Results from the AAPM Task Group 324 respiratory motion management in radiation oncology survey.

PURPOSE: To quantify the clinical practice of respiratory motion management in radiation oncology. METHODS: A respiratory motion management survey was designed and conducted based on clinician survey guidelines. The survey was administered to American Association of Physicists in Medicine (AAPM) members on 17 August 2020 and closed on 13 September 2020. RESULTS: A total of 527 respondents completed the entire survey and 651 respondents completed part of the survey, with the partially completed surveys included in the analysis. Overall, 84% of survey respondents used deep inspiration breath hold for left-sided breast cancer. Overall, 83% of respondents perceived respiratory motion management for thoracic and abdominal cancer radiotherapy patients to be either very important or required. Overall, 95% of respondents used respiratory motion management for thoracic and abdominal sites, with 36% of respondents using respiratory motion management for at least 90% of thoracic and abdominal patients. The majority (60%) of respondents used the internal target volume method to treat thoracic and abdominal cancer patients, with 25% using breath hold or abdominal compression and 13% using gating or tracking. CONCLUSIONS: A respiratory motion management survey has been completed by AAPM members. Respiratory motion management is generally considered very important or required and is widely used for breast, thoracic, and abdominal cancer treatments.

Real-time high spatial resolution dose verification in stereotactic motion adaptive arc radiotherapy.

PURPOSE: Radiation treatments delivered with real-time multileaf collimator (MLC) tracking currently lack fast pretreatment or real-time quality assurance. The purpose of this study is to test a 2D silicon detector, MagicPlate-512 (MP512), in a complex clinical environment involving real-time reconfiguration of the MLC leaves during target tracking. METHODS: MP512 was placed in the center of a solid water phantom and mounted on a motion platform used to simulate three different patient motions. Electromagnetic target tracking was implemented using the Calypso system (Varian Medical Systems, Palo Alto, CA, USA) and an MLC tracking software. A two-arc VMAT plan was delivered and 2D dose distributions were reconstructed by MP512, EBT3 film, and the Eclipse treatment planning system (TPS). Dose maps were compared using gamma analysis with 2%/2 mm and 3%/3 mm acceptance criteria. Dose profiles were generated in sup-inf and lateral directions to show the agreement of MP512 to EBT3 and to highlight the efficacy of the MLC tracking system in mitigating the effect of the simulated patient motion. RESULTS: Using a 3%/3 mm acceptance criterion for 2D gamma analysis, MP512 to EBT3 film agreement was 99% and MP512 to TPS agreement was 100%. For a 2%/2 mm criterion, the agreement was 95% and 98%, respectively. Full width at half maximum and 80%/20% penumbral width of the MP512 and EBT3 dose profiles agreed within 1 mm and 0.5 mm, respectively. Patient motion increased the measured dose profile penumbral width by nearly 2 mm (with respect to the no-motion case); however, the MLC tracking strategy was able to mitigate 80% of this effect. CONCLUSIONS: MP512 is capable of high spatial resolution 2D dose reconstruction during adaptive MLC tracking, including arc deliveries. It shows potential as an effective tool for 2D small field dosimetry and pretreatment quality assurance for MLC tracking modalities. These results provide confidence that detector-based pretreatment dosimetry is clinically feasible despite fast real-time MLC reconfigurations.

Technical note: TROG 15.01 SPARK trial multi-institutional imaging dose measurement.

