Orthogonal X-ray Images Research Articles

Novel utilization and quantification of Xsight diaphragm tracking for respiratory motion compensation in Cyberknife Synchrony treatment of liver tumors.

The Xsight lung tracking system (XLTS) utilizes an advanced image processing algorithm to precisely identify the position of a tumor and determine its location in orthogonal x-ray images, instead of finding fiducials, thereby minimizing the risk of fiducial insertion-related side effects. To assess and gauge the effectiveness of CyberKnife Synchrony in treating liver tumors located in close proximity to or within the diaphragm, we employed the Xsight diaphragm tracking system (XDTS), which was based on the XLTS. We looked back at the treatment logs of 11 patients (8/11 [XDTS], 3/11 [Fiducial-based Target Tracking System-FTTS]) who had liver tumors in close proximity to or within the diaphragm. And the results are compared with the patients who undergo the treatment of FTTS. The breathing data information was calculated as a rolling average to reduce the effect of irregular breathing. We tested the tracking accuracy with a dynamic phantom (18023-A) on the basis of patient-specific respiratory curve. The average values for the XDTS and FTTS correlation errors were 1.38±0.65 versus 1.50±0.26mm (superior-inferior), 1.28±0.48 versus 0.40±0.09mm (left-right), and 0.96±0.32 versus 0.47±0.10mm(anterior-posterior), respectively. The prediction errors for two methods of 0.65±0.16 versus 5.48±3.33mm in the S-I direction, 0.34±0.10 versus 1.41±0.76mm in the A-P direction, and 0.22±0.072 versus 1.22±0.48mm in the L-R direction. The coverage rate of FTTS slightly less than that of XDTS, such as 96.53±8.19% (FTTS) versus 98.03±1.54 (XDTS). The prediction error, the motion amplitude, and the variation of the respiratory center phase were strongly related to each other. Especially, the higher the amplitude and the variation, the higher the prediction error. The diaphragm has the potential to serve as an alternative to gold fiducial markers for detecting liver tumors in close proximity or within it. We also found that we needed to reduce the motion amplitude and train the respiration of the patients during liver radiotherapy, as well as control and evaluate their breathing.

Evaluation of Diaphragm Position Variations During Proton Therapy for Pediatric Patients With Neuroblastoma.

Background To evaluate the respiratory-induced intrafractional diaphragm motion and interfractional diaphragm displacement in pediatric patients with neuroblastoma (NBL). Materials and methods Ten pediatric patients with a mean age of 4.5 years (range: 1.8-8.7 years) with abdominal NBL treated with proton therapy (PT) have been evaluated. Intrafractional motion and interfractional displacement have been analyzed by using cine radiography and orthogonal X-ray images, respectively. In each case, the cranio-caudal positions of the diaphragm have been measured as an index. This study has investigated the possible correlations between intrafractional diaphragm motion and height. Additionally, interfractional displacementand its time trend during the treatment course have been analyzed. Results The average right and left diaphragm intrafractional motions of 8.3 mm (range: 4.4-11.5 mm) and 6.4 mm (range: 2.2-11.8 mm) were observed, respectively; however, no significant correlation has been observed with height. An interfractional displacement of 5 mm or more has been observed in 20 out of 152 fractions (13%). The average absolute value of the interfractional displacement was 2.5 mm (range: 0-8.6 mm). Interfractional displacement did not show a peculiar tendency throughout the treatment period. Conclusions It was suggested that respiratory-induced diaphragm position variation in children varies greatly among individuals, and accurately estimating it based on height is difficult. Thus, these individual evaluations are considered indispensable.

