BACKGROUND:During radiation therapy, the natural movement of organs can lead to underdosing the cancer and overdosing the healthy tissue, compromising treatment efficacy. Real-time image-guided adaptive radiation therapy can track the tumour and account for the motion. Typically, fiducial markers are implanted as a surrogate for the tumour position due to the low radiographic contrast of soft tissues in kilovoltage (kV) images. A segmentation approach that does not require markers would eliminate the costs, delays, and risks associated with marker implantation. METHODS:We trained patient-specific conditional Generative Adversarial Networks for prostate segmentation in kV images. The networks were trained using synthetic kV images generated from each patient's own imaging and planning data, which are available prior to the commencement of treatment. We validated the networks on two treatment fractions from 30 patients using multi-centre data from two clinical trials. RESULTS:Here, we present a large-scale proof-of-principle study of x-ray-based markerless prostate segmentation for globally available cancer therapy systems. Our results demonstrate the feasibility of a deep learning approach using kV images to track prostate motion across the entire treatment arc for 30 patients with prostate cancer. The mean absolute deviation is 1.4 and 1.6 mm in the anterior-posterior/lateral and superior-inferior directions, respectively. CONCLUSIONS:Markerless segmentation via deep learning may enable real-time image guidance on conventional cancer therapy systems without requiring implanted markers or additional hardware, thereby expanding access to real-time adaptive radiation therapy.

Objective: Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patient motion management methods. The purpose of this paper is to investigate a deep learning-based method to segment H&N tumors in patient kilovoltage (kV) x-ray images to enable real-time H&N tumor tracking during RT. Approach: An ethics-approved clinical study collected data from 17 H&N cancer patients undergoing conventional H&N RT. For each patient, personalized conditional Generative Adversarial Networks (cGANs) were trained to segment H&N tumors in kV x-ray images. Network training data were derived from each patient’s planning CT and contoured gross tumor volumes (GTV). For each training epoch, the planning CT and GTV were deformed and forward projected to create the training dataset. The testing data consisted of kV x-ray images used to reconstruct the pre-treatment CBCT volume for the first, middle and end fractions. The ground truth tumor locations were derived by deformably registering the planning CT to the pre-treatment CBCT and then deforming the GTV and forward projecting the deformed GTV. The generated cGAN segmentations were compared to ground truth tumor segmentations using the absolute magnitude of the centroid error and the mean surface distance (MSD) metrics. Main Results: The centroid error for the nasopharynx (n=4), oropharynx (n=9) and larynx (n=4) patients was 1.5±0.9mm, 2.4±1.6mm, 3.5±2.2mm respectively and the MSD was 1.5±0.3mm, 1.9±0.9mm and 2.3±1.0mm respectively. There was a weak correlation between the centroid error and the LR tumor location (r=0.41), which was higher for oropharynx patients (r=0.77). Significance: The paper reports on markerless H&N tumor detection accuracy using kV images. Accurate tracking of H&N tumors can enable more precise RT leading to mask-free RT enabling better patient outcomes.

Objective.Head and neck (H&N) volumetric imaging using a standard linear accelerator during treatment delivery could enable motion management strategies to improve geometric accuracy and avoid critical organs at risk (OARs). We developed a Kalman filter (KF) tracking approach to image 3D H&N anatomy in real-time using 2D x-ray images.Approach.Our method produces a 3D image corresponding to each 2D projection at the time the projection is acquired. A KF incorporates 3D-to-2D projection geometry to weight the prior 3D deformable vector field (DVF) and 2D DVF measurement generated by registering each 2D x-ray image to a 2D image generated using the planning computed tomography (CT) image. We performed a simulation study using the planning CT image and 5 minute surface motion traces for 12 H&N cancer patients. 2D intra-treatment images served as input to obtain the 3D DVF and resultant 3D images. We evaluated the KF tracking performance on the target and critical structures using centroid error, Hausdorff distance and Dice coefficient metrics. The results were compared to those from the current standard of care, no tracking.Results.Using KF tracking, we obtained a mean tumour centroid error of -0.2 ± 0.6 mm, 0.3 ± 0.8 mm, and 0.1 ± 0.4 mm in the left-right, anterior-posterior and superior-inferior axes respectively, compared to 0.9 ± 1.0 mm, -0.8 ± 1.1 mm, and -0.1 ± 1.2 mm with no tracking. The 95th percentile Hausdorff distance for the tumour contour was 1.2 ± 0.6 mm with KF tracking and 2.0 ± 1.2 mm without tracking. The Dice coefficients were 0.91 ± 0.08, 0.93 ± 0.04, 0.93 ± 0.05, 0.79 ± 0.18, and 0.89 ± 0.06 for the tumour, brainstem, left parotid gland, optic chiasm, and spinal cord respectively with KF tracking and 0.78 ± 0.17, 0.84 ± 0.06, 0.83 ± 0.08, 0.39 ± 0.14, and 0.70 ± 0.13 respectively with no tracking.Significance.Volumetric imaging with a standard linear accelerator was investigated using KF tracking with H&N patient data. The findings demonstrate accurate tracking of both the tumour and OARs, indicating potential applicability for the proposed method in a clinical translation setting.

