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In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)-a complex mechatronic device comprising over one hundred individually powered tungsten 'leaves' that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed "SPHINX": Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray "beamlets."A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mmThe optimized 5-, 7-, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mmBayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi-beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.

Radiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation-induced lung injury. The gold-standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost.An alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath-hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR-based methods when producing CTVIs.A patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U-Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross-validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]).The mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes.A convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR-based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.

Context Cancer prevalence is increasing, with many patients requiring opioid analgesia. Clinicians need to ensure patients receive adequate pain relief. However, opioid misuse is widespread, and cancer patients are at risk. Objectives This study aims (1) to identify screening approaches that have been used to assess and monitor risk of opioid misuse in patients with cancer; (2) to compare the prevalence of risk estimated by each of these screening approaches; and (3) to compare risk factors among demographic and clinical variables associated with a positive screen on each of the approaches. Methods Medline, Cochrane Controlled Trial Register, PubMed, PsycINFO, and Embase databases were searched for articles reporting opioid misuse screening in cancer patients, along with handsearching the reference list of included articles. Bias was assessed using tools from the Joanna Briggs Suite. Results Eighteen studies met the eligibility criteria, evaluating seven approaches: Urine Drug Test (UDT) (n = 8); the Screener and Opioid Assessment for Patients with Pain (SOAPP) and two variants, Revised and Short Form (n = 6); the Cut-down, Annoyed, Guilty, Eye-opener (CAGE) tool and one variant, Adapted to Include Drugs (n = 6); the Opioid Risk Tool (ORT) (n = 4); Prescription Monitoring Program (PMP) (n = 3); the Screen for Opioid-Associated Aberrant Behavior Risk (SOABR) (n = 1); and structured/specialist interviews (n = 1). Eight studies compared two or more approaches. The rates of risk of opioid misuse in the studied populations ranged from 6 to 65%, acknowledging that estimates are likely to have varied partly because of how specific to opioids the screening approaches were and whether a single or multi-step approach was used. UDT prompted by an intervention or observation of aberrant opioid behaviors (AOB) were conclusive of actual opioid misuse found to be 6.5–24%. Younger age, found in 8/10 studies; personal or family history of anxiety or other mental ill health, found in 6/8 studies; and history of illicit drug use, found in 4/6 studies, showed an increased risk of misuse. Conclusions Younger age, personal or familial mental health history, and history of illicit drug use consistently showed an increased risk of opioid misuse. Clinical suspicion of opioid misuse may be raised by data from PMP or any of the standardized list of AOBs. Clinicians may use SOAPP-R, CAGE-AID, or ORT to screen for increased risk and may use UDT to confirm suspicion of opioid misuse or monitor adherence. More research into this important area is required. Significance of results This systematic review summarized the literature on the use of opioid misuse risk approaches in people with cancer. The rates of reported risk range from 6 to 65%; however, true rate may be closer to 6.5–24%. Younger age, personal or familial mental health history, and history of illicit drug use consistently showed an increased risk of opioid misuse. Clinicians may choose from several approaches. Limited data are available on feasibility and patient experience. PROSPERO registration number. CRD42020163385.

Current respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.

Objective. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional imaging dose and provides motion data in the treatment beam frame of reference. However, accurate tracking using MV images is challenging due to low contrast and modulated fields. Here, a novel real-time marker tracking system based on a convolutional neural network (CNN) classifier was developed and evaluated on retrospectively acquired patient data for MV-based IGRT for prostate cancer patients. Approach. MV images, acquired from 29 volumetric modulated arc therapy (VMAT) prostate cancer patients treated in a multi-institutional clinical trial, were used to train and evaluate a CNN-based marker tracking system. The CNN was trained using labelled MV images from 9 prostate cancer patients (35 fractions) with implanted markers. CNN performance was evaluated on an independent cohort of unseen MV images from 20 patients (78 fractions), using a Precision–Recall curve (PRC), area under the PRC plot (AUC) and sensitivity and specificity. The accuracy of the tracking system was evaluated on the same unseen dataset and quantified by calculating mean absolute (±1 SD) and [1st, 99th] percentiles of the geometric tracking error in treatment beam co-ordinates using manual identification as the ground truth. Main results. The CNN had an AUC of 0.99, sensitivity of 98.31% and specificity of 99.87%. The mean absolute geometric tracking error was 0.30 ± 0.27 and 0.35 ± 0.31 mm in the lateral and superior–inferior directions of the MV images, respectively. The [1st, 99th] percentiles of the error were [−1.03, 0.90] and [−1.12, 1.12] mm in the lateral and SI directions, respectively. Significance. The high classification performance on unseen MV images demonstrates the CNN can successfully identify implanted prostate markers. Furthermore, the sub-millimetre accuracy and precision of the marker tracking system demonstrates potential for adaptation to real-time applications.