Intrafractional motion detection for spine SBRT via X-ray imaging using ExacTrac Dynamic

PurposeDue to its close vicinity to critical structures, especially the spinal cord, standards for safety for spine stereotactic body radiotherapy (SBRT) should be high. This study was conducted, to evaluate intrafractional motion during spine SBRT for patients without individualized immobilization (e.g., vacuum cushions) using high accuracy patient monitoring via orthogonal X-ray imaging. MethodsIntrafractional X-ray data were collected from 29 patients receiving 79 fractions of spine SBRT. No individualized immobilization devices were used during the treatment. Intrafractional motion was monitored using the ExacTrac Dynamic (ETD) System (Brainlab AG, Munich, Germany). Deviations were detected in six degrees of freedom (6 DOF). Tolerances for repositioning were 0.7 mm for translational and 0.5° for rotational deviations. Patients were repositioned when the tolerance levels were exceeded. ResultsOut of the 925 pairs of stereoscopic X-ray images examined, 138 (15 %) showed at least one deviation exceeding the predefined tolerance values. In all 6 DOF together, a total of 191 deviations out of tolerance were recorded. The frequency of deviations exceeding the tolerance levels varied among patients but occurred in all but one patient. Deviations out of tolerance could be seen in all 6 DOF. Maximum translational deviations were 2.6 mm, 2.3 mm and 2.8 mm in the lateral, longitudinal and vertical direction. Maximum rotational deviations were 1.8°, 2.6° and 1.6° for pitch, roll and yaw, respectively. Translational deviations were more frequent than rotational ones, and frequency and magnitude of deviations showed an inverse correlation. ConclusionIntrafractional motion detection and patient repositioning during spine SBRT using X-ray imaging via the ETD System can lead to improved safety during the application of high BED in critical locations. When using intrafractional imaging with low thresholds for re-positioning individualized immobilization devices (e.g. vacuum cushions) may be omitted.

The response of dry and wet silica sand to high velocity impact

Understanding the mechanisms governing the interaction of high-velocity projectiles and sand targets is important for numerous applications and industries. The high-velocity penetration of sand targets is known to be controlled by micromechanical phenomena; however, our quantification of these phenomena and our understanding of water saturation effects on target response is presently limited. To help improve our understanding of these phenomena, we performed impact experiments on wet and dry sand with in-situ and post-mortem analysis in a two-stage gas gun with varying impact chamber pressures. For in-situ analysis, we developed and used a novel experimental method for measuring projectile depth and three-dimensional displacement fields from orthogonal flash X-ray images acquired during the penetration process. We also employed in-situ high-speed imaging of ejecta. For post-mortem analysis, we examined final depths of penetration, particle size distributions, and permanent penetrator deformation. We found that impacts into wet sand targets featured enhanced sand displacements around projectiles, enhanced ejecta, and enhanced intermediate and final depths of penetration as compared to impacts into dry targets. We also found that impacts into wet targets led to less particle breakage than impacts into dry targets. Target chamber pressure was found to significantly influence final depth of penetration, likely through a fluidization process as air and fluid escaped the sample’s pores to equilibrate with the surrounding chamber pressure after initial impact. We discuss our findings in the context of existing literature and discuss the hypothesized origin of several of our observations with support from modeling efforts described elsewhere.

Commissioning of a novel gantry-less proton therapy system.

The focus of this article is to describe the configuration, testing, and commissioning of a novel gantry-less synchrotron-based proton therapy (PT) facility. The described PT system delivers protons with a water equivalent range between 4 and 38 cm in 1800 energy layers. The fixed beam delivery permits a maximum field size of 28 × 30 cm2. The patient positioning and imaging system includes a six-degree-of-freedom robotic arm, a convertible patient chair, a vertical 4DCT, and an orthogonal 2D X-ray imaging system. The spot positioning reproducibility was consistent within ±1 mm. The width (σ) of the beam profile at the isocenter was energy dependent and ranged from 2.8 mm to 7.7 mm. Absolute dose reproducibility was measured and deviations were found to be <0.62% for all possible beam scenarios. The built-in dose monitoring system was successfully tested for its ability to generate interlocks under specific conditions (beam spot deviation ≥2 mm, individual spot dose ≥10% or ≥0.25 Gy, spot energy deviation ≥0.5 MeV). The robot positioning exhibited a consistent reproducibility within ±1 mm. All tested scenarios achieved laser-free initial 3D/3D image-guided positioning within ±5 mm. Subsequent 2D/3D positioning showed an accuracy of ±1 mm. A single 2D/3D image registration event corrected positions in all cases. Results of gamma analysis (3%, 3 mm) demonstrated pass rates greater than 95% for head and neck, thorax, abdomen treatment plans. We report on the performance of a novel single-room gantry-less PT system comprised of a compact synchrotron and an adjustable (from nearly horizontal to almost vertical) patient positioning system. The commissioning results show high accuracy and reproducibility of the main proton beam parameters and the patient positioning system. The new PT facility started patient treatments in March 2023, which were the first in Israel and the Middle Eastern region.

