Stereotactic Body Radiotherapy (SBRT) to Localised Prostate Cancer in the Era of MRI-Guided Adaptive Radiotherapy: Doses Delivered in the HERMES Trial Comparing Two- and Five-Fraction Treatments.

HERMES is a phase II trial of MRI-guided daily-adaptive radiotherapy (MRIgART) randomising men with localised prostate cancer to either 2-fractions of SBRT with a boost to the tumour or 5-fraction SBRT. In the context of this highly innovative regime the dose delivered must be carefully considered. The first ten patients recruited to HERMES were analysed in order to establish the dose received by the targets and organs at risk (OARS) in the context of intrafraction motion. A regression analysis was performed to measure how the volume of air within the rectum might further impact rectal dose secondary to the electron return effect (ERE). One hundred percent of CTV target objectives were achieved on the MRI taken prior to beam-on-time. The post-delivery MRI showed that high-dose CTV coverage was achieved in 90% of sub-fractions (each fraction is delivered in two sub-fractions) in the 2-fraction cohort and in 88% of fractions the 5-fraction cohort. Rectal D1 cm3 was the most exceeded constraint; three patients exceeded the D1 cm3 < 20.8 Gy in the 2-fraction cohort and one patient exceeded the D1 cm3 < 36 Gy in the 5-fraction cohort. The volume of rectal gas within 1 cm of the prostate was directly proportional to the increase in rectal D1 cm3, with a strong (R = 0.69) and very strong (R = 0.90) correlation in the 2-fraction and 5-fraction cohort respectively. Dose delivery specified in HERMES is feasible, although for some patients delivered doses to both target and OARs may vary from those planned.

Magnetic resonance image-guided adaptive radiotherapy enables safe CTV-to-PTV margin reduction in prostate cancer: a cine MRI motion study.

Introduction: We aimed to establish if stereotactic body radiotherapy to the prostate can be delivered safely using reduced clinical target volume (CTV) to planning target volume (PTV) margins on the 1.5T MR-Linac (MRL) (Elekta, Stockholm, Sweden), in the absence of gating. Methods: Cine images taken in 3 orthogonal planes during the delivery of prostate SBRT with 36.25 Gray (Gy) in 5 fractions on the MRL were analysed. Using the data from 20 patients, the percentage of radiotherapy (RT) delivery time where the prostate position moved beyond 1, 2, 3, 4 and 5 mm in the left-right (LR), superior-inferior (SI), anterior-posterior (AP) and any direction was calculated. Results: The prostate moved less than 3 mm in any direction for 90% of the monitoring period in 95% of patients. On a per-fraction basis, 93% of fractions displayed motion in all directions within 3 mm for 90% of the fraction delivery time. Recurring motion patterns were observed showing that the prostate moved with shallow drift (most common), transient excursions and persistent excursions during treatment. Conclusion: A 3 mm CTV-PTV margin is safe to use for the treatment of 5 fraction prostate SBRT on the MRL, without gating. In the context of gating this work suggests that treatment time will not be extensively lengthened when an appropriate gating window is applied.

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.

Background: MR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute. Methods: Auto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures. Results: Automated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures. Conclusion: Majority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.

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

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

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy.

BACKGROUND: Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. PURPOSE: We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. METHODS: A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25 Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25 Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. RESULTS: On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148 ms. Median gamma pass rate (2%/3 mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07 Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12 Gy for the investigated volume and dose criteria respectively. CONCLUSIONS: Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

Interim Toxicity Analysis From the Randomized HERMES Trial of 2- and 5-Fraction Magnetic Resonance Imaging-Guided Adaptive Prostate Radiation Therapy.