Local recurrence in glioblastoma (GBM) patients is caused by the ability of tumour cells to develop treatment resistance mechanisms. Hypoxia, which is present in subregions of GBM tumours, is the leading cause of resistance to radiotherapy and is associated with worse clinical outcomes in GBM. Current standard of care treatment does not account for tumour hypoxia. The ability to image tumour hypoxia at various stages of treatment offers opportunities to personalise and improve treatment for GBM patients. Preoperative imaging of tumour hypoxia offers the opportunity for supramarginal resections in surgical planning beyond current neurosurgical standard of care guidelines. Imaging hypoxia prior to radiotherapy enables selective dose escalation strategies to radioresistant tumour regions, increasing the likelihood of local control. Identifying hypoxic tumour regions harbouring progression at follow up is key to intervening at an early stage of tumour recurrence and personalising therapy tailored to the tumour response to treatment. Imaging of tumour hypoxia in routine clinical practice is challenging, as it requires 18FMISO PET, which is not available in most clinical centres. In this clinical imaging study, we aim to investigate the role of oxygen enhanced (OE) and blood-oxygen level dependent (BOLD) MRI as imaging biomarkers of hypoxia in GBM. Aiming to recruit 20 GBM patients, this study involves the addition of OE/BOLD MRI and 18FMISO PET imaging at several timepoints during the course of treatment. The primary objective is to investigate the ability of OE/BOLD MRI to map tumour hypoxia on preoperative scans using 18FMISO PET as the ground truth. Secondary objectives include evaluating the role of hypoxia imaging biomarkers for dose-painting radiotherapy planning and treatment response assessment in GBM. Overall, by validating the role of MRI biomarkers of hypoxia we ought to provide a non-invasive and more accessible means to more effective, personalised treatment to GBM patients.

Biomedical imaging often relies on 2D images to capture information about moving 3D objects. However, without sufficient prior knowledge, the problem of mapping from 2D images to 3D motion is computationally intractable. In this paper we introduce Voxelmap, a deep learning framework that achieves 2D-3D image registration and volumetric imaging in real-time by imposing implicit constraints on biologically-driven motion. Here we demonstrate the use of this framework in image-guided radiotherapy with data from two lung cancer patients. By efficiently estimating biologically-driven 3D motion from 2D images, this framework could also find application in contexts such as fetal imaging and functional neuroimaging.

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.

Real-time target position verification during pancreas stereotactic body radiation therapy (SBRT) is important for the detection of unplanned tumour motions. Fast and accurate fiducial marker segmentation is a Requirement of real-time marker-based verification. Deep learning (DL) segmentation techniques are ideal because they don’t require additional learning imaging or prior marker information (e.g., shape, orientation). In this study, we evaluated three DL frameworks for marker tracking applied to pancreatic cancer patient data. The DL frameworks evaluated were (1) a convolutional neural network (CNN) classifier with sliding window, (2) a pretrained you-only-look-once (YOLO) version-4 architecture, and (3) a hybrid CNN-YOLO. Intrafraction kV images collected during pancreas SBRT treatments were used as training data (44 fractions, 2017 frames). All patients had 1-4 implanted fiducial markers. Each model was evaluated on unseen kV images (42 fractions, 2517 frames). The ground truth was calculated from manual segmentation and triangulation of markers in orthogonal paired kV/MV images. The sensitivity, specificity, and area under the precision-recall curve (AUC) were calculated. In addition, the mean-absolute-error (MAE), root-mean-square-error (RMSE) and standard-error-of-mean (SEM) were calculated for the centroid of the markers predicted by the models, relative to the ground truth. The sensitivity and specificity of the CNN model were 99.41% and 99.69%, respectively. The AUC was 0.9998. The average precision of the YOLO model for different values of recall was 96.49%. The MAE of the three models in the left-right, superior-inferior, and anterior-posterior directions were under 0.88 ± 0.11 mm, and the RMSE were under 1.09 ± 0.12 mm. The detection times per frame on a GPU were 48.3, 22.9, and 17.1 milliseconds for the CNN, YOLO, and CNN-YOLO, respectively. The results demonstrate submillimeter accuracy of marker position predicted by DL models compared to the ground truth. The marker detection time was fast enough to meet the requirements for real-time application.

