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Background: Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance., Purpose: To develop and test a suite of open-source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI., Methods: An open-source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split-bore 1.0 T MRI magnet. The CT images provide distortion-free dataset. These data were used to test all modules of the open-source software., Results: All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm., Conclusion: Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction., Purpose: To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities., Methods: Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments., Results: The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%., Conclusions: This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications., Methods: We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported., Results: Imaging results demonstrated that DCReconNet better preserves image structures compared to CS- and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods., Conclusions: DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Background: MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation., Purpose: Once trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep-learning-based image reconstruction for real-time tracking applications on MRI-Linacs., Methods: We use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction., Results: AUTOMAP models fine-tuned on retrospectively acquired lung cancer patient data reconstructed radial k-space with equivalent accuracy to CS but with much shorter processing times. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy., Conclusion: AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to alternative approaches for real-time image guidance applications., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background and Purpose: Magnetic resonance imaging (MRI)-Linac systems combine simultaneous MRI with radiation delivery, allowing treatments to be guided by anatomically detailed, real-time images. However, MRI can be degraded by geometric distortions that cause uncertainty between imaged and actual anatomy. In this work, we develop and integrate a real-time distortion correction method that enables accurate real-time adaptive radiotherapy., Materials and Methods: The method was based on the pre-treatment calculation of distortion and the rapid correction of intrafraction images. A motion phantom was set up in an MRI-Linac at isocentre ( P 0 ), the edge ( P 1 ) and just outside ( P 2 ) the imaging volume. The target was irradiated and tracked during real-time adaptive radiotherapy with and without the distortion correction. The geometric tracking error and latency were derived from the measurements of the beam and target positions in the EPID images., Results: Without distortion correction, the mean geometric tracking error was 1.3 mm at P 1 and 3.1 mm at P 2 . When distortion correction was applied, the error was reduced to 1.0 mm at P 1 and 1.1 mm at P 2 . The corrected error was similar to an error of 0.9 mm at P 0 where the target was unaffected by distortion indicating that this method has accurately accounted for distortion during tracking. The latency was 319 ± 12 ms without distortion correction and 335 ± 34 ms with distortion correction., Conclusions: We have demonstrated a real-time distortion correction method that maintains accurate radiation delivery to the target, even at treatment locations with large distortion., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.)

MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach., (© 2022. Springer Nature Limited.)

Purpose: A radiotherapy system with a fixed treatment beam and a rotating patient positioning system could be smaller, more robust and more cost effective compared to conventional rotating gantry systems. However, patient rotation could cause anatomical deformation and compromise treatment delivery. In this work, we demonstrate an image-guided treatment workflow with a fixed beam prototype system that accounts for deformation during rotation to maintain dosimetric accuracy., Methods: The prototype system consists of an Elekta Synergy linac with the therapy beam orientated downward and a custom-built patient rotation system (PRS). A phantom that deforms with rotation was constructed and rotated within the PRS to quantify the performance of two image guidance techniques: motion compensated cone-beam CT (CBCT) for pre-treatment volumetric imaging and kilovoltage infraction monitoring (KIM) for real-time image guidance. The phantom was irradiated with a 3D conformal beam to evaluate the dosimetric accuracy of the workflow., Results: The motion compensated CBCT was used to verify pre-treatment position and the average calculated position was within -0.3 ± 1.1 mm of the phantom's ground truth position at 0°. KIM tracked the position of the target in real-time as the phantom was rotated and the average calculated position was within -0.2 ± 0.8 mm of the phantom's ground truth position. A 3D conformal treatment delivered on the prototype system with image guidance had a 3%/2 mm gamma pass rate of 96.3% compared to 98.6% delivered using a conventional rotating gantry linac., Conclusions: In this work, we have shown that image guidance can be used with fixed-beam treatment systems to measure and account for changes in target position in order to maintain dosimetric coverage during horizontal rotation. This treatment modality could provide a viable treatment option when there insufficient space for a conventional linear accelerator or where the cost is prohibitive.