PURPOSE: Ultrahypofractionated radiation therapy (UHRT) is an effective treatment for localized prostate cancer with an acceptable toxicity profile; boosting the visible intraprostatic tumor has been shown to improve biochemical disease-free survival with no significant effect on genitourinary (GU) and gastrointestinal (GI) toxicity. METHODS AND MATERIALS: HERMES is a single-center noncomparative randomized phase 2 trial in men with intermediate or lower high risk prostate cancer. Patients were allocated (1:1) to 36.25 Gy in 5 fractions over 2 weeks or 24 Gy in 2 fractions over 8 days with an integrated boost to the magnetic resonance imaging (MRI) visible tumor of 27 Gy in 2 fractions. A minimization algorithm with a random element with risk group as a balancing factor was used for participant randomization. Treatment was delivered on the Unity MR-Linac (Elekta AB) with daily online adaption. The primary endpoint was acute GU Common Terminology Criteria for Adverse Events version 5.0 toxicity with the aim of excluding a doubling of the rate of acute grade 2+ GU toxicity seen in PACE. Analysis was by treatment received and included all participants who received at least 1 fraction of study treatment. This interim analysis was prespecified (stage 1 of a 2-stage Simon design) for when 10 participants in each treatment group had completed the acute toxicity monitoring period (12 weeks after radiation therapy). RESULTS: Acute grade 2 GU toxicity was reported in 1 (10%) patient in the 5-fraction group and 2 (20%) patients in the 2-fraction group. No grade 3+ GU toxicities were reported. CONCLUSIONS: At this interim analysis, the rate of GU toxicity in the 2-fraction and 5-fraction treatment groups was found to be below the prespecified threshold (5/10 grade 2+) and continuation of the study to complete recruitment of 23 participants per group was recommended.

Evaluation of non-vendor magnetic resonance imaging sequences for use in bladder cancer magnetic resonance image guided radiotherapy.

Hybrid systems that combine Magnetic Resonance Imaging (MRI) and linear accelerators are available clinically to guide and adapt radiotherapy. Vendor-approved MRI sequences are provided, however alternative sequences may offer advantages. The aim of this study was to develop a systematic approach for non-vendor sequence evaluation, to determine safety, accuracy and overall clinical application of two potential sequences for bladder cancer MRI guided radiotherapy. Non-vendor sequences underwent and passed clinical image qualitative review, phantom quality assurance, and radiotherapy planning assessments. Volunteer workflow tests showed the potential for one sequence to reduce workflow time by 27% compared to the standard vendor sequence.

Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy.

Background and purpose: Physiological motion impacts the dose delivered to tumours and vital organs in external beam radiotherapy and particularly in particle therapy. The excellent soft-tissue demarcation of 4D magnetic resonance imaging (4D-MRI) could inform on intra-fractional motion, but long image reconstruction times hinder its use in online treatment adaptation. Here we employ techniques from high-performance computing to reduce 4D-MRI reconstruction times below two minutes to facilitate their use in MR-guided radiotherapy. Material and methods: Four patients with pancreatic adenocarcinoma were scanned with a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac. Fast parallelised open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm were developed for central processing unit (CPU) and graphics processing unit (GPU) architectures. We assessed the impact of architecture, oversampling and respiratory binning strategy on 4D-MRI reconstruction time and compared images using the structural similarity (SSIM) index against a MATLAB reference implementation. Scaling and bottlenecks for the different architectures were studied using multi-GPU systems. Results: All reconstructed 4D-MRI were identical to the reference implementation (SSIM > 0.99). Images reconstructed with overlapping respiratory bins were sharper at the cost of longer reconstruction times. The CPU  + GPU implementation was over 17 times faster than the reference implementation, reconstructing images in 60 ± 1 s and hyper-scaled using multiple GPUs. Conclusion: Respiratory-resolved 4D-MRI reconstruction times can be reduced using high-performance computing methods for online workflows in MR-guided radiotherapy with potential applications in particle therapy.

Joint radial trajectory correction for accelerated T2 * mapping on an MR-Linac.