Objective. Cone-beam CT (CBCT) technological advances for linear accelerators (Linacs) have led to CBCT imaging in <20 s, which can reduce radiation therapy treatment times. However, these rapid CBCT scans only allow for 3DCBCT images. In this paper we evaluate 4DCBCT reconstruction methods for rapid acquisition 3DCBCT protocol scans using an anthropomorphic breathing phantom.Approach. We evaluate two previously developed motion-compensated Feldkamp-Davis-Kress (MCFDK) methods, using a prior-motion model (MCFDK-Prior) and a data-driven MCFDK method (MCFDK-DD), on CBCT images of the phantom using an Ethos linac. The deformable phantom lungs contained three synthetic tumours and a commercial phantom motion platform with a sinusoidal breathing pattern. The phantom was imaged in free-breathing with rapid (16.6 s) and standard (30.8 s) thorax 3DCBCT acquisition protocols, then reimaged while stationary at inhale and exhale, which were the ground truth reconstructions. MCFDK reconstructions were compared with conventional 3D-FDK and 4D-FDK reconstructions. Image quality was compared between all reconstructions using mean square error (MSE), structural similarity index measurement (SSIM), peak signal-to-noise (PSNR), edge response width (ERW) for the diaphragm-lung border for the right lung, tumour centroid accuracy, tumour dice similarity coefficient and sphericity.Main results. For all metrics the MCFDK-Prior reconstructions performed better than the 3D-FDK reconstructions. Similarly for all tumour-related metrics as well as ERW the MCFDK-DD reconstructions performed better than then 3D-FDK reconstructions, but the overall MSE, SSIM and PSNR were similar for the MCFDK-DD and 3D-FDK reconstructions. For all metrics except for tumour centroid error the MCFDK-Prior method produced better quality reconstructions than the MCFDK-DD method. 4D-FDK reconstructions produced poor quality volumes.Significance. We demonstrated that 4DCBCT reconstruction for rapid CBCT acquisition protocols is possible and leads to reduced motion artefacts and more accurate reconstructions when compared to 3DCBCT reconstructions. The 4DCBCT methods demonstrated in this paper will allow for fast, accurate 4DCBCT acquisition for new linacs.

Background and Purpose:Surface Guided Radiotherapy (SGRT) for head and neck radiotherapy is challenging as obstructions are common and non-rigid facial motion can compromise surface accuracy. The purpose of this work was to develop and benchmark the Remove the Mask (RtM) SGRT system, an open-source system especially designed to address the challenges faced in radiotherapy of head and neck cancer. Materials and Methods:The accuracy of the RtM SGRT system was benchmarked using a head phantom positioned on a robotic motion platform capable of sub-millimetre accuracy which was used to induce unidirectional shifts and to reproduce three real head motion traces. We also assessed the accuracy of the system in ten humans volunteers. The ground truth motion of the volunteers was obtained using a commercial motion capture system with an accuracy < 0.3 mm. Results:The mean tracking error of the RtM SGRT system for the ten volunteers was of -0.1 ± 0.4 mm -0.6 ± 0.6 mm and 0.3 ± 0.2 mm, and 0.0 ± 0.2° 0.0 ± 0.1° and 0.0 ± 0.2° for translations and rotations along the left-right, superior-inferior and anterior-posterior axes respectively and we also found similar results in measurements with the head phantom. Forced facial motion was associated with lower tracking accuracy. The RtM SGRT system achieved submillimetre accuracy. Conclusion:The RtM SGRT system is a low-cost, easy to build and open-source SGRT system that can achieve an accuracy that meets international commissioning guidelines. Its open-source and modular design allows for the development and easy translation of novel surface tracking techniques.

