Online adaptive radiotherapy using the 1.5 Tesla MR-linac is feasible for SBRT (5 Â 7 Gy) of pelvic lymph node oligometastases. The workflow allows full online planning based on daily anatomy. Session duration is less than 60 min. Quality assurance tests, including independent 3D dose calculations and film measurements were passed.

The hybrid MRI-radiotherapy machines, like the MR-linac (Elekta AB, Stockholm, Sweden) installed at the UMC Utrecht (Utrecht, The Netherlands), will be able to provide real-time patient imaging during treatment. In order to take advantage of the system's capabilities and enable online adaptive treatments, a new generation of software should be developed, ranging from motion estimation to treatment plan adaptation. In this work we present a proof of principle adaptive pipeline designed for high precision stereotactic body radiation therapy (SBRT) suitable for sites affected by respiratory motion, like renal cell carcinoma (RCC). We utilized our research MRL treatment planning system (MRLTP) to simulate a single fraction 25 Gy free-breathing SBRT treatment for RCC by performing inter-beam replanning for two patients and one volunteer. The simulated pipeline included a combination of (pre-beam) 4D-MRI and (online) 2D cine-MR acquisitions. The 4DMRI was used to generate the mid-position reference volume, while the cine-MRI, via an in-house motion model, provided three-dimensional (3D) deformable vector fields (DVFs) describing the anatomical changes during treatment. During the treatment fraction, at an inter-beam interval, the mid-position volume of the patient was updated and the delivered dose was accurately reconstructed on the underlying motion calculated by the model. Fast online replanning, targeting the latest anatomy and incorporating the previously delivered dose was then simulated with MRLTP. The adaptive treatment was compared to a conventional mid-position SBRT plan with a 3 mm planning target volume margin reconstructed on the same motion trace. We demonstrate that our system produced tighter dose distributions and thus spared the healthy tissue, while delivering more dose to the target. The pipeline was able to account for baseline variations/drifts that occurred during treatment ensuring target coverage at the end of the treatment fraction.

The new era of hybrid MRI and linear accelerator machines, including the MR-linac currently being installed in the University Medical Center Utrecht (Utrecht, The Netherlands), will be able to provide the actual anatomy and real-time anatomy changes of the patient's target(s) and organ(s) at risk (OARs) during radiation delivery. In order to be able to take advantage of this input, a new generation of treatment planning systems is needed, that will allow plan adaptation to the latest anatomy state in an online regime. In this paper, we present a treatment planning algorithm for intensity-modulated radiotherapy (IMRT), which is able to compensate for patient anatomy changes. The system consists of an iterative sequencing loop open to anatomy updates and an inter- and intrafraction adaptation scheme that enables convergence to the ideal dose distribution without the need of a final segment weight optimization (SWO). The ability of the system to take into account organ motion and adapt the plan to the latest anatomy state is illustrated using artificial baseline shifts created for three different kidney cases. Firstly, for two kidney cases of different target volumes, we show that the system can account for intrafraction motion, delivering the intended dose to the target with minimal dose deposition to the surroundings compared to conventional plans. Secondly, for a third kidney case we show that our algorithm combined with the interfraction scheme can be used to deliver the prescribed dose while adapting to the changing anatomy during multi-fraction treatments without performing a final SWO.

Proton therapy promises higher dose conformality in comparison with regular radiotherapy techniques. Also, image guidance has an increasing role in radiotherapy and MRI is a prime candidate for this imaging. Therefore, in this paper the dosimetric feasibility of Intensity Modulated Proton Therapy (IMPT) in a magnetic field of 1.5 T and the effect on the generated dose distributions compared to those at 0 T is evaluated, using the Monte Carlo software TOol for PArticle Simulation (TOPAS). For three different anatomic sites IMPT plans are generated. It is shown that the generation of an IMPT plan in a magnetic field is feasible, the impact of the magnetic field is small, and the resulting dose distributions are equivalent for 0 T and 1.5 T. Also, the framework of Monte Carlo simulation combined with an inverse optimization method can be used to generate IMPT plans. These plans can be used in future dosimetric comparisons with e.g. IMRT and conventional IMPT. Finally, this study shows that IMPT in a 1.5 T magnetic field is dosimetrically feasible.

We present DeepDose, a deep learning framework for fast dose calculations in radiation therapy. Given a patient anatomy and linear-accelerator IMRT multi-leaf-collimator shape or segment, a novel set of physics-based inputs is calculated that encode the linac machine parameters into the underlying anatomy. These inputs are then used to train a deep convolutional network to derive the dose distribution of individual MLC shapes on a given patient anatomy. In this work we demonstrate the proof-of-concept application of DeepDose on 101 prostate patients treated in our clinic with fixed-beam IMRT. The ground-truth data used for training, validation and testing of the prediction were calculated with a state-of-the-art Monte Carlo dose engine at 1% statistical uncertainty per segment. A deep convolution network was trained using the data of 80 patients at the clinically used 3 mm3 grid spacing while 10 patients were used for validation. For another 11 independent test patients, the network was able to accurately estimate the segment doses from the clinical plans of each patient passing the clinical QA when compared with the Monte Carlo calculations, yielding on average 99.9%±0.3% for the forward calculated patient plans at 3%/3 mm gamma tests. Dose prediction using the trained network was very fast at approximately 0.9 seconds for the input generation and 0.6 seconds for single GPU inference per segment and 1 minute per patient in total. The overall performance of this dose calculation framework in terms of both accuracy and inference speed, makes it compelling for online adaptive workflows where fast segment dose calculations are needed.

