Methods

Sagittal and coronal two‐dimensional cine‐MR images were obtained in 11 healthy volunteers, eight of whom also underwent imaging with abdominal compression (AC). For each pancreatic region, the magnitude of pancreatic motion with and without motion mitigation and the positional error between the actual and predicted pancreas motion based on surrogate signals were evaluated.

Results

The magnitude of pancreatic motion with and without AC in the left–right (LR) and superior–inferior (SI) directions varied depending on the pancreatic region. In respiratory gating (RG) assessments based on a surrogate signal, although the correlation was reasonable, the positional error was large in the pancreatic tail region. Furthermore, motion mitigation in the anterior‐posterior and SI directions with RG was more effective than was that with AC in the head region.

Conclusions

This study revealed pancreatic region‐dependent variations in respiration‐induced motion and their effects on motion mitigation outcomes during AC or RG. The magnitude of pancreatic motion with or without AC and the magnitude of the positional error with RG varied depending on the pancreatic region. Therefore, during radiation therapy for pancreatic cancer, it is important to consider that the effects of motion mitigation during AC or RG may differ depending on the pancreatic region.

A fast volumetric 4D-MRI with sub-second frame rate for abdominal motion monitoring and characterization in MRI-guided radiotherapy

Background

To propose a fast volumetric 4D-MRI based on 3D pulse sequence acquisition for abdominal motion monitoring and characterization in MRI-guided radiotherapy (MRgRT).

Methods

A 3D spoiled gradient echo sequence volumetric interpolated breath-hold examination (VIBE) [repetition time/echo time (TR/TE) =0.53/1.57 ms, flip-angle =5°, receiver bandwidth (RBW) =1,400 Hz/voxel] based 4D-MRI acquisition, accelerated by 4-fold controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA), named CAIPIRINHA-VIBE 4D-MRI, was implemented on a 1.5T MRI simulator (MR-sim) and applied for abdominal imaging of nine healthy volunteers under free breathing. One hundred and forty-four dynamics of the entire abdomen volume (56 slices), in total 8,064 (144×56) images with a voxel size of 2.7×2.7×4.0 mm³, were acquired in 89 s for 4D-MRI. This CAIPIRINHA-VIBE 4D-MRI was qualitatively compared with a 2D half-Fourier acquisition single-shot turbo spin-echo (2D-HASTE) based 4D-MRI. The motions of liver dome, kidney and spleen were analyzed using the CAIPIRINHA-VIBE 4D-MRI data. The kidney motion was quantitatively characterized in terms of motion range and the correlations between left and right kidneys.

Results

CAIPIRINHA-VIBE 4D-MRI was successfully conducted in all subjects. CAIPIRINHA-VIBE 4D-MRI exhibited much higher effective volumetric temporal resolution (0.615 vs. ~5 s/volume) and better reconstructed volume consistency than 2D-HASTE 4D-MRI. CAIPIRINHA-VIBE 4D-MRI was able to characterize the respiratory motion of abdominal organs simultaneously in three orthogonal directions, and could potentially be used for whole abdomen deformable motion tracking. Renal motion range was most pronounced in superior-inferior (SI) direction (L: 10.03±2.65 mm; R: 10.38±2.80 mm), significantly larger (P<0.001) than that in anterior-posterior (AP) and the least in left-right (LR) directions. Right kidney had significantly larger mobility (4.18±2.19 vs. 2.32±1.34 mm, P=0.045) than left kidney in AP, but not in LR and SI directions. The Pearson correlation coefficients r between left and right kidney motion were 0.5063 (P=0.164), 0.6624 (P=0.052) and 0.5752 (P=0.105) in LR, AP and SI correspondingly. The correlation of renal motion in SI and AP was found significant in right kidney (r=0.843, P=0.004) but not in left kidney (r=0.467, P=0.205).

Conclusions

A fast volumetric 4D-MRI was implemented for abdominal motion monitoring in MRgRT. A sub-second volumetric temporal resolution of 0.615 s, covering the entire abdomen, was demonstrated for respiratory motion monitoring and characterization. This technique holds potentials for MRgRT applications.

Comparing the effectiveness and efficiency of various gating approaches for PBS proton therapy of pancreatic cancer using 4D-MRI datasets

Abdominal organ motion may lead to considerable uncertainties in pencil-beam scanning (PBS) proton therapy of pancreatic cancer. Beam gating, where irradiation only occurs in certain breathing phases in which the gating conditions are fulfilled, may be an option to reduce the interplay effect between tumor motion and the scanning beam. This study aims to, first, determine suitable gating windows with respect to effectiveness (low interplay effect) and efficiency (high duty cycles). Second, it investigates whether beam gating allows for a better mitigation of the interplay effect along the treatment course than free-breathing irradiations. Based on synthetic 4D-CTs, generated by warping 3D-CTs with vector fields extracted from time-resolved magnetic resonance imaging (4D-MRI) for 8 pancreatic cancer patients, 4D dose calculations (4DDC) were performed to analyze the duty cycle and homogeneity index HI  =  d5/d95 for four different gating scenarios. These were based on either fixed threshold values of CTV (clinical target volume) mean or maximum motion amplitudes (5 mm), relative CTV motion amplitudes (30%) or CTV overlap criteria (95%), respectively. 4DDC for 28-fractions treatment courses were performed with fixed and variable initial breathing phases to investigate the fractionation-induced mitigation of the interplay effect. Gating criteria, based on patient-specific relative 30% CTV motion amplitudes, showed the significantly best HI values with sufficient duty cycles, in contrast to inferior results by either fixed gating thresholds or overlap criteria. For gated treatments with 28 fractions, less fractionation-induced mitigation of the interplay effect was observed for gating criteria with gating windows  ⩾30%, compared to free-breathing treatments. The gating effectiveness for multiple fractions was improved by allowing a variable initial breathing phase. Gating with relative amplitude thresholds are effective for proton therapy of pancreatic cancer. By combining beam gating with variable initial breathing phases, a pronounced mitigation of the interplay effect by fractionation can be achieved.

