Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy.

BACKGROUND: Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality. METHODS: We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison. RESULTS: Deep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution. CONCLUSIONS: Deployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

Magnetic resonance imaging (MRI) is a medical imaging modality that is used to image organs such as the brain, lungs, and liver as well as diseases such as cancer. MRI scans taken at high resolution are of overly long duration. This time constraint limits the accuracy of MRI-guided cancer radiation therapy, where imaging must be fast to adapt treatment to tumour motion. Here, we deployed artificial intelligence (AI) models to achieve fast and high detail MRI. We additionally validated our AI models across various scenarios. These AI-based models could potentially enable people with cancer to be treated with higher accuracy and precision.

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

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

Distortion-corrected image reconstruction with deep learning on an MRI-Linac.

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.

Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy.

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 (P0 ), 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 P0 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.

TopasOpt: An open-source library for optimization with Topas Monte Carlo.

PURPOSE: To describe and test TopasOpt: a free, open-source and extensible library for performing mathematical optimization of Monte Carlo simulations in Topas. METHODS: TopasOpt enables any Topas model to be transformed into an optimization problem, and any parameter within the model to be treated as an optimization variable. Three case studies are presented. The starting model consists of a 10 MeV electron beam striking a tungsten target. The resulting bremsstrahlung X-ray spectrum is collimated by a primary and secondary collimator before being scored in a water tank. In the first case study (electron phase space optimization), five parameters describing the electron beam were treated as optimization variables and assigned a random starting value. An objective function was defined based on differences of depth-dose and profiles in water between the original (ground truth) model and a given model generated by TopasOpt. The problem was solved using Bayesian Optimization and the Nelder-Mead method. One hundred iterations were run in each case. In the second case study, (collimator geometry optimization), this process was repeated, but three geometric parameters defining the secondary collimator were treated as optimization variables and assigned random starting values, and forty iterations were run. In the third case study, the optimization was repeated with different number of primary particles to study the effect of noise on convergence. RESULTS: For case 1 (phase space optimization), both optimization algorithms successfully minimized the objective function, with absolute mean differences in profile dose of 0.4% (Bayesian) and 0.3% (Nelder-Mead) and 0.2% in depth-dose for both algorithms. The beam energy was recovered to within 1%, however some parameters had relative errors of up to 171% - a result consistent with the known X-ray dose is insensitivity to many electron beam parameters. For case 2 (geometry optimization), absolute mean differences in profile dose were 0.6% (Bayesian) and 0.9% (Nelder-Mead), and 0.5% and 0.9% in depth-dose. The maximum percentage error in any parameter was 9% with Bayesian Optimization and 28% with Nelder-Mead. Finally, the Bayesian Optimization algorithm was demonstrated to be robust to moderate levels of noise; when the standard deviation of the objective function was 16% of the mean, the maximum error in any parameter value was 16%, and the absolute mean difference in dose was 0.9% (profile) and 0.8% (depth-dose). CONCLUSIONS: An open-source library for optimization with Topas Monte Carlo has been developed, tested, and released. This tool will improve accuracy and efficiency in any situation in which the optimal value of a parameter in a Monte Carlo simulation is unknown. Applications for this tool include (1) The design of new components (2) Reverse engineering of models based on limited experimental or published data, and (3) Tuning of Monte Carlo "hyper parameters" such as variance reduction, physics settings, or scoring parameters.

Diversity and Professional Advancement in Medical Physics.