BACKGROUND: T2 * mapping can characterize tumor hypoxia, which may be associated with resistance to therapy. Acquiring T2 * maps during MR-guided radiotherapy could inform treatment adaptation by, for example, escalating the dose to resistant sub-volumes. PURPOSE: The purpose of this work is to demonstrate the feasibility of the accelerated T2 * mapping technique using model-based image reconstruction with integrated trajectory auto-correction (TrACR) for MR-guided radiotherapy on an MR-Linear accelerator (MR-Linac). MATERIALS AND METHODS: The proposed method was validated in a numerical phantom, where two T2 * mapping approaches (sequential and joint) were compared for different noise levels (0,0.1,0.5,1) and gradient delays ([1, -1] and [1, -2] in units of dwell time for x- and y-axis, respectively). Fully sampled k-space was retrospectively undersampled using two different undersampling patterns. Root mean square errors (RMSEs) were calculated between reconstructed T2 * maps and ground truth. In vivo data was acquired twice weekly in one prostate and one head and neck cancer patient undergoing treatment on a 1.5 T MR-Linac. Data were retrospectively undersampled and T2 * maps reconstructed, with and without trajectory corrections were compared. RESULTS: Numerical simulations demonstrated that, for all noise levels, T2 * maps reconstructed with a joint approach demonstrated less error compared to an uncorrected and sequential approach. For a noise level of 0.1, uniform undersampling and gradient delay [1, -1] (in units of dwell time for x- and y-axis, respectively), RMSEs for sequential and joint approaches were 13.01 and 9.32 ms, respectively, which reduced to 10.92 and 5.89 ms for a gradient delay of [1, 2]. Similarly, for alternate undersampling and gradient delay [1, -1], RMSEs for sequential and joint approaches were 9.80 and 8.90 ms, respectively, which reduced to 9.10 and 5.40 ms for gradient delay [1, 2]. For in vivo data, T2 * maps reconstructed with our proposed approach resulted in less artifacts and improved visual appearance compared to the uncorrected approach. For both prostate and head and neck cancer patients, T2 * maps reconstructed from different treatment fractions showed changes within the planning target volume (PTV). CONCLUSION: Using the proposed approach, a retrospective data-driven gradient delay correction can be performed, which is particularly relevant for hybrid devices, where full information on the machine configuration is not available for image reconstruction. T2 * maps were acquired in under 5 min and can be integrated into MR-guided radiotherapy treatment workflows, which minimizes patient burden and leaves time for additional imaging for online adaptive radiotherapy on an MR-Linac.

Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?

Background: The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac. Methods: Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume. Results: The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface. Conclusion: Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy.

Background: Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and Methods: Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results: For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions: The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change.

A phase space model of a Versa HD linear accelerator for application to Monte Carlo dose calculation in a real-time adaptive workflow.

PURPOSE: This study aims to develop and validate a simple geometric model of the accelerator head, from which a particle phase space can be calculated for application to fast Monte Carlo dose calculation in real-time adaptive photon radiotherapy. With this objective in view, the study investigates whether the phase space model can facilitate dose calculations which are compatible with those of a commercial treatment planning system, for convenient interoperability. MATERIALS AND METHODS: A dual-source model of the head of a Versa HD accelerator (Elekta AB, Stockholm, Sweden) was created. The model used parameters chosen to be compatible with those of 6-MV flattened and 6-MV flattening filter-free photon beams in the RayStation treatment planning system (RaySearch Laboratories, Stockholm, Sweden). The phase space model was used to calculate a photon phase space for several treatment plans, and the resulting phase space was applied to the Dose Planning Method (DPM) Monte Carlo dose calculation algorithm. Simple fields and intensity-modulated radiation therapy (IMRT) treatment plans for prostate and lung were calculated for benchmarking purposes and compared with the convolution-superposition dose calculation within RayStation. RESULTS: For simple square fields in a water phantom, the calculated dose distribution agrees to within ±2% with that from the commercial treatment planning system, except in the buildup region, where the DPM code does not model the electron contamination. For IMRT plans of prostate and lung, agreements of ±2% and ±6%, respectively, are found, with slightly larger differences in the high dose gradients. CONCLUSIONS: The phase space model presented allows convenient calculation of a phase space for application to Monte Carlo dose calculation, with straightforward translation of beam parameters from the RayStation beam model. This provides a basis on which to develop dose calculation in a real-time adaptive setting.

Online adaptive radiotherapy for head and neck cancers on the MR linear Accelerator: Introducing a novel modified Adapt-to-Shape approach.