PURPOSE: The Trans-Tasman Radiation Oncology Group (TROG) 15.01 Stereotactic Prostate Adaptive Radiotherapy utilizing Kilovoltage intrafraction monitoring (SPARK) trial is a multicenter trial using Kilovoltage Intrafraction Monitoring (KIM) to monitor prostate position during the delivery of prostate radiation therapy. KIM increases the accuracy of prostate radiation therapy treatments and allows for hypofractionation. However, an additional imaging dose is delivered to the patient. A standardized procedure to determine the imaging dose per frame delivered using KIM was developed and applied at four radiation therapy centers on three different types of linear accelerator. METHODS: Dose per frame for kilovoltage imaging in fluoroscopy mode was measured in air at isocenter using an ion chamber. Beam quality and dose were determined for a Varian Clinac iX linear accelerator, a Varian Trilogy, four Varian Truebeams and one Elekta Synergy at four different radiation therapy centers. The imaging parameters used on the Varian machines were 125 kV, 80 mA, and 13 ms. The Elekta machine was measured at 120 kV, 80 mA, and 12 ms. Absorbed doses to the skin and the prostate for a typical SBRT prostate treatment length were estimated according to the IPEMB protocol. RESULTS: The average dose per kV frame to the skin was 0.24 ± 0.03 mGy. The average estimated absorbed dose to the prostate for all five treatment fractions across all machines measured was 39.9 ± 2.6 mGy for 1 Hz imaging, 199.7 ± 13.2 mGy for 5 Hz imaging and 439.3 ± 29.0 mGy for 11 Hz imaging. CONCLUSIONS: All machines measured agreed to within 20%. Additional dose to the prostate from using KIM is at most 1.3% of the prescribed dose of 36.25 Gy in five fractions delivered during the trial.

An EPID-based system for gantry-resolved MLC quality assurance for VMAT.

Multileaf collimator (MLC) positions should be precisely and independently mea-sured as a function of gantry angle as part of a comprehensive quality assurance (QA) program for volumetric-modulated arc therapy (VMAT). It is also ideal that such a QA program has the ability to relate MLC positional accuracy to patient-specific dosimetry in order to determine the clinical significance of any detected MLC errors. In this work we propose a method to verify individual MLC trajectories during VMAT deliveries for use as a routine linear accelerator QA tool. We also extend this method to reconstruct the 3D patient dose in the treatment planning sys-tem based on the measured MLC trajectories and the original DICOM plan file. The method relies on extracting MLC positions from EPID images acquired at 8.41fps during clinical VMAT deliveries. A gantry angle is automatically tagged to each image in order to obtain the MLC trajectories as a function of gantry angle. This analysis was performed for six clinical VMAT plans acquired at monthly intervals for three months. The measured trajectories for each delivery were compared to the MLC positions from the DICOM plan file. The maximum mean error detected was 0.07 mm and a maximum root-mean-square error was 0.8 mm for any leaf of any delivery. The sensitivity of this system was characterized by introducing random and systematic MLC errors into the test plans. It was demonstrated that the system is capable of detecting random and systematic errors on the range of 1-2mm and single leaf calibration errors of 0.5 mm. The methodology developed in the work has potential to be used for efficient routine linear accelerator MLC QA and pretreatment patient-specific QA and has the ability to relate measured MLC positional errors to 3D dosimetric errors within a patient volume.

Audiovisual biofeedback breathing guidance for lung cancer patients receiving radiotherapy: a multi-institutional phase II randomised clinical trial.