Purpose: While disparities in the inclusion and advancement of women and minorities in science, technology, engineering, mathematics, and medical fields have been well documented, less work has focused on medical physics specifically. In this study, we evaluate historical and current diversity within the medical physics workforce, in cohorts representative of professional advancement (PA) in the field, and within National Institutes of Health (NIH)-funded medical physics research activities. Methods and Materials: The 2020 American Association of Physicists in Medicine (AAPM) membership was queried as surrogate for the medical physics workforce. Select subsets of the AAPM membership were queried as surrogate for PA and early career professional advancement (ECPA) in medical physics. Self-reported AAPM-member demographics data representative of study analysis groups were identified and analyzed. Demographic characteristics of the 2020 AAPM membership were compared with those of the PA and ECPA cohorts and United States (US) population. The AAPM-NIH Research Database was appended with principal investigator (PI) demographics data and analyzed to evaluate trends in grant allocation by PI demographic characteristics. Results: Women, Hispanic/Latinx/Spanish individuals, and individuals reporting a race other than White or Asian alone comprised 50.8%, 18.7%, and 32.4% of the US population, respectively, but only 23.9%, 9.1%, and 7.9% of the 2020 AAPM membership, respectively. In general, representation of women and minorities was further decreased in the PA cohort; however, significantly higher proportions of women (P < .001) and Hispanic/Latinx/Spanish members (P < .05) were observed in the ECPA cohort than the 2020 AAPM membership. Analysis of historical data revealed modest increases in diversity within the AAPM membership since 2002. Across NIH grants awarded to AAPM members between 1985 and 2020, only 9.4%, 5.3%, and 1.7% were awarded to women, Hispanic/Latinx/Spanish, and non-White, non-Asian PIs, respectively. Conclusions: Diversity within medical physics is limited. Proactive policy should be implemented to ensure diverse, equitable, and inclusive representation within research activities, roles representative of PA, and the profession at large.

An investigation of the conformity, feasibility, and expected clinical benefits of multiparametric MRI-guided dose painting radiotherapy in glioblastoma.

Background: New technologies developed to improve survival outcomes for glioblastoma (GBM) continue to have limited success. Recently, image-guided dose painting (DP) radiotherapy has emerged as a promising strategy to increase local control rates. In this study, we evaluate the practical application of a multiparametric MRI model of glioma infiltration for DP radiotherapy in GBM by measuring its conformity, feasibility, and expected clinical benefits against standard of care treatment. Methods: Maps of tumor probability were generated from perfusion/diffusion MRI data from 17 GBM patients via a previously developed model of GBM infiltration. Prescriptions for DP were linearly derived from tumor probability maps and used to develop dose optimized treatment plans. Conformity of DP plans to dose prescriptions was measured via a quality factor. Feasibility of DP plans was evaluated by dose metrics to target volumes and critical brain structures. Expected clinical benefit of DP plans was assessed by tumor control probability. The DP plans were compared to standard radiotherapy plans. Results: The conformity of the DP plans was >90%. Compared to the standard plans, DP (1) did not affect dose delivered to organs at risk; (2) increased mean and maximum dose and improved minimum dose coverage for the target volumes; (3) reduced minimum dose within the radiotherapy treatment margins; (4) improved local tumor control probability within the target volumes for all patients. Conclusions: A multiparametric MRI model of GBM infiltration can enable conformal, feasible, and potentially beneficial dose painting radiotherapy plans.

Bayesian optimization to design a novel x-ray shaping device.

PURPOSE: In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)-a complex mechatronic device comprising over one hundred individually powered tungsten 'leaves' that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed "SPHINX": Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray "beamlets." METHODS: A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm2 were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24-core computer. For the optimized 7-mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back-of-envelope dose rate formalism was developed and used to estimate dose rate under various conditions. RESULTS: The optimized 5-, 7-, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm2 and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 ±1.9 mm to 0.01 ±0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back-of-envelope dose rate formalism, SPHINX in conjunction with next-generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC-based delivery, with delivery of an intensity modulated 100 × 100 mm2 field achievable in 0.9 to 10.6 s depending on the beamlet widths used. CONCLUSIONS: Bayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi-beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.

Integrated MRI-guided radiotherapy - opportunities and challenges.

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.

Magnetic resonance imaging (MRI) guided proton therapy: A review of the clinical challenges, potential benefits and pathway to implementation.

