Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-Linac

In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-Linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict the dose per segment for a set of MR-Linac-deliverable VMAT test plans. The training set consisted of prostate, rectal, lung and esophageal tumour data. All patients were previously treated in our clinic with VMAT on a conventional Linac. The clinical data were converted to an MR-Linac environment prior to training. During training time, gantry and collimator angles were randomized for each training sample, while the multi-leaf-collimator shapes were rigidly shifted to ensure robust learning. A Monte Carlo dose engine was used for the generation of the ground truth data at 1% statistical uncertainty per control point. For a set of 17 MR-Linac-deliverable VMAT test plans, generated on a research treatment planning system, our method predicted highly accurate dose distributions, reporting 99.7%±0.5% for the full plan prediction at the 3%/3 mm gamma criterion. Additional evaluation on previously unseen IMRT patients passed all clinical requirements resulting in 99.0%±0.6% for the 3%/3 mm analysis. The overall performance of our method makes it a promising plan validation solution for IMRT and VMAT workflows, robust to tumour anatomies and tissue density variations

Generalized div-curl based regularization for physically constrained deformable image registration

Variational image registration methods commonly employ a similarity metric and a regularization term that renders the minimization problem well-posed. However, many frequently used regularizations such as smoothness or curvature do not necessarily reflect the underlying physics that apply to anatomical deformations. This, in turn, can make the accurate estimation of complex deformations particularly challenging. Here, we present a new highly flexible regularization inspired from the physics of fluid dynamics which allows applying independent penalties on the divergence and curl of the deformations and/or their nth order derivative. The complexity of the proposed generalized div-curl regularization renders the problem particularly challenging using conventional optimization techniques. To this end, we develop a transformation model and an optimization scheme that uses the divergence and curl components of the deformation as control parameters for the registration. We demonstrate that the original unconstrained minimization problem reduces to a constrained problem for which we propose the use of the augmented Lagrangian method. Doing this, the equations of motion greatly simplify and become managable. Our experiments indicate that the proposed framework can be applied on a variety of different registration problems and produce highly accurate deformations with the desired physical properties

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator segments following the DeepDose framework. It can then be used to predict the dose distribution per segment for a set of patient anatomies. The network was trained using data from three anatomical sites of the abdomen: prostate, rectal and oligometastatic tumours. A total of 216 patient fractions were used, previously treated in our clinic with fixed-beam IMRT using the Elekta MR-linac. For the purpose of training, 176 fractions were used with random gantry angles assigned to each segment, while 20 fractions were used for the validation of the network. The ground truth data were calculated with a Monte Carlo dose engine at 1% statistical uncertainty per segment. For a total of 20 independent abdominal test fractions with the clinical angles, the network was able to accurately predict the dose distributions, achieving 99.4% ± 0.6% for the whole plan prediction at the 3%/3 mm gamma test. The average dose difference and standard deviation per segment was 0.3% ± 0.7%. Additional dose prediction on one cervical and one pancreatic case yielded high dose agreement of 99.9% and 99.8% respectively for the 3%/3 mm criterion. Overall, we show that our deep learning-based dose engine calculates highly accurate dose distributions for a variety of abdominal tumour sites treated on the MR-linac, in terms of performance and generality

Solid: a novel similarity metric for mono-modal and multi-modal deformable image registration

Medical image registration is an integral part of various clinical applications including image guidance, motion tracking, therapy assessment and diagnosis. We present a robust approach for mono-modal and multi-modal medical image registration. To this end, we propose the novel shape operator based local image distance (SOLID) which estimates the similarity of images by comparing their second-order curvature information. Our similarity metric is rigorously tailored to be suitable for comparing images from different medical imaging modalities or image contrasts. A critical element of our method is the extraction of local features using higher-order shape information, enabling the accurate identification and registration of smaller structures. In order to assess the efficacy of the proposed similarity metric, we have implemented a variational image registration algorithm that relies on the principle of matching the curvature information of the given images. The performance of the proposed algorithm has been evaluated against various alternative state-of-the-art variational registration algorithms. Our experiments involve mono-modal as well as multi-modal and cross-contrast co-registration tasks in a broad variety of anatomical regions. Compared to the evaluated alternative registration methods, the results indicate a very favorable accuracy, precision and robustness of the proposed SOLID method in various highly challenging registration tasks

Effectiveness of visual biofeedback-guided respiratory-correlated 4D-MRI for radiotherapy guidance on the MR-linac

