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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Real-time dose-guidance in radiotherapy: Proof of principle.

PURPOSE: The outcome of radiotherapy is a direct consequence of the dose delivered to the patient. Yet image-guidance and motion management to date focus on geometrical considerations as a practical surrogate for dose. Here, we propose real-time dose-guidance realized through continuous motion-including dose reconstructions and demonstrate this new concept in simulated liver stereotactic body radiotherapy (SBRT) delivery. MATERIALS AND METHODS: During simulated liver SBRT delivery, in-house developed software performed real-time motion-including reconstruction of the tumor dose delivered so far and continuously predicted the remaining fraction tumor dose. The total fraction dose was estimated as the sum of the delivered and predicted doses, both with and without the emulated couch correction that maximized the predicted final CTV D95% (minimum dose to 95% of the clinical target volume). Dose-guided treatments were simulated for 15 liver SBRT patients previously treated with tumor motion monitoring, using both sinusoidal tumor motion and the actual patient-measured motion. A dose-guided couch correction was triggered if it improved the predicted final CTV D95% with 3, 4 or 5 %-points. The final CTV D95% of the dose-guidance strategy was compared with simulated treatments using geometry guided couch corrections (Wilcoxon signed-rank test). RESULTS: Compared to geometry guidance, dose-guided couch corrections improved the median CTV D95% with 0.5-1.5 %-points (p < 0.01) for sinusoidal motions and with 0.9 %-points (p < 0.01, 3 %-points trigger threshold), 0.5 %-points (p = 0.03, 4 %-points threshold) and 1.2 %-points (p = 0.09, 5 %-points threshold) for patient-measured tumor motion. CONCLUSION: Real-time dose-guidance was proposed and demonstrated to be superior to geometrical adaptation in liver SBRT simulations.

The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose.

BACKGROUND AND PURPOSE: We present the first implementation of Adaptive 4D cone beam CT (4DCBCT) that adapts the image hardware (gantry rotation speed and kV projections) in response to the patient's real-time respiratory signal. Adaptive 4DCBCT was applied on lung cancer patients to reduce the scan time and imaging dose in the ADaptive CT Acquisition for Personalised Thoracic imaging (ADAPT) trial. MATERIALS AND METHODS: The ADAPT technology measures the patient's real-time respiratory signal and uses mathematical optimisation and external circuitry attached to the linear accelerator to modulate the gantry rotation speed and kV projection rate to reduce scan times and imaging dose. For each patient, ADAPT scans were acquired on two treatment fractions and reconstructed with a motion compensated reconstruction algorithm and compared to the current state-of-the-art four-minute 4DCBCT acquisition (conventional 4DCBCT). We report on the scan time, imaging dose and image quality for the first four adaptive 4DCBCT patients. RESULTS: The ADAPT imaging dose was reduced by 85% and scan times were 73 ± 12 s representing a 70% reduction compared to the 240 s conventional 4DCBCT scan. The contrast-to-noise ratio was improved from 9.2 ± 3.9 with conventional 4DCBCT to 11.7 ± 4.1 with ADAPT. DISCUSSION: The ADAPT trial represents the first time that gantry rotation speed and projection acquisition have been adapted and optimised in real-time in response to changes in the patient's breathing. ADAPT demonstrates substantially reduced scan times and imaging dose for clinical 4DCBCT imaging that could enable more efficient and optimised lung cancer radiotherapy.

First experimental evaluation of multi-target multileaf collimator tracking during volumetric modulated arc therapy for locally advanced prostate cancer.

PURPOSE: Locally advanced and oligometastatic cancer patients require radiotherapy treatment to multiple independently moving targets. There is no existing commercial solution that can simultaneously track and treat multiple targets. This study experimentally implemented and evaluated a real-time multi-target tracking system for locally advanced prostate cancer. METHODS: Real-time multi-target MLC tracking was integrated with 3D x-ray image guidance on a standard linac. Three locally advanced prostate cancer treatment plans were delivered to a static lymph node phantom and dynamic prostate phantom that reproduced three prostate trajectories. Treatments were delivered using multi-target MLC tracking, single-target MLC tracking, and no tracking. Doses were measured using Gafchromic film placed in the dynamic and static phantoms. Dosimetric error was quantified by the 2%/2 mm gamma failure rate. Geometric error was evaluated as the misalignment between target and aperture positions. The multi-target tracking system latency was measured. RESULTS: The mean (range) gamma failure rates for the prostate and lymph nodes, were 18.6% (5.2%, 28.5%) and 7.5% (1.1%, 13.7%) with multi-target tracking, 7.9% (0.7%, 15.4%) and 37.8% (18.0%, 57.9%) with single-target tracking, and 38.1% (0.6%, 75.3%) and 37.2% (29%, 45.3%) without tracking. Multi-target tracking had the lowest geometric error with means and standard deviations within 0.2 ± 1.5 for the prostate and 0.0 ± 0.3 mm for the lymph nodes. The latency was 730 ± 20 ms. CONCLUSION: This study presented the first experimental implementation of multi-target tracking to independently track prostate and lymph node displacement during VMAT. Multi-target tracking reduced dosimetric and geometric errors compared to single-target tracking and no tracking.

Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction.

Conventional 4DCBCT captures 1320 projections across 4 min. Adaptive 4DCBCT has been developed to reduce imaging dose and scan time. This study investigated reconstruction algorithms that best complement adaptive 4DCBCT acquisition for reducing imaging dose and scan time whilst maintaining or improving image quality compared to conventional 4DCBCT acquisition using real patient data from the first 10 adaptive 4DCBCT patients. Adaptive 4DCBCT was implemented in the ADaptive CT Acquisition for Personalized Thoracic imaging clinical trial. Adaptive 4DCBCT modulates gantry rotation speed and kV acquisition rate in response to the patient's real-time respiratory signal, ensuring even angular spacing between projections at each respiratory phase. We examined the first 10 lung cancer radiotherapy patients that received adaptive 4DCBCT. Fast, 200-projection scans over 60-80 s, and slower, 600-projection scans over ∼240 s, were obtained after routine patient treatment and compared against conventional 4DCBCT acquisition. Adaptive 4DCBCT acquisitions were reconstructed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), Motion Compensated FDK (MCFDK) and Motion Compensated MKB (MCMKB) algorithms. Reconstructions were assessed via, Structural SIMilarity (SSIM), Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), Tissue Interface Sharpness of Diaphragm (TIS-D) and Tumor (TIS-T). The 200- and 600-projection adaptive 4DCBCT acquisition corresponded to 85% and 55% reduction in imaging dose, shorter and similar scan times of approximately 90 s and 236 s respectively, compared to conventional 4DCBCT acquisition. 200- and 600-projection adaptive 4DCBCT reconstructions achieved more than 0.900 SSIM relative to conventional 4DCBCT acquisition. Compared to conventional 4DCBCT acquisition, 200-projection adaptive 4DCBCT reconstructions achieved higher SNR, CNR, TIS-T, TIS-D with motion compensated algorithms, MCFDK (208%, 159%, 174%, 247%) and MCMKB (214%, 173%, 266%, 245%) respectively. The 200-projection adaptive 4DCBCT MCFDK- and MCMKB-reconstruction results show image quality improvements are possible even with 85% fewer projections acquired. We established acquisition-reconstruction protocols that provide substantial reductions in imaging time and dose whilst improving image quality.

Pre-treatment and real-time image guidance for a fixed-beam radiotherapy system.

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

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.