Background: Deep inspiration breath hold (DIBH) reduces radiotherapy cardiac dose for left-sided breast cancer patients. The BRAVEHeart (Breast Radiotherapy Audio Visual Enhancement for sparing the Heart) trial will assess the accuracy and usability of a novel device, Breathe Well, for DIBH guidance for left-sided breast cancer patients. Breathe Well will be compared to an adapted widely available monitoring system, the Real-time Position Monitoring system (RPM).Methods: BRAVEHeart is a single institution prospective randomised trial of two DIBH devices. BRAVEHeart will assess the DIBH accuracy for Breathe Well and RPM during left-sided breast cancer radiotherapy. After informed consent has been obtained, 40 patients will be randomised into two equal groups, the experimental arm (Breathe Well) and the control arm (RPM with in-house modification of an added patient screen). The primary hypothesis of BRAVEHeart is that the accuracy of Breathe Well in maintaining the position of the chest during DIBH is superior to the RPM system. Accuracy will be measured by comparing chest wall motion extracted from images acquired of the treatment field during breast radiotherapy for patients treated using the Breathe Well system and those using the RPM system.Discussion: The Breathe Well device uses a depth camera to monitor the chest surface while the RPM system monitors a block on the patient abdomen. The hypothesis of this trial is that the chest surface is a better surrogate for the internal chest wall motion used as a measure of treatment accuracy. The Breathe Well device aims to deliver an easy to use implementation of surface monitoring. The findings from the study will help inform the technology choice for other centres performing DIBH.Trial registration: This trial was registered on ClinicalTrials.gov on 26 August 2016. The identifier is: NCT02881203. (https://clinicaltrials.gov/ct2/show/NCT02881203).

Objective. Reference dosimetry on an MRI-linac requires a chamber specific magnetic field correction factor, k B ⃗ . This work aims to measure the correction factor for a parallel plate chamber on a parallel MRI-linac. Approach. k B ⃗ is defined as the ratio of the absorbed dose to water calibration coefficient in the presence of the magnetic field, N D , w B ⃗ relative to that under 0 T conditions, N D , w 0 T . k B ⃗ was measured via a N D , w transfer to a field chamber at each magnetic field strength from a chamber with known N D , w and k B ⃗ . This was achieved on the parallel MRI-linac by moving the measurement set-up between a high magnetic field strength region at the MRI-isocentre and a low magnetic field strength region at the end of the bore whilst maintaining consistent set-up and scatter conditions. Three PTW 34001 Roos chambers were investigated as well as a PTW 30013 Farmer used to validate methodology. Main Results. The beam quality used for the measurements of k B ⃗ was TPR 20/10 = 0.632. The k B ⃗ for the PTW Farmer chamber at 1 T on a parallel MRI-linac was 0.993 ± 0.013 (k = 1). The average k B ⃗ factor measured for the three Roos chambers on a 1 T parallel MRI-linac was 0.999 ± 0.014 (k = 1). Significance. The results presented are the first measurements of k B ⃗ for a Roos chamber on a parallel MRI-linac. The Roos chamber results demonstrate the potential for the chamber as a reference dosimeter in parallel MRI-linacs.

