Real-time Tumor Motion Research Articles

LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion prediction. We propose a deep learning framework, named as LSTM-Global Temporal Convolution-External Attention Network (LGEANet). In LGEANet, we first feed each of the univariate time series into the Long Short-Term Memory (LSTM) module respectively and utilize the strength of the global temporal convolutional layer to discover the temporal pattern of the univariate signals from hidden states of the LSTM. Then, External attention is adopted to capture the dynamic dependence of the multiple time series. Also, a traditional autoregressive linear model in parallel to the non-linear neural network part was integrated to mitigate the scale insensitivity of the networks. A total of 304 motion traces for 31 patients are acquired from a public dataset in the experiments and four representative cases were selected for model evaluation. The respiratory signals were sampled at intervals of about 37.5ms (26 frames per second) for an average duration of 71min. The proposed LGEANet achieved better performance with higher empirical correlation coefficient value (CORRs) and lower mean absolute error value (MAEs) and relative squared error value (RSEs) than other investigated models. For the four representative datasets, when the response time is less than 231ms, the model can achieve CORRs more than 0.96. And the averaged position error reduction by using the proposed model was about 67% in the superior-inferior (SI) direction, 41% in the anterior-posterior (AP) direction and 38% in the right-left (RL) direction compared to that without prediction. The proposed network achieved the greatest error reduction in the SI direction, which is the main direction of tumor motion. The LGEANet achieves promising performance in minimizing the prediction error due to system latencies during real-time tumor motion tracking.

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

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-timeadaptation. 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. 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 robustreconstruction. 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 trackingaccuracy. 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 guidanceapplications.

Monte Carlo Modeling of Dynamic Tumor Tracking on a Gimbaled Linear Accelerator.

The Vero4DRT (Brainlab AG) linear accelerator is capable of dynamic tumor tracking (DTT) by panning/tilting the radiation beam to follow respiratory-induced tumor motion in real time. In this study, the panning/tilting motion is modeled in Monte Carlo (MC) for quality assurance (QA) of four-dimensional (4D) dose distributions created within the treatment planning system (TPS). Step-and-shoot intensity-modulated radiation therapy plans were optimized for 10 previously treated liver patients. These plans were recalculated on multiple phases of a 4D computed tomography (4DCT) scan using MC while modeling panning/tilting. The dose distributions on each phase were accumulated to create a respiratory-weighted 4D dose distribution. Differences between the TPS and MC modeled doses were examined. On average, 4D dose calculations in MC showed the maximum dose of an organ at risk (OAR) to be 10% greater than the TPS' three-dimensional dose calculation (collapsed cone [CC] convolution algorithm) predicted. MC's 4D dose calculations showed that 6 out of 24 OARs could exceed their specified dose limits, and calculated their maximum dose to be 4% higher on average (up to 13%) than the TPS' 4D dose calculations. Dose differences between MC and the TPS were greatest in the beam penumbra region. Modeling panning/tilting for DTT has been successfully modeled with MC and is a useful tool to QA respiratory-correlated 4D dose distributions. The dose differences between the TPS and MC calculations highlight the importance of using 4D MC to confirm the safety of OAR doses before DTT treatments.

Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.

Magnetic resonance-guided radiotherapy (MRgRT) with real-time intra-fraction tumor motion monitoring allows for high precision Stereotactic Ablative Radiotherapy (SABR). This study aimed to investigate the clinical feasibility, patient satisfaction and delivery accuracy of single-fraction MR-guided SABR in a single day (one-stop-shop, OSS). Ten patients with small lung tumors eligible for single fraction treatments were included. The OSS procedure consisted of consultation, treatment simulation, treatment planning and delivery. Following SABR delivery, patients completed a reported experience measure (PREM) questionnaire. Prescribed doses ranged 28-34Gy. Median GTV was 2.2cm3 (range 1.3-22.9cm3). A gating boundary of 3mm, and PTV margin of 5mm around the GTV, were used with auto-beam delivery control. Accuracy of SABR delivery was studied by analyzing delivered MR-cines reconstructed from machine log files. All 10 patients completed the OSS procedure in a single day, and all reported satisfaction with the process. Median time for the treatment planning step and the whole procedure were 2.8h and 6.6h, respectively. With optimization of the procedure, treatment could be completed in half a day. During beam-on, the 3mm tracking boundary encompassed between 78.0 and 100% of the GTV across all patients, with corresponding PTV values being 94.4-100% (5th-95th percentiles). On average, system-latency for triggering a beam-off event comprised 5.3% of the delivery time. Latency reduced GTV coverage by an average of -0.3 %. Duty-cycles during treatment delivery ranged from 26.1 to 64.7%. An OSS procedure with MR-guided SABR for lung cancer led to good patient satisfaction. Gated treatment delivery was highly accurate with little impact of system-latency.