Proton therapy and MRI-Linacs are two of the most exciting and fast growing technologies in radiation oncology. With over 100 MRI-Linacs and 100 proton therapy centres either in operation or under construction, an integrated approach that brings together the excellent soft tissue imaging of MRI with the superior dose conformity of proton therapy is compelling. The promise of MRI-guided proton therapy has prompted multiple research studies and the building of two pre-clinical experimental systems, taking us closer to realisation of this technology. Patients who would benefit most are those whose cancers have substantial tumour motion or anatomical variation, and those who are currently unable to receive safe dose-escalation due to nearby critical structures. MRI-guided proton therapy could allow more patients with pancreatic cancer, central lung cancer and oligo-metastatic cancers in the upper abdomen (e.g. liver and adrenal) to safely receive escalated curative doses. Head and neck, lung, brain and cervix cancers, where treatment accuracy is affected by inter-fraction tumour changes such as tumour regression or changing oedema, or normal anatomy variations, would also benefit from MRI-guidance. There will be new options to improve cure by functional MRI-guided biologically adapted proton therapy. This review focuses on the clinical aspects of MRI-guided proton therapy. We describe the clinical challenges in proton therapy and the clinical benefits from the addition of MRI-guidance. We provide updates on the design and beam modelling of in-line and perpendicular MRI-guided proton therapy systems, and a roadmap to clinical implementation.

Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT): Developing the next generation of cardiac cone beam CT imaging.

PURPOSE: An important factor when considering the use of interventional cone beam computed tomography (CBCT) imaging during cardiac procedures is the trade-off between imaging dose and image quality. Accordingly, Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) presents an alternative acquisition method, adapting the gantry velocity and projection rate of CBCT imaging systems in accordance with a patient's electrocardiogram (ECG) signal in real-time. The aim of this study was to experimentally investigate that ACROBEAT acquisitions deliver improved image quality compared to conventional cardiac CBCT imaging protocols with fewer projections acquired. METHODS: The Siemens ARTIS pheno (Siemens Healthcare, GmbH, Germany), a robotic CBCT C-arm system, was used to compare ACROBEAT with a commercially available conventional cardiac imaging protocol that utilizes multisweep retrospective ECG-gated acquisition. For ACROBEAT, real-time control of the gantry position was enabled through the Siemens Test Automation Control system. ACROBEAT and conventional image acquisitions of the CIRS Dynamic Cardiac Phantom were performed, using five patient-measured ECG traces. The traces had average heart rates of 56, 64, 76, 86, and 100 bpm. The total number of acquired projections was compared between the ACROBEAT and conventional acquisition methods. The image quality was assessed via the contrast-to-noise ratio (CNR), structural similarity index (SSIM), and root-mean square error (RMSE). RESULTS: Compared to the conventional protocol, ACROBEAT reduced the total number of projections acquired by 90%. The visual image quality provided by the ACROBEAT acquisitions, across all traces, matched or improved compared to conventional acquisition and was independent of the patient's heart rate. Across all traces, ACROBEAT averaged 1.4 times increase in the CNR, a 23% increase in the SSIM and a 29% decrease in the RMSE compared to conventional and was independent of the patient's heart rate. CONCLUSION: Adaptive patient imaging is feasible on a clinical robotic CBCT system, delivering higher quality images while reducing the number of projections acquired by 90% compared to conventional cardiac imaging protocols.

Magnetic modeling of actively shielded rotating MRI magnets in the presence of environmental steel.