Background and purpose: Monitoring the intrafraction motion and its impact on the planned dose distribution is of crucial importance in radiotherapy. In this work we quantify the delivered dose for the first prostate patients treated on a combined 1.5T Magnetic Resonance Imaging (MRI) and linear accelerator system in our clinic based on online 3D cine-MR and treatment log files. Materials and methods: A prostate intrafraction motion trace was obtained with a soft-tissue based rigid registration method with six degrees of freedom from 3D cine-MR dynamics with a temporal resolution of 8.5-16.9 s. For each fraction, all dynamics were also registered to the daily MR image used during the online treatment planning, enabling the mapping to this reference point. Moreover, each fraction's treatment log file was used to extract the timestamped machine parameters during delivery and assign it to the appropriate dynamic volume. These partial plans to dynamic volume combinations were calculated and summed to yield the delivered fraction dose. The planned and delivered dose distributions were compared among all patients for a total of 100 fractions. Results: The clinical target volume underwent on average a decrease of 2.2% ± 2.9% in terms of D99% coverage while bladder V62Gy was increased by 1.6% ± 2.3% and rectum V62Gy decreased by 0.2% ± 2.2%. Conclusions: The first MR-linac dose reconstruction results based on prostate tracking from intrafraction 3D cine-MR and treatment log files are presented. Such a pipeline is essential for online adaptation especially as we progress to MRI-guided extremely hypofractionated treatments.

The MRI linear accelerator (MR-linac) that is currently being installed in the University Medical Center Utrecht (Utrecht, The Netherlands), will be able to track the patient's target(s) and Organ(s) At Risk during radiation delivery. In this paper, we present a treatment planning system for intensity-modulated radiotherapy (IMRT). It is capable of Adaptive Radiotherapy and consists of a GPU Monte Carlo dose engine, an inverse dose optimization algorithm and a novel adaptive sequencing algorithm. The system is able to compensate for patient anatomy changes and enables radiation delivery immediately from the first calculated segment. IMRT plans meeting all clinical constraints were generated for two breast cases, one spinal bone metastasis case, two prostate cases with integrated boost regions and one head and neck case. These plans were generated by the segment weighted version of our algorithm, in a 0 T environment in order to test the feasibility of the new sequencing strategy in current clinical conditions, yielding very small differences between the fluence and sequenced distributions. All plans went through stringent experimental quality assurance on Delta4 and passed all clinical tests currently performed in our institute. A new inter-fraction adaptation scheme built on top of this algorithm is also proposed that enables convergence to the ideal dose distribution without the need of a final segment weight optimization. The first results of this method confirm that convergence is achieved within the first fractions of the treatment. These features combined will lead to a fully adaptive intra-fraction planning system able to take into account patient anatomy updates during treatment.

Stereotactic body radiation therapy (SBRT) has shown great promise in increasing local control rates for renal-cell carcinoma (RCC). Characterized by steep dose gradients and high fraction doses, these hypo-fractionated treatments are, however, prone to dosimetric errors as a result of variations in intra-fraction respiratory-induced motion, such as drifts and amplitude alterations. This may lead to significant variations in the deposited dose. This study aims to develop a method for calculating the accumulated dose for MRI-guided SBRT of RCC in the presence of intra-fraction respiratory variations and determine the effect of such variations on the deposited dose. For this, RCC SBRT treatments were simulated while the underlying anatomy was moving, based on motion information from three motion models with increasing complexity: (1) STATIC, in which static anatomy was assumed, (2) AVG-RESP, in which 4D-MRI phase-volumes were time-weighted, and (3) PCA, a method that generates 3D volumes with sufficient spatio-temporal resolution to capture respiration and intra-fraction variations. Five RCC patients and two volunteers were included and treatments delivery was simulated, using motion derived from subject-specific MR imaging. Motion was most accurately estimated using the PCA method with root-mean-squared errors of 2.7, 2.4, 1.0 mm for STATIC, AVG-RESP and PCA, respectively. The heterogeneous patient group demonstrated relatively large dosimetric differences between the STATIC and AVG-RESP, and the PCA reconstructed dose maps, with hotspots up to [Formula: see text] of the D99 and an underdosed GTV in three out of the five patients. This shows the potential importance of including intra-fraction motion variations in dose calculations.