A novel bone suppression algorithm in intensity-based 2D/3D image registration for real-time tumor motion monitoring: Development and phantom-based validation.

BackgroundReal‐time tumor motion monitoring (TMM) is a crucial process for intra‐fractional respiration management in lung cancer radiotherapy. Since the tumor can be partly or fully located behind the ribs, the TMM is challenging.PurposeThe aim of this work was to develop a bone suppression (BS) algorithm designed for real‐time 2D/3D marker‐less TMM to increase the visibility of the tumor when overlapping with bony structures and consequently to improve the accuracy of TMM.MethodA BS method was implemented in the in‐house developed software for ultrafast intensity‐based 2D/3D tumor registration (Fast Image‐based Registration [FIRE]). The method operates on both, digitally reconstructed radiograph (DRR) and intra‐fractional X‐ray images. The bony structures are derived from computed tomography data by thresholding during ray‐casting, and the resulting bone DRR is subtracted from intra‐fractional X‐ray images to obtain a soft‐tissue‐only image for subsequent tumor registration. The accuracy of TMM utilizing BS was evaluated within a retrospective phantom study with nine different 3D‐printed tumor phantoms placed in the in‐house developed Advanced Radiation DOSimetry (ARDOS) breathing phantom. A 24 mm craniocaudal tumor motion, including rib eclipses, was simulated, and X‐ray images were acquired on the Elekta Versa HD Linac in the lateral and posterior–anterior directions. An error assessment for BS images was evaluated with respect to the ground truth tumor position.ResultsA total error (root mean square error) of 0.87 ± 0.23 mm and 1.03 ± 0.26 mm was found for posterior–anterior and lateral imaging; the mean time for BS was 8.03 ± 1.54 ms. Without utilizing BS, TMM failed in all X‐ray images since the registration algorithm focused on the rib position due to the predominant intensity of this tissue within DRR and X‐ray images.ConclusionThe BS algorithm developed and implemented improved the accuracy, robustness, and stability of real‐time TMM in lung cancer in a phantom study, even in the case of rib interlude where normal tumor registration fails.

Investigation the Efficacy of Fuzzy Logic Implementation at Image-Guided Radiotherapy.

At image-guided radiotherapy, technique, different imaging, and monitoring systems are utilized for (i) organs border detection and tumor delineation during the treatment planning process and (ii) patient setup and tumor localization at pretreatment step and (iii) real-time tumor motion tracking for dynamic thorax tumors during the treatment. In this study, the effect of fuzzy logic is quantitatively investigated at different steps of image-guided radiotherapy. Fuzzy logic-based models and algorithms have been implemented at three steps, and the obtained results are compared with commonly available strategies. Required data are (i) real patients treated with Synchrony Cyberknife system at Georgetown University Hospital for real-time tumor motion prediction, (ii) computed tomography images taken from real patients for geometrical setup, and also (iii) tomography images of an anthropomorphic phantom for tumor delineation process. In real-time tumor tracking, the targeting error averages of the fuzzy correlation model in comparison with the Cyberknife modeler are 4.57 mm and 8.97 mm, respectively, for a given patient that shows remarkable error reduction. In the case of patient geometrical setup, the fuzzy logic-based algorithm has better influence in comparing with the artificial neural network, while the setup error averages is reduced from 1.47 to 0.4432 mm using the fuzzy logic-based method, for a given patient.Finally, the obtained results show that the fuzzy logic based image processing algorithm exhibits much better performance for edge detection compared to four conventional operators. This study is an effort to show that fuzzy logic based algorithms are also highly applicable at image-guided radiotherapy as one of the important treatment modalities for tumor delineation, patient setup error reduction, and intrafractional motion error compensation due to their inherent properties.