Rotating MRI systems could enable novel integrated medical devices such as MRI-Linacs, MRI-xray-angiography systems, and MRI-proton therapy systems. This work aimed to investigate the feasibility of rotating actively shielded superconducting MRI magnets in the presence of environmental steel-in particular, construction steel in the floor of the installation site. Two magnets were investigated: a 1.0 T split bore magnet, and a 1.5 T closed bore magnet. Each magnet was scaled to emulate field strengths of 0.5, 1.0, and 1.5 T. Finite Element Modeling was used to simulate these magnets in the presence of a 3 × 4 m steel plate located 1250 mm or 1400 mm below the isocenter. There are two possible rotation directions: around the longitudinal (z) axis or around the transverse (x) axis. Each model was solved for rotation angles between 0 and 360° in 30° intervals around each of these axes. For each simulation, a 300 mm DSV was extracted and decomposed into spherical harmonics. For the closed-bore magnet, total induced perturbation for the zero degree rotation angle was 223, 432, and 562 µT peak-to-peak (pk-pk) for the 0.5, 1.0, and 1.5 T models respectively (steel at 1250 mm). For the split-bore magnet, the same numbers were 1477, 16747, and 1766 µT. The substantially higher perturbation for the split-bore magnet can be traced to its larger fringe field. For rotation around the z-axis, total perturbation does not change as a function of angle but is exchanged between different harmonics. For rotation around the x-axis, total perturbation is different at each rotation angle. For the closed bore magnet, maximum perturbations occurred for a 90° rotation around the transverse axis. For the split-bore magnet, the opposite was observed, with the same 90° rotation yielding total perturbation lower than the conventional position. In all cases, at least 95% of the total perturbation was composed of 1st and 2nd order harmonics. The presence of environmental steel poses a major challenge to the realization of an actively shielded rotating superconducting MRI system, requiring some novel form of shimming. Possible shimming strategies are discussed at length.

Domain classification and analysis of national institutes of health-funded medical physics research.

PURPOSE: The American Association of Physicists in Medicine (AAPM) previously developed a research database consisting of the National Institutes of Health (NIH) grants that were awarded to its members. The purpose of this report is to classify these NIH grants into various medical physics subdisciplines and analyze the scope of AAPM member research. METHODS: For this report, an algorithm classified grant topics into medical physics research subdisciplines (grants from 2002 to 2019 were analyzed). This algorithm utilized a search for common words and phrases within grant titles, keywords, abstracts, and activity codes to perform the classification. AAPM member grants were compared with non-AAPM member grants in various relevant subcategories to assess what percentage of these grants was held by AAPM members. RESULTS: The percentage of AAPM member grants that included words relating to both imaging and therapy (image-guided therapy grants) increased from 13% (27/207) in 2002 to 27% (79/293) in 2019. The percentage of AAPM member grants utilizing words relating to artificial intelligence increased from 8% in 2002 to 20% in 2019. From 2002 to 2019, AAPM member grants referenced cancer more than all other diseases combined. The majority of AAPM member grants included words relating to clinical research (81% of grants in 2002 and 99% in 2019). When comparing AAPM member with non-AAPM member grants it was found that in 2019 AAPM members held a substantial fraction of all NIH grants that referenced stereotactic radiation therapies (41%), radionuclide therapies (10%), brachytherapies (35%), intensity-modulated radiation therapies (45%), and external beam particle therapies (55%). From 2002 to 2019, the percentage of AAPM membership holding NIH grants decreased for males (3.2% down to 2.3%) and increased for females (0.8% up to 1.3%) CONCLUSIONS: The majority of grants awarded to AAPM members focus on clinical research, which underlies the translational aspect of medical physics and suggests medical physicists are uniquely positioned to help translate new technologies such as artificial intelligence into the clinic. Since 2002, NIH grants awarded to AAPM members have increasingly referenced some form of image-guided therapy, suggesting opportunities for continued innovation of imaging technologies. A substantial fraction of all radiotherapy-related research grants were awarded to AAPM members, emphasizing the important role physicists have in developing radiotherapy-related treatments.

Toward improved 3D carotid artery imaging with Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT).