IntroductionIn radiotherapy, tumour tracking leads the radiation beam to accurately target the tumour while it moves in a complex and unpredictable way due to respiration. Several tumour tracking techniques require the implantation of fiducial markers around the tumour, a procedure that involves unnecessary risks and costs. Markerless tumour tracking (MTT) negates the need for implanted markers, potentially enabling accurate and optimal radiotherapy in a non-invasive way.Methods and analysisWe will perform a phase I interventional trial called MArkerless image Guidance using Intrafraction Kilovoltage x-ray imaging (MAGIK) to investigate the technical feasibility of the MTT technology developed at the University of Sydney (sponsor). 30 participants will undergo the current standard of care lung stereotactic ablative radiation therapy, with the exception that kilovoltage X-ray images will be acquired continuously during treatment delivery to enable MTT. If MTT indicates that the mean lung tumour position has shifted >3 mm, a warning message will be displayed to indicate the need for a treatment intervention. The radiation therapist will then pause the treatment, shift the treatment couch to account for the shift in tumour position and resume the treatment. Participants will be implanted with fiducial markers, which act as the ground truth for evaluating the accuracy of MTT. MTT is considered feasible if the tracking accuracy is 80% of the treatment time.Ethics and disseminationThe MAGIK trial has received ethical approval from The Alfred Human Research Ethics Committee and has been registered with ClinicalTrials.gov with the Identifier: NCT04086082. Estimated time of first recruitment is early 2022. The study recruitment and data analysis phases will be performed concurrently. Treatment for all 30 participants is expected to be completed within 2 years and participant follow-up within a total duration of 7 years. Findings will be disseminated through peer-reviewed publications and conference presentations.Trial registration numberNCT04086082; Pre-result.

Magnetic resonance imaging (MRI) guided cardiac radioablation (CR) for atrial fibrillation (AF) is a promising treatment concept. However, the visibility of AF CR targets on MRI acquisitions requires further exploration and MRI sequence and parameter optimization has not yet been performed for this application. This pilot study explores the feasibility of MRI-guided tracking of AF CR targets by evaluating AF CR target visualization on human participants using a selection of 3D and 2D MRI sequences.MRI datasets were acquired in healthy and AF participants using a range of MRI sequences and parameters. MRI acquisition categories included 3D free-breathing acquisitions (3Dacq), 2D breath-hold ECG-gated acquisitions (2DECG-gated), stacks of 2D breath-hold ECG-gated acquisitions which were retrospectively interpolated to 3D datasets (3Dinterp), and 2D breath-hold ungated acquisitions (2Dreal-time). The ease of target delineation and the presence of artifacts were qualitatively analyzed. Image quality was quantitatively analyzed using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and non-uniformity. Confident 3D target delineation was achievable on all 3Dinterp datasets but was not possible on any of the 3Dacq datasets. Fewer artifacts and significantly better SNR, CNR and non-uniformity metrics were observed with 3Dinterp compared to 3Dacq. 2Dreal-time datasets had slightly lower SNR and CNR than 2DECG-gated and 3Dinterp n datasets. AF CR target visualization on MRI was qualitatively and quantitatively evaluated. The study findings indicate that AF CR target visualization is achievable despite the imaging challenges associated with these targets, warranting further investigation into MRI-guided AF CR treatments.

Four-Dimensional Computed Tomography (4D CT) is of increasing importance in stereotactic body radiotherapy (SBRT) treatments affected by respiratory motion. However, 4D CT images are commonly impacted by irregular breathing, causing image artifacts that can propagate through to treatment, negatively effecting local control. REspiratory Adaptive CT (REACT) is a real-time gating method demonstrated to reduce motion artifacts by avoiding imaging during irregular respiration. Despite artifact reduction seen through in silico and clinical phantom-based studies, REACT has not been able to remove all artifacts. Here, we explore several hardware and software latencies (gantry rotation time, couch shifts, acquisition delays and phase calculation method) inherently linked to REACT and 4D CT in general and investigate their contribution to artifacts beyond those caused by irregular breathing. Imaging was simulated using the digital extended cardiac-torso (XCAT) phantom for fifty patient-measured respiratory traces. Imaging protocols included conventional cine 4D CT and five REACT scans with systematically varied parameters to test the effect of different latencies on artifacts. Artifacts were quantified by comparing the image normalized cross correlation across couch transition points and determining the volume error compared to a static phantom ground truth both as a total error and individually across pixel rows in the main plane of motion. Artifacts were determined for each lung, the whole heart and lung tumour and were compared back to conventional 4D CT and REACT with standard clinical scanning parameters. The gantry rotation time and acquisition delay were found to have the largest impact on reducing image artifacts and should be the focus of future development. The phase calculation method was also found to influence motion artifacts and should potentially be assessed on a patient-to-patient basis. Finally, the correlation between an increase in artifacts and baseline drift suggests that longer scan times allowing drift to occur may impact image quality.