Feasibility of MR-guided radiotherapy using beam-eye-view 2D-cine with tumor-volume projection

Purpose. Current magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) applies sagittal/coronal 2D-cine to monitor major tumor motions, however, the beam eye’s view (BEV) with volumetric tumor projection would be the best measure for radiation beam conformality, independent of tumor through-plane motion. The goal is to assess the feasibility, accuracy, and performance of the BEV approach. Methods. Beam-specific BEV 2D-cine with volume-projected tumor contours were simulated to establish a 2D/3D tumor match against a tumor-motion library based on multi-breath time-resolved (TR) 4DMRI images. Two BEV-library-matching methods were developed: (1) fast screening with tumor center-of-mass (∆COM), in-plane area ratio, and DICE similarity, and finalizing with the highest DICE score and (2) DICE screening for top-3 candidates and finalizing with rigid registration. A 4D-XCAT digital phantom and 8 lung-cancer patients were used for assessment. For each patient, 3 sets of 40 s TR-4DMRI were acquired at 2 Hz and 6 representative BEV were created with the isocenter set at tumor COM in mid-respiration. One TR-4DMRI set (40 × 2 = 80-images) was used to simulate BEV 2D-cine and the other two (160-images) were used to create a library. The matching result was validated against the ground truth within the test set. Using a leave-one-out strategy, the success rate, accuracy, and speed of tumor matching were assessed for volume-projected tumors over 11520 time-points (=8patients•3sets•80images•6BEVs). Results. Volume-projected tumor contour area on the 6 BEVs varies by 60% ± 8% and (in-plane/volume-projected) varies by 82% ± 9%. The changes with tumor shape, orientation, and through-plane motion. Method-1 produces 96% matching success (ΔCOM = 0.7 ± 0.2 mm, =1.01 ± 0.02, Dice=0.92 ± 0.02) with the computational time of 15 ± 1 ms/match, while method-2 produces 94% ± 1% success (ΔCOM = 0.2 ± 0.1 mm, =1.00 ± 0.01, Dice = 0.94 ± 0.02) with 223 ± 13 ms/match. Conclusion. This study has demonstrated the feasibility, accuracy, and benefits of BEV 2D-cine imaging with tumor-volume projection, allowing real-time tumor motion monitoring and beam conformality checking. Further clinical evaluation is necessary before MRgRT applications.

Erken Evre Küçük Hücreli Dışı Akciğer Kanserlerinin Tedavisinde Robotik Radyocerrahi

Objective: In this study, we retrospectively reviewed and statistically analyzed the Cyberknife SBRT outcomes in terms of local control and survival times for the patients with primer lung tumors treated at Anadolu Medical Center. Methods: We included 135 patients who were treated between 2005 and 2016 and diagnosed primary lung cancer who were judged medically inoperable. median BED10 was 180 Gy (ranging 45-180). The treatment response was assessed using either a CT or a PET-CT scan or both. There were 108 men and 27 women, with an overall median age of 65 years (range 44-88 years). The median follow-up and overall survival were 19 (3-88) and 34 months, respectively. Results: Overall survival and local control of the patients for 1, 2, 3, 5 years were 88%, 72%, 50%, 39%, and 81%, 54%, 51%, 39%, respectively. 1, 2 and 3 year survival rates for BED10=180Gy group were 89%, 84% and 72% respectively. 1, 2 and 3 year survival rates were found as 88%, 68% and 42% for BED<180Gy, respectively. 1, 2 and 3 year local control rates for BED=180Gy group were found as 92%, 86% and 86% respectively. 1, 2 and 3 year local control rates for BED<180Gy group were found as 78%, 42% and 38%. Local control and overall survival were associated with higher BED10. The difference between survival and local control of BED=180 Gy and BED<180 Gy are significant (p=0.008, p=0.002). Conclusion: The Cyberknife stereotactic radiosurgery treatment with real-time tumor motion tracking is a promising well tolerated treatment option for inoperable early stage lung tumors.

RespiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking

PurposeAn intraoperative real-time respiratory tumor motion prediction system with magnetic tracking technology is presented. Based on respiratory movements in different body regions, it provides patient and single/multiple tumor-specific prediction that facilitates the guiding of treatments.MethodsA custom-built phantom patient model replicates the respiratory cycles similar to a human body, while the custom-built sensor holder concept is applied on the patient’s surface to find optimum sensor number and their best possible placement locations to use in real-time surgical navigation and motion prediction of internal tumors. Automatic marker localization applied to patient’s 4D-CT data, feature selection and Gaussian process regression algorithms enable off-line prediction in the preoperative phase to increase the accuracy of real-time prediction.ResultsTwo evaluation methods with three different registration patterns (at fully/half inhaled and fully exhaled positions) were used quantitatively at all internal target positions in phantom: The statical method evaluates the accuracy by stopping simulated breathing and dynamic with continued breathing patterns. The overall root mean square error (RMS) for both methods was between 0.32pm 0.06~hbox {mm} and 3.71pm 0.79~hbox {mm}. The overall registration RMS error was 0.6pm 0.4~hbox {mm}. The best prediction errors were observed by registrations at half inhaled positions with minimum 0.27pm 0.02~hbox {mm}, maximum 2.90pm 0.72~hbox {mm}. The resulting accuracy satisfies most radiotherapy treatments or surgeries, e.g., for lung, liver, prostate and spine.ConclusionThe built system is proposed to predict respiratory motions of internal structures in the body while the patient is breathing freely during treatment. The custom-built sensor holders are compatible with magnetic tracking. Our presented approach reduces known technological and human limitations of commonly used methods for physicians and patients.

Initial Dosimetric Outcomes and Treatment Related Toxicities Using Stereotactic MRI-Guided Radiation Therapy (SMART) for Unresectable Pancreatic Cancer at a Single Institution

High dose stereotactic body radiotherapy (SBRT) may offer improved disease outcomes for locally advanced pancreatic cancer but treatment delivery remains a challenge due to tumor proximity to organs-at-risk (OAR) and daily, anatomic variability. Stereotactic MRI-guided adaptive radiation therapy (SMART) with real-time tumor tracking allows for improved dose delivery to disease targets and OAR sparing by accounting for anatomic changes with each treatment. This study reports initial dosimetric outcomes and treatment related toxicities in unresectable pancreatic cancer patients treated using SMART. Ten patients with unresectable, locally advanced pancreatic cancer were treated on a MR-guided linear accelerator using real-time tumor tracking and motion management. A plan was generated for each patient after initial CT and MR-simulation. At each treatment, MR imaging was obtained for the daily anatomy and the initial (unadapted) plan was compared to obtain a predicted dose coverage of tumor volumes and OAR constraints using a prescribed dose of 50 Gy in 5 fractions. If OAR constraints were not met or PTV coverage was inadequate, an adaptive plan was generated based on the anatomy imaged on the day of treatment. Goal PTV coverage was 95% of the volume at 100% of prescription dose, however, this was subject to hard OAR constraints and plans were optimized for best PTV coverage within an acceptable OAR dose. For each adaptive plan generated, the delivered dose for that fraction was compared to the predicted dose from the initial plan. Only toxicities grade 3 or higher were considered for inclusion. At time of analysis, from June 2018 to February 2019, a total of 46 fractions were delivered to ten patients. Of the 46 treatments, 5 were with the initial, unadapted plan and 41 were adaptive. Due to intra-fraction adjustments that occurred during 6 treatments, only 35 adaptive plans were included for analysis. When comparing the performance of the initial plan on the daily updated MR anatomy, adaptive planning led to improved coverage of both PTV (86.36% vs. 73.15% at 100% of prescribed dose) and CTV (95.97% vs 89.59% at 95% of prescribed dose). Unadapted plans were much more likely to exceed OAR constraints, with 27 of 35 (77%) unadapted fractions predicted to violate prescribed duodenal, stomach, or small bowel constraints compared to 2 of 35 (6%) of adapted fractions. Moreover, 16/35 (46%) of unadapted fractions violated constraints for 2 of 3 abdominal OARs and 5/35 (14%) violated constraints for all 3 compared to none for adapted fractions. No grade 3 toxicities were reported in any of the treated patients. Stereotactic adaptive MRI-guided radiotherapy led to improved tumor volume coverage and reduced dose to abdominal OARs in patients with locally advanced pancreatic cancer with no grade 3 or higher toxicities. Prospective studies examining disease outcomes and treatment related toxicities are ongoing.

Real-time Tumor Motion Monitoring and PTV Margin Determination in Lung SBRT Treatment

Stereotactic body radiation therapy (SBRT) is an effective treatment procedure allowing the delivery of high radiation doses in a small number of fractions to produce a high biological effective do...