BACKGROUND: There is a clear link between irregular breathing and errors in medical imaging and radiation treatment. The audiovisual biofeedback system is an advanced form of respiratory guidance that has previously demonstrated to facilitate regular patient breathing. The clinical benefits of audiovisual biofeedback will be investigated in an upcoming multi-institutional, randomised, and stratified clinical trial recruiting a total of 75 lung cancer patients undergoing radiation therapy. METHODS/DESIGN: To comprehensively perform a clinical evaluation of the audiovisual biofeedback system, a multi-institutional study will be performed. Our methodological framework will be based on the widely used Technology Acceptance Model, which gives qualitative scales for two specific variables, perceived usefulness and perceived ease of use, which are fundamental determinants for user acceptance. A total of 75 lung cancer patients will be recruited across seven radiation oncology departments across Australia. Patients will be randomised in a 2:1 ratio, with 2/3 of the patients being recruited into the intervention arm and 1/3 in the control arm. 2:1 randomisation is appropriate as within the interventional arm there is a screening procedure where only patients whose breathing is more regular with audiovisual biofeedback will continue to use this system for their imaging and treatment procedures. Patients within the intervention arm whose free breathing is more regular than audiovisual biofeedback in the screen procedure will remain in the intervention arm of the study but their imaging and treatment procedures will be performed without audiovisual biofeedback. Patients will also be stratified by treating institution and for treatment intent (palliative vs. radical) to ensure similar balance in the arms across the sites. Patients and hospital staff operating the audiovisual biofeedback system will complete questionnaires to assess their experience with audiovisual biofeedback. The objectives of this clinical trial is to assess the impact of audiovisual biofeedback on breathing motion, the patient experience and clinical confidence in the system, clinical workflow, treatment margins, and toxicity outcomes. DISCUSSION: This clinical trial marks an important milestone in breathing guidance studies as it will be the first randomised, controlled trial providing the most comprehensive evaluation of the clinical impact of breathing guidance on cancer radiation therapy to date. This study is powered to determine the impact of AV biofeedback on breathing regularity and medical image quality. Objectives such as determining the indications and contra-indications for the use of AV biofeedback, evaluation of patient experience, radiation toxicity occurrence and severity, and clinician confidence will shed light on the design of future phase III clinical trials. TRIAL REGISTRATION: This trial has been registered with the Australian New Zealand Clinical Trials Registry (ANZCTR), its trial ID is ACTRN12613001177741 .

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

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.

Time-resolved dose reconstruction by motion encoding of volumetric modulated arc therapy fields delivered with and without dynamic multi-leaf collimator tracking.

BACKGROUND: Organ motion during treatment delivery in radiotherapy (RT) may lead to deterioration of the planned dose, but can be mitigated by dynamic multi-leaf collimator (DMLC) tracking. The purpose of this study was to implement and experimentally validate a method for time-resolved motion including dose reconstruction for volumetric modulated arc therapy (VMAT) treatments delivered with and without DMLC tracking. MATERIAL AND METHODS: Tracking experiments were carried out on a linear accelerator (Trilogy, Varian) with a prototype DMLC tracking system. A motion stage carrying a biplanar dosimeter phantom (Delta4PT, Scandidos) reproduced eight representative clinical tumor trajectories (four lung, four prostate). For each trajectory, two single-arc 6 MV VMAT treatments with low and high modulation were delivered to the moving phantom with and without DMLC tracking. An existing in-house developed program that adds target motion to treatment plans was extended with the ability to split an arc plan into any number of sub-arcs, allowing the calculated dose for different parts of the treatment to be examined individually. For each VMAT sub-arc, reconstructed and measured doses were compared using dose differences and 3%/3 mm γ-tests. RESULTS: For VMAT sub-arcs the reconstructed dose distributions had a mean root-mean-square (rms) dose difference of 2.1% and mean γ failure rate of 2.0% when compared with the measured doses. For final accumulated doses the mean rms dose difference was 1.6% and the γ failure rate was 0.7%. CONCLUSION: The time-resolved motion including dose reconstruction was experimentally validated for complex tracking and non-tracking treatments with patient-measured tumor motion trajectories. The reconstructed dose will be of high value for evaluation of treatment plan robustness facing organ motion and adaptive RT.

A kinematics-based method for creating deformed patient-derived head and neck CT scans.

CT scans of the head and neck have multiple clinical uses, and simulating deformation of these CT scans allows for predicting patient motion and data augmentation for machine-learning methods. Current methods for creating patient-derived deformed CT scans require multiple scans or use unrealistic head and neck motion. This paper describes the CTHeadDeformation software package which allows for realistic synthetic deformation of head and neck CT scans for small amounts of motion. CTHeadDeformation is a python-based package that uses a kinematics-based approach using anatomical landmarks, and rigid/non-rigid registration to create a realistic patient-derived deformed CT scan. CTHeadDeformation is also designed for simple clinical implementation. The CTHeadDeformation software package was demonstrated on a head and neck CT scan of one patient. The CT scan was deformed in the anterior-posterior, superior-inferior, and left-right directions. Internal organ motion and more complex combination motions were also simulated. The results showed the patient's CT scan was able to be deformed in a way that preserved the shape and location of the anatomy.Clinical Relevance- This method allows for the realistic simulation of head and neck motion in CT scans. Clinical applications including simulating how patient motion affects radiation therapy treatment effectiveness. The CTHeadDeformation software can also be used to train machine-learning networks that are robust to patient motion, or to generate ground truth images for imaging or segmentation grand challenges.