INTRODUCTION: The Elekta Unity MR-Linac (MRL) has enabled adaptive radiotherapy (ART) for patients with head and neck cancers (HNC). Adapt-To-Shape-Lite (ATS-Lite) is a novel Adapt-to-Shape strategy that provides ART without requiring daily clinician presence to perform online target and organ at risk (OAR) delineation. In this study we compared the performance of our clinically-delivered ATS-Lite strategy against three Adapt-To-Position (ATP) variants: Adapt Segments (ATP-AS), Optimise Weights (ATP-OW), and Optimise Shapes (ATP-OS). METHODS: Two patients with HNC received radical-dose radiotherapy on the MRL. For each fraction, an ATS-Lite plan was generated online and delivered and additional plans were generated offline for each ATP variant. To assess the clinical acceptability of a plan for every fraction, twenty clinical goals for targets and OARs were assessed for all four plans. RESULTS: 53 fractions were analysed. ATS-Lite passed 99.9% of mandatory dose constraints. ATP-AS and ATP-OW each failed 7.6% of mandatory dose constraints. The Planning Target Volumes for 54 Gy (D95% and D98%) were the most frequently failing dose constraint targets for ATP. ATS-Lite median fraction times for Patient 1 and 2 were 40 mins 9 s (range 28 mins 16 s - 47 mins 20 s) and 32 mins 14 s (range 25 mins 33 s - 44 mins 27 s), respectively. CONCLUSIONS: Our early data show that the novel ATS-Lite strategy produced plans that fulfilled 99.9% of clinical dose constraints in a time frame that is tolerable for patients and comparable to ATP workflows. Therefore, ATS-Lite, which bridges the gap between ATP and full ATS, will be further utilised and developed within our institute and it is a workflow that should be considered for treating patients with HNC on the MRL.

Quantification of Differential Response of Tumour and Normal Cells to Microbeam Radiation in the Absence of FLASH Effects.

Microbeam radiotherapy (MRT) is a preclinical method of delivering spatially-fractionated radiotherapy aiming to improve the therapeutic window between normal tissue complication and tumour control. Previously, MRT was limited to ultra-high dose rate synchrotron facilities. The aim of this study was to investigate in vitro effects of MRT on tumour and normal cells at conventional dose rates produced by a bench-top X-ray source. Two normal and two tumour cell lines were exposed to homogeneous broad beam (BB) radiation, MRT, or were separately irradiated with peak or valley doses before being mixed. Clonogenic survival was assessed and compared to BB-estimated surviving fractions calculated by the linear-quadratic (LQ)-model. All cell lines showed similar BB sensitivity. BB LQ-model predictions exceeded the survival of cell lines following MRT or mixed beam irradiation. This effect was stronger in tumour compared to normal cell lines. Dose mixing experiments could reproduce MRT survival. We observed a differential response of tumour and normal cells to spatially fractionated irradiations in vitro, indicating increased tumour cell sensitivity. Importantly, this was observed at dose rates precluding the presence of FLASH effects. The LQ-model did not predict cell survival when the cell population received split irradiation doses, indicating that factors other than local dose influenced survival after irradiation.

Patterns of Care, Tolerability, and Safety of the First Cohort of Patients Treated on a Novel High-Field MR-Linac Within the MOMENTUM Study: Initial Results From a Prospective Multi-Institutional Registry.

PURPOSE: High-field magnetic resonance-linear accelerators (MR-Linacs), linear accelerators combined with a diagnostic magnetic resonance imaging (MRI) scanner and online adaptive workflow, potentially give rise to novel online anatomic and response adaptive radiation therapy paradigms. The first high-field (1.5T) MR-Linac received regulatory approval in late 2018, and little is known about clinical use, patient tolerability of daily high-field MRI, and toxicity of treatments. Herein we report the initial experience within the MOMENTUM Study (NCT04075305), a prospective international registry of the MR-Linac Consortium. METHODS AND MATERIALS: Patients were included between February 2019 and October 2020 at 7 institutions in 4 countries. We used descriptive statistics to describe the patterns of care, tolerability (the percentage of patients discontinuing their course early), and safety (grade 3-5 Common Terminology Criteria for Adverse Events v.5 acute toxicity within 3 months after the end of treatment). RESULTS: A total 943 patients participated in the MOMENTUM Study, 702 of whom had complete baseline data at the time of this analysis. Patients were primarily male (79%) with a median age of 68 years (range, 22-93) and were treated for 39 different indications. The most frequent indications were prostate (40%), oligometastatic lymph node (17%), brain (12%), and rectal (10%) cancers. The median number of fractions was 5 (range, 1-35). Six patients discontinued MR-Linac treatments, but none due to an inability to tolerate repeated high-field MRI. Of the 415 patients with complete data on acute toxicity at 3-month follow-up, 18 (4%) patients experienced grade 3 acute toxicity related to radiation. No grade 4 or 5 acute toxicity related to radiation was observed. CONCLUSIONS: In the first 21 months of our study, patterns of care were diverse with respect to clinical utilization, body sites, and radiation prescriptions. No patient discontinued treatment due to inability to tolerate daily high-field MRI scans, and the acute radiation toxicity experience was encouraging.