A 3D/2D registration method based on reconstruction of orthogonal-view Xray images

To establish a 3D/2D registration method for preoperative CT and intra-operative X-ray images in imageguided spine surgery. We propose a 3D/2D registration algorithm based on 3D image reconstruction. The algorithm performs 3D image reconstruction of 2D orthogonal view X-ray images, thus converting the problem into 3D/3D registration. By constructing an end-to-end framework that combines the two tasks of reconstruction and registration, the geodesic distance is measured in the 3D manifold space to complete the registration. We conducted experiments on the public dataset CTSpine1k. The tests on two test sets with different initial registration errors showed that for data with small initial errors, the proposed algorithm achieved a rotation estimation error of 0.115±0.095° and a translation estimation error of 0.144±0.124 mm; for data with larger initial errors, a rotation estimation error of 0.792±0.659° and a translation estimation error of 0.867±0.701 mm were achieved. The proposed method can achieve robust and accurate 3D/2D registration at a speed that meets real-time requirements to improve the performance of spine surgery navigation.

Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model.

The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, density, and location impact the ability to successfully detect and track a lung lesion in 2D orthogonal X-ray images. The standard workflow to identify successful candidates for lung tumor tracking is called Lung Optimized Treatment (LOT) simulation, and involves multiple steps from CT acquisition to the execution of the simulation plan on CyberKnife. The aim of the study is to develop a deep learning classification model to predict which patients can be successfully treated with lung tumor tracking, thus circumventing the LOT simulationprocess. Target tracking is achieved by matching orthogonal X-ray images with a library of digital radiographs reconstructed from the simulation CT scan (DRRs). We developed a deep learning model to create a binary classification of lung lesions as being trackable or untrackable based on tumor template DRR extracted from the CyberKnife system, and tested five different network architectures. The study included a total of 271 images (230 trackable, 41 untrackable) from 129 patients with one or multiple lung lesions. Eighty percent of the images were used for training, 10% for validation, and the remaining 10% for testing. For all five convolutional neural networks, the binary classification accuracy reached 100% after training, both in the validation and the test set, without any falseclassifications. A deep learning model can distinguish features of trackable and untrackable lesions in DRR images, and can predict successful candidates for fiducial-free lung tumortracking.

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images.

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images. Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s. The 3D prediction error within 3mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT . PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Comparison of setup accuracy of optical surface image versus orthogonal x-ray images for VMAT of the left breast using deep-inspiration breath-hold.