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

BACKGROUND: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery. PURPOSE: The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment. METHOD: In this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient-specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data. The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no-tracking segmentations from the original contoured GTV, the current standard-of-care. This hypothesis was tested using the 1-tailed Mann-Whitney U-test. RESULTS: The magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no-tracking segmentation, but did not significantly increase the DSC (p = 0.294). CONCLUSIONS: The accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring methods to compensate for tumor motion during treatment, ensuring more accurate dose delivery and enabling better H&N cancer patient outcomes.

Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy.

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.

Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial.

PURPOSE: The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging. METHODS AND MATERIALS: Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test. RESULTS: Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n = 24 for CFRT; n = 9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints. CONCLUSIONS: This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.

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.

The MANGO study: a prospective investigation of oxygen enhanced and blood-oxygen level dependent MRI as imaging biomarkers of hypoxia in glioblastoma.

Background: Glioblastoma (GBM) is the most aggressive type of brain cancer, with a 5-year survival rate of ~5% and most tumours recurring locally within months of first-line treatment. Hypoxia is associated with worse clinical outcomes in GBM, as it leads to localized resistance to radiotherapy and subsequent tumour recurrence. Current standard of care treatment does not account for tumour hypoxia, due to the challenges of mapping tumour hypoxia in routine clinical practice. In this clinical study, we aim to investigate the role of oxygen enhanced (OE) and blood-oxygen level dependent (BOLD) MRI as non-invasive imaging biomarkers of hypoxia in GBM, and to evaluate their potential role in dose-painting radiotherapy planning and treatment response assessment. Methods: The primary endpoint is to evaluate the quantitative and spatial correlation between OE and BOLD MRI measurements and [18F]MISO values of uptake in the tumour. The secondary endpoints are to evaluate the repeatability of MRI biomarkers of hypoxia in a test-retest study, to estimate the potential clinical benefits of using MRI biomarkers of hypoxia to guide dose-painting radiotherapy, and to evaluate the ability of MRI biomarkers of hypoxia to assess treatment response. Twenty newly diagnosed GBM patients will be enrolled in this study. Patients will undergo standard of care treatment while receiving additional OE/BOLD MRI and [18F]MISO PET scans at several timepoints during treatment. The ability of OE/BOLD MRI to map hypoxic tumour regions will be evaluated by assessing spatial and quantitative correlations with areas of hypoxic tumour identified via [18F]MISO PET imaging. Discussion: MANGO (Magnetic resonance imaging of hypoxia for radiation treatment guidance in glioblastoma multiforme) is a diagnostic/prognostic study investigating the role of imaging biomarkers of hypoxia in GBM management. The study will generate a large amount of longitudinal multimodal MRI and PET imaging data that could be used to unveil dynamic changes in tumour physiology that currently limit treatment efficacy, thereby providing a means to develop more effective and personalised treatments.

The dosimetric error due to uncorrected tumor rotation during real-time adaptive prostate stereotactic body radiation therapy.

BACKGROUND: During prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correction alone. PURPOSE: The purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees-of-freedom tumor motion monitoring technology. METHODS: The delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5-fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as T r e s $T_{res}$ . Prostate intrafractional rotational motion R u n c o r r $R_{uncorr}$ was recorded but not corrected. The dose differences from the planned dose due to T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ , ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and due to T r e s $T_{res}$ alone, ΔD( T r e s $T_{res}$ ), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation, R u n c o r r $R_{uncorr}$ was then quantified: Δ D R e s i d u a l $\Delta D_{Residual}$ = ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) - ΔD( T res ${T}_{\textit{res}}$ ). RESULTS: Fractional data analysis shows that the dose differences from the plan (both ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and ΔD( T r e s $T_{res}$ )) for CTV D98 was less than 5% in all treatment fractions. ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation, R u n c o r r $R_{uncorr}$ induced residual dose error, Δ D R e s i d u a l $\Delta D_{Residual}$ , resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and ∑D( T r e s $T_{res}$ ), was always within 5% of the planned dose for all four structures for every patient. CONCLUSIONS: The dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3-5 mm CTV-PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetric accuracy.