Purpose: High quality radiotherapy is challenging in cases where multiple targets with independent motion are simultaneously treated. A real-time tumor tracking system that can simultaneously account for the motion of two targets was developed and characterized., Methods: The multitarget tracking system was implemented on a magnetic resonance imaging (MRI)-linac and utilized multi-leaf collimator (MLC) tracking to adapt the radiation beam to phantom targets reproducing motion with prostate and lung motion traces. Multitarget tracking consisted of three stages: (a) pretreatment aperture segmentation where the treatment aperture was divided into segments corresponding to each target, (b) MR imaging where the positions of the two targets were localized, and (c) MLC tracking where an updated treatment aperture was calculated. Electronic portal images (EPID) acquired during irradiation were analyzed to characterize geometric uncertainty and tracking latency., Results: Multitarget MLC tracking effectively accounted for the motion of both targets during treatment. The root-mean-square error between the centers of the targets and the centers of the corresponding MLC leaves were reduced from 5.5 mm without tracking to 2.7 mm with tracking for lung motion traces and reduced from 4.2 to 1.4 mm for prostate motion traces. The end-to-end latency of tracking was measured to be 328 ± 44 ms., Conclusions: We have demonstrated the first experimental implementation of MLC tracking for multiple targets having independent motion. This technology takes advantage of the imaging capabilities of MRI-linacs and would allow treatment margins to be reduced in cases where multiple targets are simultaneously treated., (© 2020 American Association of Physicists in Medicine.)

Purpose: Compared to conventional linacs with rotating gantries, a fixed-beam radiotherapy system could be smaller, more robust and more cost-effective. In this work, we developed and commissioned a prototype x-ray radiotherapy system utilizing a fixed vertical radiation beam and horizontal patient rotation., Methods: The prototype system consists of an Elekta Synergy linac with gantry fixed at 0° and a custom-built patient rotation system (PRS). The PRS was designed to immobilize patients and safely rotate them about the horizontal axis. The interlocks and emergency stops of the linac and PRS were connected. Custom software was developed to monitor the system status, control the motion of the PRS and modify treatment plans for the fixed-beam configuration. Following installation, the prototype system was commissioned for three-dimensional (3D) conformal therapy based on guidelines specified in AAPM TG-45 and TG-142, with modifications for the fixed-beam geometry made where necessary., Results: The system and control software was tested in a variety of machine states and executed motion, stop and beam gating commands as expected. Interlocks and emergency stops of the linac and PRS were found to correctly stop PRS motion and both kV and MV radiation beams when triggered. For 3D conformal treatments, the prototype system met all AAPM TG-45 and TG-142 specifications for geometric and dosimetric accuracy. Motion of the PRS was within 0.6 ± 0.3 mm and 0.10° ± 0.07° of input values for translation and rotation respectively. The axis of rotation of the PRS was coincident with the radiation beam axis to less than 1 mm. End-to-end treatment verification for 6 MV conformal treatments showed less than 2% difference between planned and delivered dose for all fields., Conclusion: In this work, we have developed and commissioned a radiotherapy system that utilizes a fixed vertical radiation beam and horizontal patient rotation. This system is a proof-of-concept prototype for a fixed-beam treatment system without a rotating gantry. Fixed-beam systems that are smaller and more cost-effective could help in improving global access to radiotherapy., (© 2018 American Association of Physicists in Medicine.)

Purpose: Fixed beam radiotherapy systems utilize couch movement and rotation instead of gantry rotation in order to simplify linear accelerator design. We investigate the ability to deliver fixed beam treatments with the same level of clinical accuracy as conventional (rotating beam) treatments using real-time image guidance to maintain this accuracy in the presence of rigid target motion., Methods: A prototype fixed beam radiotherapy system was built using a standard linac with the beam fixed in the vertical position and a computer controlled rotation stage that rotated a rigid phantom about the superior-inferior axis. Kilovoltage Intrafraction Monitoring (KIM) and real-time beam adaptation with MLC tracking was applied to a five-field IMRT treatment plan with motion introduced to the phantom. The same IMRT treatment was also delivered with real-time adaptation using the conventional rotating beam geometry. Film dosimetry was used to measure the dose delivered with a fixed beam compared to a rotating beam, as well as to compare treatments delivered with and without real-time adaptation., Results: The dose distributions were found to be equivalent between the fixed beam and rotating beam geometry for real-time adaptive radiotherapy using KIM and MLC tracking beam adaptation. Gamma analysis on the films showed agreement >98% using a 2%/2 mm criteria with adaptation for static shifts and periodic motion., Conclusions: Fixed beam treatments with real-time beam adaptation are dosimetrically equivalent to conventional treatments with a rotating beam, even in the presence of rigid target motion. This suggests that, for a rigid target, the high clinical accuracy of real-time adaptive radiotherapy can be achieved with simpler beam geometry., (© 2018 American Association of Physicists in Medicine.)