Multicentre clinical trials evaluating the role ofCompared to the gCS method, the MI method showed significantly lower intra- and inter-reader variability both in background activity and in BTV definition.The proposed semiautomated MI method minimises intra- and inter-reader variability, providing a valuable approach for standardisation of

Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to ‘see what we treat, as we treat’ and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

The dynamic keyhole is a new MR image reconstruction method for thoracic and abdominal MR imaging. To date, this method has not been investigated with cancer patient magnetic resonance imaging (MRI) data. The goal of this study was to assess the dynamic keyhole method for the task of lung tumor localization using cine-MR images reconstructed in the presence of respiratory motion.The dynamic keyhole method utilizes a previously acquired a library of peripheral k-space datasets at similar displacement and phase (where phase is simply used to determine whether the breathing is inhale to exhale or exhale to inhale) respiratory bins in conjunction with central k-space datasets (keyhole) acquired. External respiratory signals drive the process of sorting, matching, and combining the two k-space streams for each respiratory bin, thereby achieving faster image acquisition without substantial motion artifacts. This study was the first that investigates the impact of k-space undersampling on lung tumor motion and area assessment across clinically available techniques (zero-filling and conventional keyhole). In this study, the dynamic keyhole, conventional keyhole and zero-filling methods were compared to full k-space dataset acquisition by quantifying (1) the keyhole size required for central k-space datasets for constant image quality across sixty four cine-MRI datasets from nine lung cancer patients, (2) the intensity difference between the original and reconstructed images in a constant keyhole size, and (3) the accuracy of tumor motion and area directly measured by tumor autocontouring.For constant image quality, the dynamic keyhole method, conventional keyhole, and zero-filling methods required 22%, 34%, and 49% of the keyhole size (P0.0001), respectively, compared to the full k-space image acquisition method. Compared to the conventional keyhole and zero-filling reconstructed images with the keyhole size utilized in the dynamic keyhole method, an average intensity difference of the dynamic keyhole reconstructed images (P0.0001) was minimal, and resulted in the accuracy of tumor motion within 99.6% (P0.0001) and the accuracy of tumor area within 98.0% (P0.0001) for lung tumor monitoring applications.This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiation therapy requiring the MR monitoring of thoracic tumors. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by up to a factor of five compared with full k-space methods for real-time lung tumor MRI.

The advent of image-guided radiation therapy has led to dramatic improvements in the accuracy of treatment delivery in radiotherapy. Such advancements have highlighted the deleterious impact tumor motion can have on both image quality and radiation treatment delivery. One approach to reducing tumor motion irregularities is the use of breathing guidance systems during imaging and treatment. These systems aim to facilitate regular respiratory motion which in turn improves image quality and radiation treatment accuracy. A review of such research has yet to be performed; it was therefore their aim to perform a systematic review of breathing guidance interventions within the fields of radiation oncology and radiology.From August 1-14, 2014, the following online databases were searched: Medline, Embase, PubMed, and Web of Science. Results of these searches were filtered in accordance to a set of eligibility criteria. The search, filtration, and analysis of articles were conducted in accordance with preferred reporting items for systematic reviews and meta-analyses. Reference lists of included articles, and repeat authors of included articles, were hand-searched.The systematic search yielded a total of 480 articles, which were filtered down to 27 relevant articles in accordance to the eligibility criteria. These 27 articles detailed the intervention of breathing guidance strategies in controlled studies assessing its impact on such outcomes as breathing regularity, image quality, target coverage, and treatment margins, recruiting either healthy adult volunteers or patients with thoracic or abdominal lesions. In 21/27 studies, significant (p0.05) improvements from the use of breathing guidance were observed.There is a trend toward the number of breathing guidance studies increasing with time, indicating a growing clinical interest. The results found here indicate that further clinical studies are warranted that quantify the clinical impact of breathing guidance, along with the health technology assessment to determine the advantages and disadvantages of breathing guidance.