PURPOSE: Interventional treatments of aneurysms in the carotid artery are increasingly being supplemented with three-dimensional (3D) x-ray imaging. The 3D imaging provides additional information on device sizing and stent malapposition during the procedure. Standard 3D x-ray image acquisition is a one-size fits all model, exposing patients to additional radiation and results in images that may have cardiac-induced motion blur around the artery. Here, we investigate the potential of a novel dynamic imaging technique Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) to personalize image acquisition by adapting the gantry velocity and projection rate in real-time to changes in the patient's electrocardiogram (ECG) trace. METHODS: We compared the total number of projections acquired, estimated carotid artery widths and image quality between ACROBEAT and conventional (single rotation fixed gantry velocity and acquisition rate, no ECG-gating) scans in a simulation study and a proof-of-concept physical phantom experimental study. The simulation study dataset consisted of an XCAT digital software phantom programmed with five patient-measured ECG traces and artery motion curves. The ECG traces had average heart rates of 56, 64, 76, 86, and 100 bpm. To validate the concept experimentally, we designed and manufactured the physical phantom from an 8-mm diameter silicon rubber tubing cast into Phytagel. An artery motion curve and the ECG trace with an average heart rate of 56 bpm was passed through the phantom. To implement ACROBEAT on the Siemens ARTIS pheno angiography system for the proof-of-concept experimental study, the Siemens Test Automation Control System was used. The total number of projections acquired and estimated carotid artery widths were compared between the ACROBEAT and conventional scans. As the ground truth was available for the simulation studies, the image quality metrics of Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM) were also utilized to assess image quality. RESULTS: In the simulation study, on average, ACROBEAT reduced the number of projections acquired by 63%, reduced carotid width estimation error by 65%, reduced RMSE by 11% and improved SSIM by 27% compared to conventional scans. In the proof-of-concept experimental study, ACROBEAT enabled a 60% reduction in the number of projections acquired and reduced carotid width estimation error by 69% compared to a conventional scan. CONCLUSION: A simulation and proof-of-concept experimental study was completed applying a novel dynamic imaging protocol, ACROBEAT, to imaging the carotid artery. The ACROBEAT results showed significantly improved image quality with fewer projections, offering potential applications to intracranial interventional procedures negatively affected by cardiac motion.

Dosimetric Optimization and Commissioning of a High Field Inline MRI-Linac.

Purpose: Unique characteristics of MRI-linac systems and mutual interactions between their components pose specific challenges for their commissioning and quality assurance. The Australian MRI-linac is a prototype system which explores the inline orientation, with radiation beam parallel to the main magnetic field. The aim of this work was to commission the radiation-related aspects of this system for its application in clinical treatments. Methods: Physical alignment of the radiation beam to the magnetic field was fine-tuned and magnetic shielding of the radiation head was designed to achieve optimal beam characteristics. These steps were guided by investigative measurements of the beam properties. Subsequently, machine performance was benchmarked against the requirements of the IEC60976/77 standards. Finally, the geometric and dosimetric data was acquired, following the AAPM Task Group 106 recommendations, to characterize the beam for modeling in the treatment planning system and with Monte Carlo simulations. The magnetic field effects on the dose deposition and on the detector response have been taken into account and issues specific to the inline design have been highlighted. Results: Alignment of the radiation beam axis and the imaging isocentre within 2 mm tolerance was obtained. The system was commissioned at two source-to-isocentre distances (SIDs): 2.4 and 1.8 m. Reproducibility and proportionality of the dose monitoring system met IEC criteria at the larger SID but slightly exceeded it at the shorter SID. Profile symmetry remained under 103% for the fields up to ~34 × 34 and 21 × 21 cm2 at the larger and shorter SID, respectively. No penumbra asymmetry, characteristic for transverse systems, was observed. The electron focusing effect, which results in high entrance doses on central axis, was quantified and methods to minimize it have been investigated. Conclusion: Methods were developed and employed to investigate and quantify the dosimetric properties of an inline MRI-Linac system. The Australian MRI-linac system has been fine-tuned in terms of beam properties and commissioned, constituting a key step toward the application of inline MRI-linacs for patient treatments.

Redefining and reinvigorating the role of physics in clinical medicine: A Report from the AAPM Medical Physics 3.0 Ad Hoc Committee.