The impact of audio-visual biofeedback on 4D PET images: results of a phantom study.

PURPOSE: Irregular breathing causes motion blurring artifacts in 4D PET images. Audiovisual (AV) biofeedback has been demonstrated to improve breathing regularity. To investigate the hypothesis that, compared with free breathing, motion blurring artifacts are reduced with AV biofeedback, the authors performed the first experimental phantom-based quantification of the impact of AV biofeedback on 4D PET image quality. METHODS: The authors acquired 4D PET dynamic phantom images with AV biofeedback and free breathing by moving a phantom programmed with AV biofeedback trained and free breathing respiratory traces of ten healthy subjects. The authors also acquired stationary phantom images for reference. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The authors quantified motion blurring using the target diameter, Dice coefficient and recovery coefficient (RC) metrics to estimate the effect of motion. RESULTS: The average increase in target diameter for AV biofeedback was 0.6±1.6mm (4.7±13%), which was significantly (p<0.001) smaller than for free breathing 1.3±2.2mm (9.1±19%). The average Dice coefficient for AV biofeedback was 0.90±0.07, which was significantly (p<0.001) larger than for free breathing (0.88±0.10). The RCs for AV biofeedback were consistently higher than those for free breathing and comparable to those for stationary targets. However, for RCs the impact of target sizes was more dominant than that of motion. In addition, the authors observed large variations in the results with respect to target sizes, subject traces and respiratory bins due to partial volume effects and respiratory motion irregularity. CONCLUSIONS: The results indicate that AV biofeedback can significantly reduce motion blurring artifacts and may facilitate improved identification and localization of lung tumors in 4D PET images. The results justify proceeding with clinical studies to quantify the impact of AV biofeedback on 4D PET image quality and tumor detectability.

Optimising multi-target multileaf collimator tracking using real-time dose for locally advanced prostate cancer patients.

Objective. The accuracy of radiotherapy for patients with locally advanced cancer is compromised by independent motion of multiple targets. To date, MLC tracking approaches have used 2D geometric optimisation where the MLC aperture shape is simply translated to correspond to the target's motion, which results in sub-optimal delivered dose. To address this limitation, a dose-optimised multi-target MLC tracking method was developed and evaluated through simulated locally advanced prostate cancer treatments.Approach. A dose-optimised multi-target tracking algorithm that adapts the MLC aperture to minimise 3D dosimetric error was developed for moving prostate and static lymph node targets. A fast dose calculation algorithm accumulated the planned dose to the prostate and lymph node volumes during treatment in real time, and the MLC apertures were recalculated to minimise the difference between the delivered and planned dose with the included motion. Dose-optimised tracking was evaluated by simulating five locally advanced prostate plans and three prostate motion traces with a relative interfraction displacement. The same simulations were performed using geometric-optimised tracking and no tracking. The dose-optimised, geometric-optimised, and no tracking results were compared with the planned doses using a 2%/2 mmγcriterion.Main results. The mean dosimetric error was lowest for dose-optimised MLC tracking, withγ-failure rates of 12% ± 8.5% for the prostate and 2.2% ± 3.2% for the nodes. Theγ-failure rates for geometric-optimised MLC tracking were 23% ± 12% for the prostate and 3.6% ± 2.5% for the nodes. When no tracking was used, theγ-failure rates were 37% ± 28% for the prostate and 24% ± 3.2% for the nodes.Significance. This study developed a dose-optimised multi-target MLC tracking method that minimises the difference between the planned and delivered doses in the presence of intrafraction motion. When applied to locally advanced prostate cancer, dose-optimised tracking showed smaller errors than geometric-optimised tracking and no tracking for both the prostate and nodes.