Purpose: Respiratory-correlated 4D-MRI may provide motion characteristics for radiotherapy but is susceptible to irregular breathing. This study investigated the effectiveness of visual biofeedback (VBF) guidance for breathing regularization during 4D-MRI acquisitions on an MR-linac. Methods: A simultaneous multislice-accelerated 4D-MRI sequence was interleaved with a one-dimensional respiratory navigator (1D-RNAV) in 10 healthy volunteers on a 1.5T Unity MR-linac (Elekta AB, Stockholm, Sweden). Volunteer-specific breathing amplitudes and periods were derived from the 1D-RNAV signal obtained during unguided 4D-MRI acquisitions. These were used for the guidance waveform, while the 1D-RNAV positions were overlayed as VBF. VBF effectiveness was quantified by calculating the change in coefficient of variation ((Formula presented.)) for the breathing amplitude and period, the position SD of end-exhale, end-inhale and midposition locations, and the agreement between the 1D-RNAV signals and guidance waveforms. The 4D-MRI quality was assessed by quantifying amounts of missing data. Results: VBF had an average latency of 520 ± 2 ms. VBF reduced median breathing variations by 18% to 35% (amplitude) and 29% to 57% (period). Median position SD reductions ranged from −3% to 35% (end-exhale), 29% to 38% (end-inhale), and 25% to 37% (midposition). Average differences between guidance waveforms and 1D-RNAV signals were 0.0 s (period) and +1.7 mm (amplitude). VBF also decreased the median amount of missing data by 11% and 29%. Conclusion: A VBF system was successfully implemented, and all volunteers were able to adapt to the guidance waveform. VBF during 4D-MRI acquisitions drastically reduced breathing variability but had limited effect on missing data in respiratory-correlated 4D-MRI

Integration of operator-validated contours in deformable image registration for dose accumulation in radiotherapy

BACKGROUND AND PURPOSE: Deformable image registration (DIR) is a core element of adaptive radiotherapy workflows, integrating daily contour propagation and/or dose accumulation in their design. Propagated contours are usually manually validated and may be edited, thereby locally invalidating the registration result. This means the registration cannot be used for dose accumulation. In this study we proposed and evaluated a novel multi-modal DIR algorithm that incorporated contour information to guide the registration. This integrates operator-validated contours with the estimated deformation vector field and warped dose. MATERIALS AND METHODS: The proposed algorithm consisted of both a normalized gradient field-based data-fidelity term on the images and an optical flow data-fidelity term on the contours. The Helmholtz-Hodge decomposition was incorporated to ensure anatomically plausible deformations. The algorithm was validated for same- and cross-contrast Magnetic Resonance (MR) image registrations, Computed Tomography (CT) registrations, and CT-to-MR registrations for different anatomies, all based on challenging clinical situations. The contour-correspondence, anatomical fidelity, registration error, and dose warping error were evaluated. RESULTS: The proposed contour-guided algorithm considerably and significantly increased contour overlap, decreasing the mean distance to agreement by a factor of 1.3 to 13.7, compared to the best algorithm without contour-guidance. Importantly, the registration error and dose warping error decreased significantly, by a factor of 1.2 to 2.0. CONCLUSIONS: Our contour-guided algorithm ensured that the deformation vector field and warped quantitative information were consistent with the operator-validated contours. This provides a feasible semi-automatic strategy for spatially correct warping of quantitative information even in difficult and artefacted cases

Proof-of-concept delivery of intensity modulated arc therapy on the Elekta Unity 1.5 T MR-linac

In this work we present the first delivery of intensity modulated arc therapy on the Elekta Unity 1.5 T MR-linac. The machine's current intensity modulated radiation therapy based control system was modified suitably to enable dynamic delivery of radiation, for the purpose of exploring MRI-guided radiation therapy adaptation modes in a research setting. The proof-of-concept feasibility was demonstrated by planning and delivering two types of plans, each investigating the performance of different parts of a dynamic treatment. A series of fixed-speed arc plans was used to show the high-speed capabilities of the gantry during radiation, while several fully modulated prostate plans-optimised following the volumetric modulated arc therapy approach-were delivered in order to establish the performance of its multi-leaf collimator and diaphragms. These plans were delivered to Delta4 Phantom+ MR and film phantoms passing the clinical quality assurance criteria used in our clinic. In addition, we also performed some initial MR imaging experiments during dynamic therapy, demonstrating that the impact of radiation and moving gantry/collimator components on the image quality is negligible. These results show that arc therapy is feasible on the Elekta Unity system. The machine's high performance components enable dynamic delivery during fast gantry rotation and can be controlled in a stable fashion to deliver fully modulated plans

Feasibility of delivered dose reconstruction for MR-guided SBRT of pancreatic tumors with fast, real-time 3D cine MRI