To compare the setup accuracy of optical surface image (OSI) versus orthogonal x-ray images (2DkV) using cone beam computed tomography (CBCT) as ground truth for radiotherapy of left breast cancer in deep-inspiration breath-hold (DIBH). Ten left breast DIBH patients treated with volumetric modulated arc therapy (VMAT) were studied retrospectively. OSI, 2DkV, and CBCT were acquired weekly at treatment setup. OSI, 2DkV, and CBCT were registered to planning CT or planning DRR based on a breast surface region of interest (ROI), bony anatomy (chestwall and sternum), and both bony anatomy and breast surface, respectively. These registrations provided couch shifts for each imaging system. The setup errors, or the difference in couch shifts between OSI and CBCT were compared to those between 2DkV and CBCT. A second OSI was acquired during last beam delivery to evaluate intrafraction motion. The median absolute setup errors were (0.21, 0.27, 0.23cm, 0.6°, 1.3°, 1.0°) for OSI, and (0.26, 0.24, 0.18cm, 0.9°, 1.0°, 0.6°) for 2DkV in vertical, longitudinal and lateral translations, and in rotation, roll and pitch, respectively. None of the setup errors was significantly different between OSI and 2DkV. For both systems, the systematic and random setup errors were ≤0.6cm and ≤1.5° in all directions. Nevertheless, larger setup errors were observed in some sessions in both systems. There was no correlation between OSI and CBCT whereas there was modest correlation between 2DkV and CBCT. The intrafraction motion in DIBH detected by OSI was small with median absolute translations <0.2cm, and rotations ≤0.4°. Though OSI showed comparable and small setup errors as 2DkV, it showed no correlation with CBCT. We concluded that to achieve accurate setup for both bony anatomy and breast surface, daily 2DkV can't be omitted following OSI for left breast patients treated with DIBH VMAT.

SGReg: segmentation guided 3D/2D rigid registration for orthogonal X-ray and CT images in spine surgery navigation

Objective. One of the essential technologies in various image-guided spine surgeries is the rigid registration of 3D pre-operative CT and 2D intra-operative X-ray images. The 3D/2D registration is patterned as two essential tasks, that is, dimensional correspondence establishment and estimation of the 3D pose. 3D data is projected to 2D for dimensional correspondence by most of the existing methods, which makes pose parameters difficult to estimate caused by the loss of spatial information. This work aims to develop a reconstruction based 3D/2D registration method for spine surgery navigation. Approach. A novel segmentation-guided 3D/2D registration (SGReg) method for orthogonal X-ray and CT images was proposed based on reconstruction. SGReg consists of a bi-path segmentation network and an inter-path multi-scale pose estimation module. The X-ray segmentation path in the bi-path segmentation network reconstructs 3D spatial information from 2D orthogonal X-ray images to segmentation masks; meanwhile, the CT segmentation path predicts segmentation masks from 3D CT images, thereby bringing the 3D/2D data into dimensional correspondence. In the inter-path multi-scale pose estimation module, the features from the two segmentation paths are integrated, and the pose parameters are directly regressed under the guidance of the coordinate information. Main result. We evaluated SGReg using a public dataset CTSpine1k and compared the registration performance with other methods. SGReg achieved considerable improvement over other methods with great robustness. Significance We have proposed an end-to-end 3D/2D registration framework named SGReg. Based on the idea of reconstruction, SGReg performs a unified framework between dimensional correspondence establishment and direct pose estimation in 3D space, showing significant potential in spine surgery navigation.

Validation of a Novel Method of Measuring Cup Orientation Using BiPlanar Simultaneous Radiographic Images

BackgroundAccurate acetabular component positioning is paramount to the success of total hip arthroplasty (THA). Two-dimensional imaging alone remains a popular tool for implant position assessment despite known limitations. We investigated the accuracy of a novel method for assessing acetabular component position based upon orthogonal simultaneous biplanar X-ray images. MethodsThere were forty consecutive patients who had a preexisting THA on the contralateral side who underwent both computed tomography (CT) and simultaneous orthogonal biplanar radiographic scans for preoperative planning of THA. The operative inclination (OI) and operative anteversion (OA) of the acetabular cup were calculated by a new measurement method using the biplanar simultaneous scans. Those measurements were compared to measurement of the cup orientation on CT. The measurements were made by 2 independent observers. Interobserver correlation coefficients were calculated between the 2 observers to measure reliability. ResultsThe mean error in OA measurement of the acetabular cup between simultaneous orthogonal biplanar radiographic and CT imaging was 0.5° (SD: 1.9°, minimum −4.0°, maximum 5.0°), the mean error in OI was 0.0° (SD: 1.7°, minimum −5.0°, maximum 4.0°). The average absolute error was 1.5° for OA and 1.2° for OI. Interobserver correlation coefficient was 0.83 for OA and 0.93 for OI. ConclusionThe novel method of measuring cup orientation using simultaneous biplanar radiographic scans utilized in this study was accurate and reproducible between observers compared to CT measurements.