Report of four cases of pulmonary malignant tumors treated with SBRT using BODYFIX plus 4D-CBCT image guidance technique.

e20050 Background: To monitor the range of motion in SBRT treatment of lung tumors by using BODYFIX combined with four-dimensional cone beam CT (4D-CBCT), and provide evidence for delineating the reasonable internal target area (ITV) of lung cancer, and observe the short-term outcom of treatment and treatment of complications. Methods: 4D-CT was used to locate CT scan, delineate the GTV of the tumor, make a simple radiation treatment planning, determine the treatment center point, and then use line accelerator 4D-CBCT to perform pre-SBRT treatment on 4 patients with lung malignant tumor. The system automatically reconstructs the image and matches the CT image of the treatment plan to obtain the placement error of the patient in the head and foot (SI), left and right (LR), and front and rear (AP) directions. The double registration technique is used to correct the correction first. Positioning error, the 4D-CBCT dynamic image is again registered with the target area, the range of motion within the tumor is observed, and the time-weighted 4D-CBCT image is transmitted back to the Monaco planning system to delineate the target area to accurately determine the range of the ITV. According to this target area, a radiation treatment planning was developed, GTV 20Gy/f, 1 to 3 fractions, and then 4D-CBCT scan was performed in each SBRT treatment to observe the range of tumor motion in real time. Results: 3 of the 4 patients the lung tumors were CR at 3 months after treatment, and 1 patient was SD. No serious complications occurred during the treatment or within 3 months after radiotherapy. Conclusions: The BODYFIX plus 4D-CBCT image can more accurately outline the ITV range of lung cancer. The patient is well tolerated and the short-term outcom is good.

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

A novel method was developed to track lung tumor motion in real time during radiation therapy with the purpose to allow target radiation dose escalation while simultaneously reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. This method analyzes beam’s eye view radiation therapy treatment megavoltage (MV) images with simulated digitally reconstructed radiographs (DRRs) as references. Instead of comparing global DRRs with projection images, this method incorporates a technique that divides the global composite DRR and the corresponding MV projection into sub-images called tiles. Registration is performed independently on tile pairs in order to reduce the effects of global discrepancies due to scattering or imaging modality differences. This algorithm was evaluated by phantom studies while simulated tumors were controlled to move with various patterns in a complex humanoid torso. Approximately 15,000 phantom MV images were acquired at nine gantry angles, with different tumors moving within ranges between 10 and 20 mm. Tumors were successfully identified on every projection with a total maximum/average error of 1.84/0.98 mm. This algorithm was also applied to over 5,000 frames of MV projections acquired during radiation therapy of five lung cancer patients. This tumor-tracking methodology is capable of accurately locating lung tumors during treatment without implanting any internal fiducial markers nor delivering extra imaging radiation doses.

A quantitative investigation on lung tumor site on its motion tracking in radiotherapy with external surrogates

Introduction: In external beam radiotherapy each effort is done to deliver 3D dose distribution onto the tumor volume uniformly, while minimizing the dose to healthy organs at the same time. Radiation treatment of tumors located at thorax region such as lung and liver has a challenging issue during target localization since these tumors move mainly due to respiration. There are several methods to compensate the effect or tumor motion error, clinically. Recently, stereotactic body radiation therapy strategy has been proposed clinically that can lead to better local control using external surrogates. In this method, the exact information of tumor position in real-time is obtained using consistent correlation models between tumor motion and external surrogates motion. In this work, the influence of motion variability and the site of lung tumor has been taken into account over total treatment time. Materials and Methods: In this study, motion information of lung tumor of real patients treated with the CyberKnife Synchrony system, have been utilized. Real time tumor motion tracking is performed by developing a fuzzy logic based correlation model to predict tumor position using external surrogate’s motion. In this work the variabilities of lung tumor motion in the Superior-Inferior (SI), Latero-Lateral (LL), and Anterior-Posterior (AP) directions and their geometrical location in lung region have been investigated on the performance accuracy of correlation model, as metric. With divided lung tumors location into four upper, lower, left and right sites. Results: Targeting errors between of 20 patients with lung tumors located at various sites have been recorded using Root Mean Square Error (RMSE). Intra-fraction variation of lung tumors ranges from 4.0 to 70.5 mm at SI, 1.7 to 75.5 mm at LL, 1.8 to 40.7 mm at AP directions. Total treatment time ranges between 27.0 to 118.8 minutes over total patients. Moreover, the average of RMSEs during tumor tracking by means of correlation model are 15.6 mm, 6.7 mm, 7.8 mm and 9.5 mm for upper, lower, left and right tumors, respectively in one division. Conclusion: There are many influencing factors and patterns on lung tumor motion management at external radiotherapy. By understanding these factors and considering to them during real time tumor tracking, the treatment quality may have enhanced significantly at stereotactic body radiation therapy. In this work we assess tumors motion variability and their geometrical sites on the accuracy of our developed fuzzy correlation model, quantitatively.