Background and purpose: In MR-guided SBRT of pancreatic cancer, intrafraction motion is typically monitored with (interleaved) 2D cine MRI. However, tumor surroundings are often not fully captured in these images, and motion might be distorted by through-plane movement. In this study, the feasibility of highly accelerated 3D cine MRI to reconstruct the delivered dose during MR-guided SBRT was assessed. Materials and methods: A 3D cine MRI sequence was developed for fast, time-resolved 4D imaging, featuring a low spatial resolution that allows for rapid volumetric imaging at 430 ms. The 3D cines were acquired during the entire beam-on time of 23 fractions of online adaptive MR-guided SBRT for pancreatic tumors on a 1.5 T MR-Linac. A 3D deformation vector field (DVF) was extracted for every cine dynamic using deformable image registration. Next, these DVFs were used to warp the partial dose delivered in the time interval between consecutive cine acquisitions. The warped dose plans were summed to obtain a total delivered dose. The delivered dose was also calculated under various motion correction strategies. Key DVH parameters of the GTV, duodenum, small bowel and stomach were extracted from the delivered dose and compared to the planned dose. The uncertainty of the calculated DVFs was determined with the inverse consistency error (ICE) in the high-dose regions. Results: The mean (SD) relative (ratio delivered/planned) D99% of the GTV was 0.94 (0.06), and the mean (SD) relative D0.5cc of the duodenum, small bowel, and stomach were respectively 0.98 (0.04), 1.00 (0.07), and 0.98 (0.06). In the fractions with the lowest delivered tumor coverage, it was found that significant lateral drifts had occurred. The DVFs used for dose warping had a low uncertainty with a mean (SD) ICE of 0.65 (0.07) mm. Conclusion: We employed a fast, real-time 3D cine MRI sequence for dose reconstruction in the upper abdomen, and demonstrated that accurate DVFs, acquired directly from these images, can be used for dose warping. The reconstructed delivered dose showed only a modest degradation of tumor coverage, mostly attainable to baseline drifts. This emphasizes the need for motion monitoring and development of intrafraction treatment adaptation solutions, such as baseline drift corrections

Intrafraction pancreatic tumor motion patterns during ungated magnetic resonance guided radiotherapy with an abdominal corset

Background: Stereotactic body radiotherapy (SBRT) has been shown to be a promising therapy for unresectable pancreatic tumors. However, intrafraction motion, caused by respiratory motion and organ drift, is one of the main concerns for efficient dose delivery in ungated upper abdominal radiotherapy. The aim of this study was to analyze the intrafraction gross tumor volume (GTV) motion in a clinical cohort. Materials and methods: We included 13 patients that underwent online adaptive magnetic resonance (MR)-guided SBRT for malignancies in the pancreatic region (5 × 8 Gy). An abdominal corset was fitted in order to reduce the abdominal respiratory motion. Coronal and sagittal cine magnetic resonance images of the tumor region were made at 2 Hz during the entire beam-on time of each fraction. We used deformable image registration to obtain GTV motion profiles in all three directions, which were subsequently high-pass and low-pass filtered to isolate the motion caused by respiratory motion and baseline drift, respectively. Results: The mean (SD) respiratory amplitudes were 4.2 (1.9) mm cranio-caudal (CC), 2.3 (1.1) mm ventral-dorsal (AP) and 1.4 (0.6) mm left–right (LR), with low variability within patients. The mean (SD) maximum baseline drifts were 1.2 (1.1) mm CC, 0.5 (0.4) mm AP and 0.5 (0.3) mm LR. The mean (SD) minimum baseline drifts were −0.7 (0.5) mm CC, −0.6 (0.5) mm AP and −0.5 (0.4) mm LR. Conclusion: Overall tumor motion during treatment was small and interfractionally stable. These findings show that high-precision ungated MR-guided SBRT is feasible with an abdominal corset

Dosimetric impact of intrafraction motion under abdominal compression during MR-guided SBRT for (Peri-) pancreatic tumors

Objective. Intrafraction motion is a major concern for the safety and effectiveness of high dose stereotactic body radiotherapy (SBRT) in the upper abdomen. In this study, the impact of the intrafraction motion on the delivered dose was assessed in a patient group that underwent MR-guided radiotherapy for upper abdominal malignancies with an abdominal corset. Approach. Fast online 2D cine MRI was used to extract tumor motion during beam-on time. These tumor motion profiles were combined with linac log files to reconstruct the delivered dose in 89 fractions of MR-guided SBRT in twenty patients. Aside the measured tumor motion, motion profiles were also simulated for a wide range of respiratory amplitudes and drifts, and their subsequent dosimetric impact was calculated in every fraction. Main results. The average (SD) D 99%of the gross tumor volume (GTV), relative to the planned D 99%, was 0.98 (0.03). The average (SD) relative D 0.5 cc of the duodenum, small bowel and stomach was 0.99 (0.03), 1.00 (0.03), and 0.97 (0.05), respectively. No correlation of respiratory amplitude with dosimetric impact was observed. Fractions with larger baseline drifts generally led to a larger uncertainty of dosimetric impact on the GTV and organs at risk (OAR). The simulations yielded that the delivered dose is highly dependent on the direction of on baseline drift. Especially in anatomies where the OARs are closely abutting the GTV, even modest LRor APdrifts can lead to substantial deviations from the planned dose. Significance. The vast majority of the fractions was only modestly impacted by intrafraction motion, increasing our confidence that MR-guided SBRT with abdominal compression can be safely executed for patients with abdominal tumors, without the use of gating or tracking strategies