A ROI-based global motion model established on 4DCT and 2D cine-MRI data for MRI-guidance in radiation therapy

In-room magnetic resonance imaging (MRI) allows the acquisition of fast 2D cine-MRI centered in the tumor for advanced motion management in radiotherapy. To achieve 3D information during treatment, patient-specific motion models can be considered the most viable solution. However, conventional global motion models are built using a single motion surrogate, independently from the anatomical location. In this work, we present a novel motion model based on regions of interest (ROIs) established on 4D computed tomography (4DCT) and 2D cine-MRI, aiming at accurately compensating for changes during treatment. In the planning phase, a motion model is built on a 4DCT dataset, through 3D deformable image registration (DIR). ROIs are then defined and correlated with motion fields derived by 2D DIR between CT slices centered in the tumor. In the treatment phase, the model is applied to in-room cine-MRI data to compensate for organ motion in a multi-modal framework, aiming at estimating a time-resolved 3DCT. The method is validated on a digital phantom and tested on two lung patients. Analysis is performed by considering different anatomical planes (coronal, sagittal and a combination of the two) and evaluating the performance of the method on tumor and diaphragm. For the phantom study, the ROI-based model results in a uniform median error on both diaphragm and tumor below 1.5 mm. For what concerns patients, median errors on both diaphragm and tumor are around 2 mm (maximum patient resolution), confirming the capability of the method to regionally compensate for motion. A novel ROI-based motion model is proposed as an integral part of an envisioned clinical MRI-guided workflow aiming at enhanced image guidance compared to conventional strategies.

Multislice motion modeling for MRI-guided radiotherapy gating

PURPOSE

On-board magnetic resonance imaging (MRI) greatly enhances real-time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out-of-slice motion estimation during MRI-guided radiotherapy are demonstrated.

METHODS

Eight healthy volunteer studies were performed using a 0.35 T MRI-guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady-state-free precession sequence. A previously published five-dimensional (5D) linear motion model used for MRI-guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image-based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group-wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out-of-slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave-one-out approach. Model-generated gating decision accuracy and beam-on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids.

RESULTS

The average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate-based 5D model.

CONCLUSION

The proposed techniques for out-of-slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate-based model allows for unbiased in vivo accuracy evaluation.

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.

Model-Interpolated Gating for Magnetic Resonance Image-Guided Radiation Therapy

PURPOSE

To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments.

METHODS AND MATERIALS

The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported.

RESULTS

Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm.

CONCLUSIONS

This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.

Technical Note: Characterization of clinical linear accelerator triggering latency for motion management system development

PURPOSE

Latencies for motion management systems have previously been presented as guidelines for system development and implementation. These guidelines consider the overall system latency, including data acquisition, algorithm processing, and linac triggering time. However, during system development, the triggering latency of the clinical linear accelerator is often considered fixed. This paper presents a method to decouple the linac-only triggering latency from the total system latency such that latency can be considered in terms of only the linac-independent aspects of the system.

METHODS

The linac-only latency was investigated by considering the time at which a linac response was observed relative to the time at which a beam-on/off triggering signal was sent to the linac. The relative time between the two signals was analyzed using a multichannel oscilloscope with input signals from a custom gating box to manually trigger the beam state as well as a diode positioned at beam isocenter to monitor the linac response. The beam-on/off latency was measured at multiple energies (6/18 MV) and repetition rates (100-600 MU/min) to investigate beam setting dependencies.

RESULTS

The measured latency was observed to be dependent on the accelerator settings for repetition rate and energy, with beam-on latencies decreasing with increasing repetition rate and decreasing energy. In contrast, the opposite trend was present for the observed beam-off latency. At 600 MU/min, beam-on/off latencies were observed to be 3.37/1.45 ms for a 6 MV beam and 6.02/0.73 ms for an 18 MV beam. Negative latencies were possible for beam-off measurements due to the mechanical latency being less than the pulse separation at given repetition rates.

CONCLUSIONS

The linac latency associated with triggering the beam-on/off was determined to have a minor contribution to the total allowable system latency; thus, the majority of the total system latency can be attributed to linac-independent factors.

Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy

The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy.

Magnetic Resonance Imaging-Guided Adaptive Radiation Therapy: A "Game Changer" for Prostate Treatment?

Radiation therapy to the prostate involves increasingly sophisticated delivery techniques and changing fractionation schedules. With a low estimated α/β ratio, a larger dose per fraction would be beneficial, with moderate fractionation schedules rapidly becoming a standard of care. The integration of a magnetic resonance imaging (MRI) scanner and linear accelerator allows for accurate soft tissue tracking with the capacity to replan for the anatomy of the day. Extreme hypofractionation schedules become a possibility using the potentially automated steps of autosegmentation, MRI-only workflow, and real-time adaptive planning. The present report reviews the steps involved in hypofractionated adaptive MRI-guided prostate radiation therapy and addresses the challenges for implementation.

Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT

OBJECTIVE

To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments.

METHODS

Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm² pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV_20 min) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV_10 s).

RESULTS

Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV_10 s areas were as small as 46% of the MR-ITV_20 min.

CONCLUSION

An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.