3DSRNet: 3-D Spine Reconstruction Network Using 2-D Orthogonal X-Ray Images Based on Deep Learning

3DSRNet: 3-D Spine Reconstruction Network Using 2-D Orthogonal X-Ray Images Based on Deep Learning

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment. Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm3 and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5mm was 93.6%. The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

Commissioning and validation of a novel commercial TPS for ocular proton therapy.

Until today, the majority of ocular proton treatments worldwide were planned with the EYEPLAN treatment planning system (TPS). Recently, the commercial, computed tomography (CT)-based TPS for ocular proton therapy RayOcular was released, which follows the general concepts of model-based treatment planning approach in conjunction with a pencil-beam-type dose algorithm (PBA). To validate RayOcular with respect to two main features: accurate geometrical representation of the eye model and accuracy of its dose calculation algorithm in combination with an Ion Beam Applications (IBA) eye treatment delivery system. Different 3D-printed eye-ball-phantoms were fabricated to test the geometrical representation of the corresponding CT-based model, both in orthogonal 2D images for X-ray image overlay and in fundus view overlaid with a funduscopy. For the latter, the phantom was equipped with a lens matching refraction of the human eye. Funduscopy was acquired in a Zeiss Claus 500 camera. Tantalum clips and fiducials attached to the phantoms were localized in the TPS model, and residual deviations to the actual position in X-ray images for various orientations of the phantom were determined, after the nominal eye orientation was corrected in RayOcular to obtain a best overall fit. In the fundus view, deviations between known and displayed distances were measured. Dose calculation accuracy of the PBA on a 0.2mm grid was investigated by comparing between measured lateral and depth-dose profiles in water for various combinations of range, modulation, and field-size. Ultimately, the modeling of dose distributions behind wedges was tested. A 1D gamma-test was applied, and the lateral and distal penumbra were further compared. Average residuals between model clips and visible clips/fiducials in orthogonal X-ray images were within 0.3mm, including different orientations of the phantom. The differences between measured distances on the registered funduscopy image in the RayOcular fundus view and the known ground-truth were within 1mm up to 10.5mm distance from the posterior pole. No clear benefit projection of either polar mode or camera mode could be identified, the latter mimicking camera properties. Measured dose distributions were reproduced with gamma-test pass-rates of >95% with 2%/0.3mm for depth and lateral profiles in the middle of spread-out Bragg-peaks. Distal falloff and lateral penumbra were within 0.2mm for fields without a wedge. For shallow depths, the agreement was worse, reaching pass-rates down to 80% with 5%/0.3mm when comparing lateral profiles in air. This is caused by low-energy protons from a scatter source in the IBA system not modeled by RayOcular. Dose distributions modified by wedges were reproduced, matching the wedge-induced broadening of the lateral penumbra to within 0.4mm for the investigated cases and showing the excess dose within the field due to wedge scatter. RayOcular was validated for its use with an IBA single scattering delivery nozzle. Geometric modeling of the eye and representation of 2D projections fulfill clinical requirements. The PBA dose calculation reproduces measured distributions and allows explicit handling of wedges, overcoming approximations of simpler dose calculation algorithms used in other systems.

Description of a novel technique for ultrasound-based planning for gynaecological 3D brachytherapy and comparison between plans of this technique and 2D with fluoroscopy.