In this work, the authors present a novel magnetic resonance imaging reconstruction method to improve the quality of MR images in the presence of respiratory motion for real-time thoracic image-guided radiotherapy.This new reconstruction method is called dynamic keyhole and utilizes a library of previously acquired, peripheral k-space datasets from the same (or similar) respiratory state in conjunction with central k-space datasets acquired in real-time. Internal or external respiratory signals are utilized to sort, match, and combine the two separate peripheral and central k-space datasets with respect to respiratory displacement, thereby reducing acquisition time and improving image quality without respiratory-related artifacts. In this study, the dynamic keyhole, conventional keyhole, and zero-filling methods were compared to full k-space acquisition (ground truth) for 60 coronal datasets acquired from 15 healthy human subjects.For the same image-quality difference from the ground-truth image, the dynamic keyhole method reused 79% of the prior peripheral phase-encoding lines, while the conventional keyhole reused 73% and zero-filling 63% (p-value0.0001), corresponding to faster acquisition speed of dynamic keyhole for real-time imaging applications.This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiotherapy requiring real-time MR monitoring of the thoracic region. Based on the results from this study, the dynamic keyhole method could increase the temporal resolution by a factor of five compared with full k-space methods.

To cope with intrafraction tumor motion, integrated MRI-linac systems for real-time image guidance are currently under development. The multileaf collimator (MLC) is a key component in every state-of-the-art radiotherapy treatment system, allowing for accurate field shaping and tumor tracking. This work quantifies the magnetic impact of a widely used MLC on the MRI field homogeneity for such a modality.The finite element method was employed to model a MRI-linac assembly comprised of a 1.0 T split-bore MRI magnet and the key ferromagnetic components of a Varian Millennium 120 MLC, namely, the leaves and motors. Full 3D magnetic field maps of the system were generated. From these field maps, the peak-to-peak distortion within the MRI imaging volume was evaluated over a 30 cm diameter sphere volume (DSV) around the isocenter and compared to a maximum preshim inhomogeneity of 300 μT. Five parametric studies were performed: (1) The source-to-isocenter distance (SID) was varied from 100 to 200 cm, to span the range of a compact system to that with lower magnetic coupling. (2) The MLC model was changed from leaves only to leaves with motors, to determine the contribution to the total distortion caused by MLC leaves and motors separately. (3) The system was configured in the inline or perpendicular orientation, i.e., the linac treatment beam was oriented parallel or perpendicular to the magnetic field direction. (4) The treatment field size was varied from 0 × 0 to 20×20 cm(2), to span the range of clinical treatment fields. (5) The coil currents were scaled linearly to produce magnetic field strengths B0 of 0.5, 1.0, and 1.5 T, to estimate how the MLC impact changes with B0.(1) The MLC-induced MRI field distortion fell continuously with increasing SID. (2) MLC leaves and motors were found to contribute to the distortion in approximately equal measure. (3) Due to faster falloff of the fringe field, the field distortion was generally smaller in the perpendicular beam orientation. The peak-to-peak DSV distortion was below 300 μT at SID≥130 cm (perpendicular) and SID≥140 cm (inline) for the 1.0 T design. (4) The simulation of different treatment fields was identified to cause dynamic changes in the field distribution. However, the estimated residual distortion was below 1.2 mm geometric distortion at SID≥120 cm (perpendicular) and SID≥130 cm (inline) for a 10 mT/m frequency-encoding gradient. (5) Due to magnetic saturation of the MLC materials, the field distortion remained constant at B01.0 T.This work shows that the MRI field distortions caused by the MLC cannot be ignored and must be thoroughly investigated for any MRI-linac system. The numeric distortion values obtained for our 1.0 T magnet may vary for other magnet designs with substantially different fringe fields, however the concept of modest increases in the SID to reduce the distortion to a shimmable level is generally applicable.