BACKGROUND:STereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone-beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration-related patient motion. 4D-CBCT imaging could similarly be used to account for respiration-related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D-CBCT images challenging. If cardiac structures can be identified in pre-treatment 4D-CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities. PURPOSE:The aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images. METHODS:Repeat 4D-CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D-CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen "testing" 4D-CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D-CT using the previously validated platipy toolkit. The platipy segmentations from the testing 4D-CT were defined as the ground truth segmentations for the synthetic 4D-CBCT images. Five different 4D-CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D-CT (Planning CT) was used to assist in generating 4D-CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics. RESULTS:The mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ± 2.8 , 9.3 ± 3.0 mm, the MSD values were 2.9 ± 0.6 , 2.9 ± 0.6 , 6.3 ± 2.5 , 2.5 ± 0.6 , 2.4 ± 0.8 mm, and the VR Values were 0.81 ± 0.12, 0.78 ± 0.14, 1.10 ± 0.47, 0.72 ± 0.15, 0.98 ± 0.44, respectively. Of the five methods investigated the Synthetic CT segmentation method generated the most accurate segmentations for all calculated segmentation metrics. CONCLUSION:This simulation study investigates the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images. Accurate 4D-CBCT cardiac segmentation will provide more accurate information on the location of cardiac anatomy during STAR treatments which can lead to safer and more effective STAR. As the data and segmentation methods used in this study are all open source, this study provides a useful benchmarking tool to evaluate other CBCT cardiac segmentation methods.

Objective.Newer generation linear accelerators (Linacs) allow 20 s cone-beam CT (CBCT) acquisition which reduces radiation therapy treatment time. However, the current clinical application of these rapid scans is only 3DCBCT. In this paper we propose a novel data-driven rapid 4DCBCT reconstruction method for new generation linacs.Approach.This method relies on estimating the magnitude of the diaphragm motion from an initial 3D reconstruction. This estimated motion is used to linearly approximate a deformation vector field (DVF) for each respiration phase. These DVFs are then used for motion compensated Feldkamp-Davis-Kress (MCFDK) reconstructions. This method, named MCFDK Data Driven (MCFDK-DD), was compared to a MCFDK reconstruction using a prior motion model (MCFDK-Prior), a 3D-FDK reconstruction, and a conventional acquisition (4 mins) conventional reconstruction 4DCBCT (4D-FDK). The data used in this paper were derived from 4DCT volumes from 12 patients from The Cancer Imaging Archives. Image quality was quantified using RMSE of line plots centred on the tumour, tissue interface width (TIW), the mean square error (MSE) and structural similarity index measurement (SSIM).Main Results.The tumour line plots in the Superior-Inferior direction showed reduced RMSE for the MCFDK-DD compared to the 3D-FDK method, indicating the MCFDK-DD method provided a more accurate tumour location. Similarly, the TIW values from the MCFDK-DD reconstructions (median 8.6 mm) were significantly reduced for the MCFDK-DD method compared to the 3D-FDK reconstructions (median 14.8 mm, (p< 0.001). The MCFDK-DD, MCFDK-Prior and 3D-FDK had median MSE values of1.08×10-6mm-1,1.11×10-6mm-1and1.17×10-6mm-1respectively. The corresponding median SSIM values were 0.93, 0.92 and 0.92 respectively indicating the MCFDK-DD had good agreement with the conventional 4D-FDK reconstructions.Significance.These results demonstrate the feasibility of creating accurate data-driven 4DCBCT images for rapid scans on new generation linacs. These findings could lead to increased clinical usage of 4D information on newer generation linacs.

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.

BACKGROUND:This paper describes the protocol for the Nano X Image Guidance (Nano X IG) trial, a single-institution, clinical imaging study. The Nano X is a prototype fixed-beam radiotherapy system developed to investigate the feasibility of a low-cost, compact radiotherapy system to increase global access to radiation therapy. This study aims to assess the feasibility of volumetric image guidance with cone beam computed tomography (CBCT) acquired during horizontal patient rotation on the Nano X radiotherapy system.METHODS:In the Nano X IG study, we will determine whether radiotherapy image guidance can be performed with the Nano X radiotherapy system where the patient is horizontally rotated while scan projections are acquired. We will acquire both conventional CBCT scans and Nano X CBCT scans for 30 patients aged 18 and above and receiving radiotherapy for head/neck or upper abdomen cancers. For each patient, a panel of experts will assess the image quality of Nano X CBCT scans against conventional CBCT scans. Each patient will receive two Nano X CBCT scans to determine the image quality reproducibility, the extent and reproducibility of patient motion and assess patient tolerance.DISCUSSION:Fixed-beam radiotherapy systems have the potential to help ease the current shortfall and increase global access to radiotherapy treatment. Advances in image guidance could facilitate fixed-beam radiotherapy using horizontal patient rotation. The efficacy of this radiotherapy approach is dependent on our ability to image and adapt to motion due to rotation and for patients to tolerate rotation during treatment.TRIAL REGISTRATION:ClinicalTrials.gov, NCT04488224. Registered on 27 July 2020.