With the purpose of implementing a way to obtain volumes from ultrasound axial images, a novel method for 3D gynaecologic brachytherapy was assessed, with a 3D-printed attachment for a commercial stepper (for prostate brachytherapy). It allowed the acquisition of a transabdominal axial image series by ultrasound; these images were uploaded to a treatment planning system where high-risk clinical tumour volume (HR-CTV) and risk organs were contoured. A dose administration plan was developed with orthogonal X-ray images (0° and 270° incidences), using International Commission on Radiation Units and Measurements (ICRU) 38 points. The same plan was applied in the ultrasound images’ sequence and their respective volumes; differences were noted. In the 20 cases analysed, with a given point A dose, its corresponding dose delivered to 90% of HR-CTV percentage was highly variable (mean = 104.2, SD = 26.01). There is a significant variation of this percentage when point A falls outside the HR-CTV than when it falls inside (p < 0.00001). There is a significant correlation in terms of the bladder point dose ICRU 38 and the Maximum dose to 2cc of organ or target volume (D2cc) bladder (p = 0.021); however, there is no such correlation when we relate the rectum point dose ICRU 38 to the D2cc rectum (p = 0.327). There was a negative correlation between HR-CTV and D2cc bladder and D2cc rectum; both were statistically significant. There were significant differences comparing ICRU points and dose to prescription and organ at risk volumes, pointing out that there is room for optimisation of plans using the latter technique. So, it is proposed to further test this image modality and compare it to other imaging techniques that allow the creation of volumes, such as computed tomography or magnetic resonance imaging.

End to end comparison of surface-guided imaging versus stereoscopic X-rays for the SRS treatment of multiple metastases with a single isocenter using 3D anthropomorphic gel phantoms.

IntroductionTwo end‐to‐end tests evaluate the accuracy of a surface‐guided radiation therapy (SGRT) system (CRAD Catalyst HD) for position verification in comparison to a stereoscopic x‐ray imaging system (Brainlab Exactrac ) for single‐isocenter, multiple metastases stereotactic radiosurgery (SRS) using 3D polymer gel inserts.Materials and methodsA 3D‐printed phantom (Prime phantom, RTsafe PC, Athens, Greece) with two separate cylindrical polymer gel inserts were immobilized in open‐face masks and treated with a single isocentric, multitarget SRS plan. Planning was done in Brainlab (Elements) to treat five metastatic lesions in one fraction, and initial setup was done using cone beam computed tomography. Positional verification was done using orthogonal X‐ray imaging (Brainlab Exactrac) and/or a surface imaging system (CRAD Catalyst HD, Uppsala, Sweden), and shift discrepancies were recorded for each couch angle. Forty‐two hours after irradiation, the gel phantom was scanned in a 1.5 Tesla MRI, and images were fused with the patient computed tomography data/structure set for further analysis of spatial dose distribution.ResultsDiscrepancies between the CRAD Catalyst HD system and Brainlab Exactrac were <1 mm in the translational direction and <0.5° in the angular direction at noncoplanar couch angles. Dose parameters (D Mean% , D 95%) and 3D gamma index passing rates were evaluated for both setup modalities for each planned target volume (PTV) at a variety of thresholds: 3%/2 mm (Exactrac≥93.1% and CRAD ≥87.2%), 5%/2 mm (Exactrac≥95.6% and CRAD ≥94.6%), and 5%/1 mm (Exactrac≥81.8% and CRAD ≥83.7%).ConclusionDose metrics for a setup with surface imaging was found to be consistent with setup using x‐ray imaging, demonstrating high accuracy and reproducibility for treatment delivery. Results indicate the feasibility of using surface imaging for position verification at noncoplanar couch angles for single‐isocenter, multiple‐target SRS using end‐to‐end quality assurance (QA) testing with 3D polymer gel dosimetry.

Development of a deep learning‐based patient‐specific target contour prediction model for markerless tumor positioning