Derived from 2 yr of deliberations and community engagement, Medical Physics 3.0 (MP3.0) is an effort commissioned by the American Association of Physicists in Medicine (AAPM) to devise a framework of strategies by which medical physicists can maintain and improve their integral roles in, and contributions to, health care and its innovation under conditions of rapid change and uncertainty. Toward that goal, MP3.0 advocates a broadened and refreshed model of sustainable excellence by which medical physicists can and should contribute to health care. The overarching conviction of MP3.0 is that every healthcare facility can benefit from medical physics and every patient's care can be improved by a medical physicist. This large and expansive challenge necessitates a range of strategies specific to each area of medical physics: clinical practice, research, product development, and education. The present paper offers a summary of the Phase 1 deliberations of the MP3.0 initiative pertaining to strategic directions of the discipline primarily but not exclusively oriented toward the clinical practice of medical physics in the United States.

Passive magnetic shielding in MRI-Linac systems.

Passive magnetic shielding refers to the use of ferromagnetic materials to redirect magnetic field lines away from vulnerable regions. An application of particular interest to the medical physics community is shielding in MRI systems, especially integrated MRI-linear accelerator (MRI-Linac) systems. In these systems, the goal is not only to minimize the magnetic field in some volume, but also to minimize the impact of the shield on the magnetic fields within the imaging volume of the MRI scanner. In this work, finite element modelling was used to assess the shielding of a side coupled 6 MV linac and resultant heterogeneity induced within the 30 cm diameter of spherical volume (DSV) of a novel 1 Tesla split bore MRI magnet. A number of different shield parameters were investigated; distance between shield and magnet, shield shape, shield thickness, shield length, openings in the shield, number of concentric layers, spacing between each layer, and shield material. Both the in-line and perpendicular MRI-Linac configurations were studied. By modifying the shield shape around the linac from the starting design of an open ended cylinder, the shielding effect was boosted by approximately 70% whilst the impact on the magnet was simultaneously reduced by approximately 10%. Openings in the shield for the RF port and beam exit were substantial sources of field leakage; however it was demonstrated that shielding could be added around these openings to compensate for this leakage. Layering multiple concentric shield shells was highly effective in the perpendicular configuration, but less so for the in-line configuration. Cautious use of high permeability materials such as Mu-metal can greatly increase the shielding performance in some scenarios. In the perpendicular configuration, magnetic shielding was more effective and the impact on the magnet lower compared with the in-line configuration.

Feasibility study on 3D image reconstruction from 2D orthogonal cine-MRI for MRI-guided radiotherapy.

INTRODUCTION: In-room MRI is a promising image guidance strategy in external beam radiotherapy to acquire volumetric information for moving targets. However, limitations in spatio-temporal resolution led several authors to use 2D orthogonal images for guidance. The aim of this work is to present a method to concurrently compensate for non-rigid tumour motion and provide an approach for 3D reconstruction from 2D orthogonal cine-MRI slices for MRI-guided treatments. METHODS: Free-breathing sagittal/coronal interleaved 2D cine-MRI were acquired in addition to a pre-treatment 3D volume in two patients. We performed deformable image registration (DIR) between cine-MRI slices and corresponding slices in the pre-treatment 3D volume. Based on an extrapolation of the interleaved 2D motion fields, the 3D motion field was estimated and used to warp the pre-treatment volume. Due to the lack of a ground truth for patients, the method was validated on a digital 4D lung phantom. RESULTS: On the phantom, the 3D reconstruction method was able to compensate for tumour motion and compared favourably to the results of previously adopted strategies. The difference in the 3D motion fields between the phantom and the extrapolated motion was 0.4 ± 0.3 mm for tumour and 0.8 ± 1.5 mm for whole anatomy, demonstrating feasibility of performing a 3D volumetric reconstruction directly from 2D orthogonal cine-MRI slices. Application of the method to patient data confirmed the feasibility of utilizing this method in real world scenarios. CONCLUSION: Preliminary results on phantom and patient cases confirm the feasibility of the proposed approach in an MRI-guided scenario, especially for non-rigid tumour motion compensation.