To provide real-time lung tumor motion estimation during radiotherapy treatment delivery without the need for implanted fiducial markers or additional imaging dose to the patient.2D radiographs from the therapy beam's-eye-view (BEV) perspective are captured at a frame rate of 12.8 Hz with a frame grabber allowing direct RAM access to the image buffer. An in-house developed real-time soft tissue localization algorithm is utilized to calculate soft tissue displacement from these images in real-time. The system is tested with a Varian TX linear accelerator and an AS-1000 amorphous silicon electronic portal imaging device operating at a resolution of 512 × 384 pixels. The accuracy of the motion estimation is verified with a dynamic motion phantom. Clinical accuracy was tested on lung SBRT images acquired at 2 fps.Real-time lung tumor motion estimation from BEV images without fiducial markers is successfully demonstrated. For the phantom study, a mean tracking error1.0 mm [root mean square (rms) error of 0.3 mm] was observed. The tracking rms accuracy on BEV images from a lung SBRT patient (≈20 mm tumor motion range) is 1.0 mm.The authors demonstrate for the first time real-time markerless lung tumor motion estimation from BEV images alone. The described system can operate at a frame rate of 12.8 Hz and does not require prior knowledge to establish traceable landmarks for tracking on the fly. The authors show that the geometric accuracy is similar to (or better than) previously published markerless algorithms not operating in real-time.

The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction.An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test.Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p0.001) and 29% (p0.001) for abdominal wall and diaphragm respiratory motion, respectively.This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

In lung radiotherapy, variations in cycle-to-cycle breathing results in four-dimensional computed tomography imaging artifacts, leading to inaccurate beam coverage and tumor targeting. In previous studies, the effect of audiovisual (AV) biofeedback on the external respiratory signal reproducibility has been investigated but the internal anatomy motion has not been fully studied. The aim of this study is to test the hypothesis that AV biofeedback improves diaphragm motion reproducibility of internal anatomy using magnetic resonance imaging (MRI).To test the hypothesis 15 healthy human subjects were enrolled in an ethics-approved AV biofeedback study consisting of two imaging sessions spaced ∼1 week apart. Within each session MR images were acquired under free breathing and AV biofeedback conditions. The respiratory signal to the AV biofeedback system utilized optical monitoring of an external marker placed on the abdomen. Synchronously, serial thoracic 2D MR images were obtained to measure the diaphragm motion using a fast gradient-recalled-echo MR pulse sequence in both coronal and sagittal planes. The improvement in the diaphragm motion reproducibility using the AV biofeedback system was quantified by comparing cycle-to-cycle variability in displacement, respiratory period, and baseline drift. Additionally, the variation in improvement between the two sessions was also quantified.The average root mean square error (RMSE) of diaphragm cycle-to-cycle displacement was reduced from 2.6 mm with free breathing to 1.6 mm (38% reduction) with the implementation of AV biofeedback (p-value0.0001). The average RMSE of the respiratory period was reduced from 1.7 s with free breathing to 0.3 s (82% reduction) with AV biofeedback (p-value0.0001). Additionally, the average baseline drift obtained using a linear fit was reduced from 1.6 mm∕min with free breathing to 0.9 mm∕min (44% reduction) with AV biofeedback (p-value = 0.012). The diaphragm motion reproducibility improvements with AV biofeedback were consistent with the abdominal motion reproducibility that was observed from the external marker motion variation.This study was the first to investigate the potential of AV biofeedback to improve the motion reproducibility of internal anatomy using MRI. The study demonstrated the significant improvement in diaphragm motion reproducibility using AV biofeedback combined with MRI. This system can potentially provide clinically beneficial motion management of internal anatomy in MRI and radiotherapy.

3D knowledge of the tumor position during abdominal and thoracic radiotherapy is an important component of motion management in radiation therapy. A wide variety of real-time position monitoring systems are available or under development. These are based on a diversity of modalities including radiofrequency, radioisotopes, ultrasound and MRI in addition to the optical, kilovoltage and megavoltage imaging systems available on conventional accelerators. These systems are also providing new insights into the magnitude and complexity of target and normal tissue motion during a course of therapy, and are driving the development of real-time targeting systems. Real-time targeting devices to align the tumor and the radiation beam have built upon technologies of robots, multileaf collimators, and couch-based and gimbaled positioning systems. The integration and widespread dissemination of systems that locate and target moving tumors are ongoing developments in the early 21st century, and future systems are likely to include the functionality of targeting temporally changing tumors and normal tissue physiology as well as anatomy.