For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X-ray imaging systems, internal markers must be implemented to facilitate tumor tracking. The purpose of this study was to develop a markerless deep learning-based pancreatic tumor positioning procedure for real-time tumor tracking with a kV X-ray imaging system. Fourteen pancreatic cancer patients treated with intensity-modulated radiation therapy from six fixed gantry angles with a gimbal-head radiotherapy system were included in this study. For a gimbal-head radiotherapy system, the three-dimensional (3D) intrafraction target position can be determined using an orthogonal kV X-ray imaging system. All patients underwent four-dimensional computed tomography (4DCT) simulations for treatment planning, which were divided into 10 respiratory phases. After a patient's 4DCT was acquired, for each X-ray tube angle, 10 digitally reconstructed radiograph (DRR) images were obtained. Then, a data augmentation procedure was conducted. The data augmentation procedure first rotated the CT volume around the superior-inferior and anterior-posterior directions from -3° to 3° in 1.5° intervals. Then, the Super-SloMo model was adapted to interpolate 10 frames between respiratory phases. In total, the data augmentation procedure expanded the data scale 250-fold. In this study, for each patient, 12 datasets containing the DRR images from each specific X-ray tube angle based on the radiation therapy plan were obtained. The augmented dataset was randomly divided into training and testing datasets. The training dataset contained 2000 DRR images with clinical target volume (CTV) contours labeled for fine-tuning the pre-trained target contour prediction model. After the fine-tuning, the patient and X-ray tube angle-specific CTV contour prediction model was acquired. The testing dataset contained the remaining 500 images to evaluate the performance of the CTV contour prediction model. The dice similarity coefficient (DSC) between the area enclosed by the CTV contour and predicted contour was calculated to evaluate the model's contour prediction performance. The 3D position of the CTV was calculated based on the centroid of the contour in the orthogonal DRR images, and the 3D error of the prediction position was calculated to evaluate the CTV positioning performance. For each patient, the DSC results from 12 X-ray tube angles and 3D error from 6 gantry angles were calculated, representing the novelty of this study. The mean and standard deviation (SD) of all patients' DSCs were 0.98 and 0.015, respectively. The mean and SD of the 3D error were 0.29mm and 0.14mm, respectively. The global maximum 3D error was 1.66mm, and the global minimum DSC was 0.81. The mean calculation time for CTV contour prediction was 55ms per image. This fulfills the requirement of RTTT. Regarding the positioning accuracy and calculation efficiency, the presented procedure can provide a solution for markerless real-time tumor tracking for pancreatic cancer patients.

82786 Quantification of the Accuracy of Stereotactic Radiosurgery using Surface Guided Imaging with 3D Printed Head Phantoms

ABSTRACT IMPACT: This work assesses clinical implementation of a surface guided imaging system to improve the accuracy radiation delivery for treatment of brain lesions using a patient CT derived head phantom. OBJECTIVES/GOALS: Advancements in radiotherapy design have made clinical demand for efficient and accurate methods to deliver stereotactic radiosurgery (SRS) for treatment of intracranial lesions. This study assesses the potential of using surface guided imaging for setup using a 3D patient specific head phantom. METHODS/STUDY POPULATION: A single isocenter, multiple metastases SRS plan was generated on a CT derived RTsafe Prime phantom made of tissue equivalent materials and a polymer gel insert. Five targets of varying diameters were treated with 8Gy of radiation using two different positioning techniques. The first gel insert was irradiated within the phantom according to internal alignment with standard orthogonal x-ray imaging while the second setup used surface guided imaging, based on external anatomy. 42 hours after irradiation, the phantom was scanned in a head coil using a 1.5T MRI. MR images were fused with the patient CT data and structure set to further evaluate calculated and measured dose distributions. RESULTS/ANTICIPATED RESULTS: Discrepancies in phantom setup according to standard orthogonal x-ray imaging compared to surface guided imaging demonstrated to be &lt;1mm in each translational (vertical, longitudinal, and lateral) and angular (rot, roll, pitch) directions. The 3D gel inserts permitted spatial analysis to compare dose distributions of measured values to those calculated in a treatment planning system (TPS). 3D GI (Gamma Index) analysis showed good alignment in high dose regions and resulted in passing rates &gt;94% (5%/2mm) and &gt;87% (3%/2mm). Finally, 3 of 5 targets showed better 3D GI passing rates and less geometric offset for positioning with the surface guided imaging. DISCUSSION/SIGNIFICANCE OF FINDINGS: 3D spatial analysis of human like phantoms demonstrated that patient positioning according to external anatomy performed comparable to standard methods aligning to the internal anatomy, for a multiple met SRS treatment.