Real-Time Tumor Motion Tracking in 3D Using Planning 4D CT Images during Image-Guided Radiation Therapy

In this paper we propose a novel method for tracking the respiratory phase and 3D tumor position in real time during treatment. The method uses planning four-dimensional (4D) computed tomography (CT) obtained through the respiratory phase, and a kV projection taken during treatment. First, digitally rendered radiographs (DRRs) are generated from the 4DCT, and the structural similarity (SSIM) between the DRRs and the kV projection is computed to determine the current respiratory phase and magnitude. The 3D position of the tumor corresponding to the phase and magnitude is estimated using non-rigid registration by utilizing the tumor path segmented in the 4DCT. This method is evaluated using data from six patients with lung cancer and dynamic diaphragm phantom data. The method performs well irrespective of the gantry angle used, i.e., a respiration phase tracking accuracy of 97.2 ± 2.5%, and tumor tracking error in 3D of 0.9 ± 0.4 mm. The phantom study reveals that the DRRs match the actual projections well. The time taken to track the tumor is 400 ± 53 ms. This study demonstrated the feasibility of a technique used to track the respiratory phase and 3D tumor position in real time using kV fluoroscopy acquired from arbitrary angles around the freely breathing patient.

An augmented correlation framework for the estimation of tumour translational and rotational motion during external beam radiotherapy treatments using intermittent monoscopic x-ray imaging and an external respiratory signal

Increasing evidence shows that intrafraction tumour motion monitoring must include both six degrees of freedom (6DoF): 3D translations and 3D rotations. Existing real-time algorithms for 6DoF target motion estimation require continuous intrafraction fluoroscopic imaging at high frequency, thereby exposing patients to additional high imaging dose. This paper presents the first method capable of 6DoF motion monitoring using intermittent 2D kV imaging and a continuous external respiratory signal.Our approach is to optimise a state-augmented linear correlation model between an external signal and internal 6DoF motion. In standard treatments, the model can be built using information obtained during pre-treatment cone beam CT (CBCT). Real-time 6DoF tumor motion can then be estimated using just the external signal. Intermittent intrafraction kV images are used to update the model parameters, accounting for changes in correlation and baseline shifts.The method was evaluated in silico using data from 6 lung SABR patients, with the internal tumour motion recorded with electromagnetic beacons and the external signal from a bellows belt. Projection images from CBCT (10 Hz) and intermittent kV images were simulated by projecting the 3D Calypso beacon positions onto an imager. IMRT and VMAT treatments were simulated with increasing imaging update intervals: 0.1 s, 1 s, 3 s, 10 s and 30 s.For all the tested clinical scenarios, translational motion estimates with our method had sub-mm accuracy (mean) and precision (standard deviation) while rotational motion estimates were accurate to < and precise to . Motion estimation errors increased as the imaging update interval increased. With the largest imaging update interval (30 s), the errors were mm, mm and mm for translation in the left–right, superior–inferior and anterior–posterior directions, respectively, and , and for rotation around the aforementioned axes for both VMAT and IMRT treatments.In conclusion, we developed and evaluated a novel method for highly accurate real-time 6DoF motion monitoring on a standard linear accelerator without requiring continuous kV imaging. The proposed method achieved sub-mm and sub-degree accuracy on a lung cancer patient dataset.