Task Group 101 of the AAPM has prepared this report for medical physicists, clinicians, and therapists in order to outline the best practice guidelines for the external-beam radiation therapy technique referred to as stereotactic body radiation therapy (SBRT). The task group report includes a review of the literature to identify reported clinical findings and expected outcomes for this treatment modality. Information is provided for establishing a SBRT program, including protocols, equipment, resources, and QA procedures. Additionally, suggestions for developing consistent documentation for prescribing, reporting, and recording SBRT treatment delivery is provided.

The purpose of this work is to revisit the impediments and characteristics of fast Monte Carlo techniques for applications in radiation therapy treatment planning using new methods of utilizing pregenerated electron tracks. The limitations of various techniques for the improvement of speed and accuracy of electron transport have been evaluated. A method is proposed that takes advantage of large available memory in current computer hardware for extensive generation of precalculated data. Primary tracks of electrons are generated in the middle of homogeneous materials (water, air, bone, lung) and with energies between 0.2 and 18 MeV using the EGSnrc code. Secondary electrons are not transported, but their position, energy, charge, and direction are saved and used as a primary particle. Based on medium type and incident electron energy, a track is selected from the precalculated set. The performance of the method is tested in various homogeneous and heterogeneous configurations and the results were generally within 2% compared to EGSnrc but with a 40-60 times speed improvement. In a second stage the authors studied the obstacles for further increased speed-ups in voxel geometries by including ray-tracing and particle fluence information in the pregenerated track information. The latter method leads to speed increases of about a factor of 500 over EGSnrc for voxel-based geometries. In both approaches, no physical calculation is carried out during the runtime phase after the pregenerated data has been stored even in the presence of heterogeneities. The precalculated data are generated for each particular material and this improves the performance of the precalculated Monte Carlo code both in terms of accuracy and speed. Precalculated Monte Carlo codes are accurate, fast, and physics independent and therefore applicable to different radiation types including heavy-charged particles.

The current climate of rapid technological evolution is reflected in newer and better methods to modulate and direct radiation beams for cancer therapy. This Vision 20/20 paper focuses on part of this evolution, locating and targeting moving tumors. The two processes are somewhat independent and in principle different implementations of the locating and targeting processes can be interchanged. Advanced localization and targeting methods have an impact on treatment planning and also present new challenges for quality assurance (QA), that of verifying real-time delivery. Some methods to locate and target moving tumors with radiation beams are currently FDA approved for clinical use-and this availability and implementation will increase with time. Extensions of current capabilities will be the integration of higher order dimensionality, such as rotation and deformation in addition to translation, into the estimate of the patient pose and real-time reoptimization and adaption of delivery to the dynamically changing anatomy of cancer patients.

The last 50 years have seen great advances in the accuracy of external beam radiation therapy. Geometrical uncertainties have been reduced from a centimeter or more in presimulation, skin-mark guided days to 1-2 mm in today's image-guided radiation therapy treatments. Medical physicists, with the support and guidance of the American Association of Physicists in Medicine (AAPM), have been, and continue to be, at the forefront of research, development and clinical implementation in this area. This article reviews some of the major contributions of physicists to the improvement of treatment accuracy and precision, and speculates as to what the future may bring.

In numerous cases of radiotherapy delivery to moving targets, simplifying assumptions of identical pattern of motions of tissue for each fraction are not satisfied. Therefore, algorithms capable to respond in real time to motions of target registered at treatment should be developed to improve the precision of radiation intensity delivery. The DMLC delivery of predetermined intensity maps to moving and deforming targets in real time is developed in this paper. Algorithms are constructed so that constraints on maximum admissible speed of leaves are preserved during delivery. A sequence of examples is presented to illustrate behavior of leaf trajectories for representative cases of [dynamic multileaf collimator] (DMLC) [intensity modulated radiation therapy] (IMRT) real-time delivery. The examples presented show real-time deliveries to targets moving as rigid bodies and targets deforming uniformly over their volumes. Examples are admitting random perturbations of predefined target motions that are time dependent only, i.e., target motion perturbations are identical for all target points.