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.

Abstract BackgroundThis paper describes the protocol for the Nano X Image Guidance (Nano X IG), a single-institution, clinical imaging study. The Nano X is a prototype fixed-beam radiotherapy system developed to investigate the feasibility of a low cost, compact radiotherapy system to increase global access to radiation therapy. This study aims to assess the feasibility of volumetric image guidance with Cone Beam Computed Tomography (CBCT) acquired during horizontal patient rotation on the Nano X radiotherapy system.MethodsIn the Nano X IG study we will determine whether image guidance can be performed with the Nano X radiotherapy system, to a clinically acceptable and usable standard for radiotherapy. We will acquire both conventional CBCT scans and Nano X CBCT scans, where the patient is horizontally rotated while scan projections are acquired, for 30 patients. For each patient a panel of experts will assess the image quality of Nano X CBCT scans against conventional CBCT scans. Each patient will receive two Nano X CBCT scans to determine the image quality reproducibility, the extent and reproducibility of patient motion and assess patient tolerance.DiscussionFixed-beam radiotherapy systems have the potential to help ease the current shortfall and increase global access to radiotherapy treatment. Advances in image guidance could facilitate fixed-beam radiotherapy using horizontal patient rotation. The efficacy of this radiotherapy approach is dependent on our ability to image and adapt to motion due to rotation and for patients to tolerate rotation during treatment.Trial registrationThis study was registered on ClinicalTrials.gov on 27 July 2020. The identifier is: NCT04488224.

Background Obstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR). Objective This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device. Methods A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation. Results Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error. Conclusions These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible.

Fixed-gantry radiation therapy has been proposed as a low-cost alternative to the conventional rotating-gantry radiation therapy, that may help meet the rising global treatment demand. Fixed-gantry systems require gravitational motion compensated reconstruction algorithms to produce cone-beam CT (CBCT) images of sufficient quality for image guidance. The aim of this work was to adapt and investigate five CBCT reconstruction algorithms for fixed-gantry CBCT images. The five algorithms investigated were Feldkamp-Davis-Kress (FDK), prior image constrained compressed sensing (PICCS), gravitational motion compensated FDK (GMCFDK), motion compensated PICCS (MCPICCS) (a novel CBCT reconstruction algorithm) and simultaneous motion estimation and iterative reconstruction (SMEIR). Fixed-gantry and rotating-gantry CBCT scans were acquired of 3 rabbits, with the rotating-gantry scans used as a reference. Projections were sorted into rotation bins, based on the angle of rotation of the rabbit during image acquisition. The algorithms were compared using the structural similarity index measure root mean square error, and reconstruction time. Evaluation of the reconstructed volumes showed that, when compared with the reference rotating-gantry volume, the conventional FDK algorithm did not accurately reconstruct fixed-gantry CBCT scans. Whilst the PICCS reconstruction algorithm reduced some motion artefacts, the motion estimation reconstruction methods (GMCFDK, MCPICCS and SMEIR) were able to greatly reduce the effect of motion artefacts on the reconstructed volumes. This finding was verified quantitatively, with GMCFDK, MCPICCS and SMEIR reconstructions having RMSE 17%-19% lower and SSIM 1% higher than a conventional FDK. However, all motion compensated fixed-gantry CBCT reconstructions had a 56%-61% higher RMSE and 1.5% lower SSIM than FDK reconstructions of conventional rotating-gantry CBCT scans. The results show that motion compensation is required to reduce motion artefacts for fixed-gantry CBCT reconstructions. This paper further demonstrates the feasibility of fixed-gantry CBCT scans, and the ability of CBCT reconstruction algorithms to compensate for motion due to horizontal rotation.