A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow

In radiation therapy, for accurate radiation dose delivery to a target tumor and reduction of the extra exposure of normal tissues, real-time tumor tracking is typically an important technique in lung cancer treatment since lung tumors move with patients’ respiration. To observe a tumor motion in real time, x-ray fluoroscopic devices can be employed, and various tracking techniques have been proposed to track tumors. However, development of a fast and accurate tracking method for clinical use is still a challenging task since the obscured image of the tumor can cause decreased tracking accuracy and can result in additional processing time for remedying the accuracy. In this study, a new key-point-based tumor tracking method, which is sufficiently fast and accurate, is presented. Given an x-ray image sequence, the proposed method employs a difference-of-Gaussians filtering technique to detect key points in the tumor region of the first frame which are robust against noise and outliers in the subsequent frames. In the subsequent frames, these key points are tracked using a fast optical flow technique, and tumor motion is estimated via their movement. To evaluate the performance, the proposed method has been tested on several clinical kV and MV x-ray image sequences. The experimental results showed that the average of the root mean square errors of tracking were and for kV and MV x-ray image sequences, respectively. This tracking performance was more accurate than previous tracking methods. In addition, the average processing times for each frame were and for kV and MV image sequences, respectively, and the proposed method was faster than previous methods as well as shorter than frame acquisition interval. Therefore, the proposed method has the potential for both highly accurate and fast tumor tracking in clinical applications.

Feasibility of real‐time lung tumor motion monitoring using intrafractional ultrasound and kV cone beam projection images

The ability to monitor intrafractional tumor motion is essential for radiation therapy of thoracic and abdominal tumors. This study aims to develop a method to track lung tumor motion using intrafractional continuous ultrasound (US) and periodic cone-beam projection images (CBPI). Time-sequenced b-mode US and CBPI data were extracted from the data acquired with the Clarity® and XVI platforms on an Elekta linac, respectively. The data were synchronized through a video capture card (VCE-PRO, IMPERX Inc.) which was triggered by the XVI system. In this way, a system was configured to allow real-time acquisition of the diaphragm position synchronized with periodic acquisition of the lung tumor position. Feasibility of the system was demonstrated by acquiring synchronized data on an in-house motion platform with embedded spheres of different materials and US images of the diaphragm on 5 volunteers of various body sizes. Finally, ultrasound b-mode images and CBPI were also acquired simultaneously from 3 lung cancer patients. Diaphragm motion monitoring under free breathing (FB) was successful with intracostal US imaging. We observed that diaphragm visualization decreased with the increase in the body size of the volunteer. The US system was able to track the motion as small as 2mm in the phantom. The intrafractional CBPI acquired during VMAT delivery was successfully synchronized with US acquisition in a phantom study. Collected patient data showed a significant correlation between diaphragm motion, an internal surrogate monitored by US, and the tumor motion in superior-inferior (SI) direction monitored by XVI (P˂0.0001). The feasibility of real-time lung tumor motion tracking in SI direction with continuous ultrasound and periodic CBPI was demonstrated. The real-time estimation of the target position from the two streams for lung cancer patients would enable respiration gating or tracking during SBRT.

Evaluation of the 4D RADPOS dosimetry system for dose and position quality assurance of CyberKnife.

The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations. Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation. For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5mm for all directions. For the 10mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8mm for sinusoidal motion but increased to ≤2.6mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system. RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

Accuracy assessment of wireless transponder tracking in the operating room environment.

To evaluate the applicability of the Calypso® wireless transponder tracking system (Varian Medical Systems Inc., USA) for real-time tumor motion tracking during surgical procedures on tumors in non-rigid target areas. An accuracy assessment was performed for an extended electromagnetic field of view (FoV) of 27.5 × 27.5 × 22.5cm (which included the standard FoV of 14 × 14 × 19cm) in which 5DOF wireless Beacon® transponders can be tracked. Using a custom-made measurement setup, we assessed single transponder relative accuracy, absolute accuracy and jitter throughout the extended FoV at 1440 locations interspaced with 2.5cm in each orthogonal direction. The NDI Polaris Spectra optical tracking system (OTS) was used as a reference. Measurements were taken in a room without surrounding distorting factors and repeated in an operating room (OR). In the OR, the influence of a carbon fiber and regular stainless steel OR tabletop was investigated. The calibration of the OTS and transponder system resulted in an average root-mean-square error (RMSE) vector of 0.03cm. For both the standard and extended FoV, all accuracy measures were dependent on transponder to tracking array (TA) distances and the absolute accuracy was also dependent on TA to OR tabletop distances. This latter influence was reproducible, and after calibrating this, the residual error was below 0.1cm RMSE within the entire standard FoV. Within the extended FoV, this residual RMSE did not exceed 0.1cm for transponder to TA distances up to 25cm. This study shows that transponder tracking is promising for accurate tumor tracking in the operating room. This applies when using the standard FoV, but also when using the extended FoV up to 25cm above the TA, substantially increasing flexibility.