Traceable reference dosimetry in MRI guided radiotherapy using alanine: calibration and magnetic field correction factors of ionisation chambers.

Magnetic resonance imaging (MRI)-guided radiotherapy (RT) (MRIgRT) falls outside the scope of existing high energy photon therapy dosimetry protocols, because those protocols do not consider the effects of the magnetic field on detector response and on absorbed dose to water. The aim of this study is to evaluate and demonstrate the traceable measurement of absorbed dose in MRIgRT systems using alanine, made possible by the characterisation of alanine sensitivity to magnetic fields reported previously by Billaset al(2020Phys. Med. Biol.65115001), in a way which is compatible with existing standards and calibrations available for conventional RT. In this study, alanine is used to transfer absorbed dose to water to MRIgRT systems from a conventional linac. This offers an alternative route for the traceable measurement of absorbed dose to water, one which is independent of the transfer using ionisation chambers. The alanine dosimetry is analysed in combination with measurements with several Farmer-type chambers, PTW 30013 and IBA FC65-G, at six different centres and two different MRIgRT systems (Elekta Unity™ and ViewRay MRIdian™). The results are analysed in terms of the magnetic field correction factors, and in terms of the absorbed dose calibration coefficients for the chambers, determined at each centre. This approach to reference dosimetry in MRIgRT produces good consistency in the results, across the centres visited, at the level of 0.4% (standard deviation). Farmer-type ionisation chamber magnetic field correction factors were determined directly, by comparing calibrations in some MRIgRT systems with and without the magnetic field ramped up, and indirectly, by comparing calibrations in all the MRIgRT systems with calibrations in a conventional linac. Calibration coefficients in the MRIgRT systems were obtained with a standard uncertainty of 1.1% (Elekta Unity™) and 0.9% (ViewRay MRIdian™), for three different chamber orientations with respect to the magnetic field. The values obtained for the magnetic field correction factor in this investigation are consistent with those presented in the summary by de Pooteret al(2021Phys. Med. Biol.6605TR02), and would tend to support the adoption of a magnetic field correction factor which depends on the chamber type, PTW 30013 or IBA FC65-G.

Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula.

BACKGROUND AND PURPOSE: 4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. METHODS: Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. RESULTS: For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. CONCLUSION: Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

New target volume delineation and PTV strategies to further personalise radiotherapy.

Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient's unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient's gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy.

Cross-modality deep learning: Contouring of MRI data from annotated CT data only.

PURPOSE: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-segmentation algorithms for organs-at-risk and radiation targets. Current practice of manual segmentation is subjective and time-consuming. While deep learning-based algorithms offer ample opportunities to solve this problem, they typically require large datasets. However, medical imaging data are generally sparse, in particular annotated MR images for radiotherapy. In this study, we developed a method to exploit the wealth of publicly available, annotated CT images to generate synthetic MR images, which could then be used to train a convolutional neural network (CNN) to segment the parotid glands on MR images of head and neck cancer patients. METHODS: Imaging data comprised 202 annotated CT and 27 annotated MR images. The unpaired CT and MR images were fed into a 2D CycleGAN network to generate synthetic MR images from the CT images. Annotations of axial slices of the synthetic images were generated by propagating the CT contours. These were then used to train a 2D CNN. We assessed the segmentation accuracy using the real MR images as test dataset. The accuracy was quantified with the 3D Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) between manual and auto-generated contours. We benchmarked the approach by a comparison to the interobserver variation determined for the real MR images, as well as to the accuracy when training the 2D CNN to segment the CT images. RESULTS: The determined accuracy (DSC: 0.77±0.07, HD: 18.04±12.59mm, MSD: 2.51±1.47mm) was close to the interobserver variation (DSC: 0.84±0.06, HD: 10.85±5.74mm, MSD: 1.50±0.77mm), as well as to the accuracy when training the 2D CNN to segment the CT images (DSC: 0.81±0.07, HD: 13.00±7.61mm, MSD: 1.87±0.84mm). CONCLUSIONS: The introduced cross-modality learning technique can be of great value for segmentation problems with sparse training data. We anticipate using this method with any nonannotated MRI dataset to generate annotated synthetic MR images of the same type via image style transfer from annotated CT images. Furthermore, as this technique allows for fast adaptation of annotated datasets from one imaging modality to another, it could prove useful for translating between large varieties of MRI contrasts due to differences in imaging protocols within and between institutions.