Reducing 4DCBCT imaging dose and time: exploring the limits of adaptive acquisition and motion compensated reconstruction.

This study investigates the dose and time limits of adaptive 4DCBCT acquisitions (adaptive-acquisition) compared with current conventional 4DCBCT acquisition (conventional-acquisition). We investigate adaptive-acquisitions as low as 60 projections (∼25 s scan, 6 projections per respiratory phase) in conjunction with emerging image reconstruction methods. 4DCBCT images from 20 patients recruited into the adaptive CT acquisition for personalized thoracic imaging clinical study (NCT04070586) were resampled to simulate faster and lower imaging dose acquisitions. All acquisitions were reconstructed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), motion compensated FDK (MCFDK), motion compensated MKB (MCMKB) and simultaneous motion estimation and image reconstruction (SMEIR) algorithms. All reconstructions were compared against conventional-acquisition 4DFDK-reconstruction using Structural SIMilarity Index (SSIM), signal-to-noise ratio (SNR), contrast-to-noise-ratio (CNR), tissue interface sharpness diaphragm (TIS-D), tissue interface sharpness tumor (TIS-T) and center of mass trajectory (COMT) for difference in diaphragm and tumor motion. All reconstruction methods using 110-projection adaptive-acquisition (11 projections per respiratory phase) had a SSIM of greater than 0.92 relative to conventional-acquisition 4DFDK-reconstruction. Relative to conventional-acquisition 4DFDK-reconstruction, 110-projection adaptive-acquisition MCFDK-reconstructions images had 60% higher SNR, 10% higher CNR, 30% higher TIS-T and 45% higher TIS-D on average. The 110-projection adaptive-acquisition SMEIR-reconstruction images had 123% higher SNR, 90% higher CNR, 96% higher TIS-T and 60% higher TIS-D on average. The difference in diaphragm and tumor motion compared to conventional-acquisition 4DFDK-reconstruction was within submillimeter accuracy for all acquisition reconstruction methods. Adaptive-acquisitions resulted in faster scans with lower imaging dose and equivalent or improved image quality compared to conventional-acquisition. Adaptive-acquisition with motion compensated-reconstruction enabled scans with as low as 110 projections to deliver acceptable image quality. This translates into 92% lower imaging dose and 80% less scan time than conventional-acquisition.

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.

Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration.

Background and purpose: Glioblastoma (GBM) patients have a dismal prognosis. Tumours typically recur within months of surgical resection and post-operative chemoradiation. Multiparametric magnetic resonance imaging (mpMRI) biomarkers promise to improve GBM outcomes by identifying likely regions of infiltrative tumour in tumour probability (TP) maps. These regions could be treated with escalated dose via dose-painting radiotherapy to achieve higher rates of tumour control. Crucial to the technical validation of dose-painting using imaging biomarkers is the repeatability of the derived dose prescriptions. Here, we quantify repeatability of dose-painting prescriptions derived from mpMRI. Materials and methods: TP maps were calculated with a clinically validated model that linearly combined apparent diffusion coefficient (ADC) and relative cerebral blood volume (rBV) or ADC and relative cerebral blood flow (rBF) data. Maps were developed for 11 GBM patients who received two mpMRI scans separated by a short interval prior to chemoradiation treatment. A linear dose mapping function was applied to obtain dose-painting prescription (DP) maps for each session. Voxel-wise and group-wise repeatability metrics were calculated for parametric, TP and DP maps within radiotherapy margins. Results: DP maps derived from mpMRI were repeatable between imaging sessions (ICC > 0.85). ADC maps showed higher repeatability than rBV and rBF maps (Wilcoxon test, p = 0.001). TP maps obtained from the combination of ADC and rBF were the most stable (median ICC: 0.89). Conclusions: Dose-painting prescriptions derived from a mpMRI model of tumour infiltration have a good level of repeatability and can be used to generate reliable dose-painting plans for GBM patients.

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.