Evaluation of the inter- and intrafraction displacement for head patients treated at the particle therapy centre MedAustron based on the comparison of different commercial immobilisation devices

In December 2016 the clinical operation has started at the particle therapy centre MedAustron, Wiener Neustadt, Austria. Different commercial immobilisation devices are used for head patients. These immobilisation devices are a combination of table tops (Qfix BoS™ Headframe, Elekta HeadStep™), pillows (BoS™ Standard pillow, Moldcare®, HeadStep™ pillow) and thermoplastic masks (Klarity Green™, Qfix Fibreplast™, HeadStep™ iCAST double). For each patient image-guided radiotherapy (IGRT) is performed by acquiring orthogonal X-ray imaging and 2D3D registration and the application of the resulting 6-degree of freedom (DOF) position correction on the robotic couch.The inter- and intrafraction displacement of 101 adult head patients and 27 paediatric sedated head patients were evaluated and compared among each other regarding reproducibility during the entire treatment and stability during each fraction. For the comparison, statistical methods (Shapiro–Wilk test, Mann–Whitney U-test) were applied on the position corrections as well as on the position verifications. The actual planning target volume margins of 3mm (adults) and 2mm (children) were evaluated by applying the van Herk formula on the intrafraction displacement results and performing treatment plan robustness simulations of twelve different translational offset scenarios including a HU uncertainty of 3.5%.Statistically significant differences between the immobilisation devices were found, but they turned out to be clinically irrelevant. The margin calculation for adult head patients resulted in 0.8mm (lateral), 1.2mm (cranio-caudal) and 0.6mm (anterior–posterior), and for paediatric head patients under anaesthesia in 0.8mm (lateral), 0.5mm (cranio-caudal) and 0.9mm (anterior–posterior). Based on these values, robustness evaluations of selected adult head patients and sedated children showed the validity of the currently used PTV margins.

Artificial intelligence-based framework in evaluating intrafraction motion for liver cancer robotic stereotactic body radiation therapy with fiducial tracking.

This study aimed to design a fully automated framework to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. The proposed framework includes three modules: (a) automated fiducial marker detection, (b) three-dimensional (3D) position reconstruction, and (c) intrafraction motion evaluation. A total of 5927 images from real patients treated with CyberKnife fiducial tracking were collected. The ground truth was established by labeling coarse bounding boxes manually, and binary mask images were then obtained by applying a binary threshold and filter. These images and labels were used to train a detection model using a fully convolutional network (fCN). The output of the detection model can be used to reconstruct the 3D positions of the fiducial markers and then evaluate the intrafraction motion via a rigid transformation. For a patient test, the motion amplitudes, rotations, and fiducial cohort deformations were calculated used the developed framework for 13 patients with a total of 52 fractions. The precision and recall of the fiducial marker detection model were 98.6% and 95.6%, respectively, showing high model performance. The mean (±SD) centroid error between the predicted fiducial markers and the ground truth was 0.25±0.47 pixels on the test data. For intrafraction motion evaluation, the mean (±SD) translations in the superior-posterior (SI), left-right (LR), and anterior-posterior (AP) directions were 13.1±2.2mm, 2.0±0.4mm, and 5.2±1.4mm, respectively, and the mean (±SD) rotations in the roll, pitch and yaw directions were 2.9±1.5°, 2.5±1.5°, and 3.1±2.2°. Seventy-one percent of the fractions had rotations larger than the system limitations. With rotation correction during rigid registration, only 2 of the 52 fractions had residual errors larger than 2mm in any direction, while without rotation correction, the probability of large residual errors increased to 46.2%. We developed a framework with high performance and accuracy for automatic fiducial marker detection, which can be used to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. For liver patients, most fractions have fiducial cohort rotations larger than the system limitations; however, the fiducial cohort deformation is small, especially for the scenario with rotation correction.