Optical Surface Imaging Research Articles

Initial Experience of Implementing a Pre-treatment Dry Run for HyperArc Stereotactic Radiosurgery Treatments With Optical Surface Imaging for Intra-fraction Motion Monitoring

Initial Experience of Implementing a Pre-treatment Dry Run for HyperArc Stereotactic Radiosurgery Treatments With Optical Surface Imaging for Intra-fraction Motion Monitoring

432 - A review of accuracy of optical surface imaging systems for setup verification during breast radiotherapy treatment

432 - A review of accuracy of optical surface imaging systems for setup verification during breast radiotherapy treatment

Complexation of correlation-extreme navigation systems

The constantly increasing intensity of air traffic, due to the growth in the number of manned and unmanned aerial vehicles, as well as the increasingly frequent disruptions of radio navigation systems in recent years, require the development of new methods for increasing the accuracy and continuity of navigation support for flights. The use of correlation-extreme navigation systems (CENS), using digital terrain matrices (DEM) of the earth's surface, makes it possible to solve navigation problems when flying over many types of terrain. However, in the case of a multi-extreme correlation dependence, the problem of ambiguity in determining the location of the aircraft arises, which reduces the accuracy and continuity of the data generated by such a CENS. The aim of article to develop a method for integrating of airborne CENSs using DEM and optical images of the earth's surface to improve the accuracy and continuity of navigation determinations. A method has been developed for integrating CENS, built on the use of DEM and digital devices for the formation and processing of optical images, and allowing to increase the accuracy of classical correlation-extremal methods in the conditions of the emergence of multi-extremal correlation dependence. The paper examines the possibility of using digital devices for forming and processing images as part of the on-board equipment of an aircraft as a means of clarifying its current location when solving autonomous navigation problems, in particular, to correct deviations of the flight path of the aircraft from a given course.

Automated Test for Monthly Quality Assurance of Optical Surface Imaging Dynamic Localization Accuracy.

The American Association of Physicists in Medicine (AAPM) recently published the report of Task Group (TG) 302, which provides recommendations on acceptance, commissioning, and ongoing routine quality assurance (QA) for surface-guided radiation therapy (SGRT) systems. One of the recommended monthly QA tests is a dynamic localization accuracy test. This work aimed to develop an automated procedure for monthly SGRT dynamic localization QA. An anthropomorphic head phantom was rigidly attached to the 6-dof couch of a TrueBeam linac. TrueBeam Developer Mode was used to take an MV image of the phantom at the starting position, then automatically drive the couch through a series of translations and rotations, taking an MV image after each translation. The Identify SGRT system monitored the motion of the phantom surface from the starting position. Translations assessed on MV images were compared to translations reported in trajectory log files and Identify log files. Rotations were compared between trajectory log files and Identify log files. Three experiments were conducted. None of the translations or rotations from any experiment exceeded the tolerance values for stereotactic ablative body radiation therapy (SABR) recommended by AAPM TG-142. Maximum deviations from the expected translation values from MV imaging, trajectory log files, and Identify log files were -0.94mm, -0.11mm, and -0.78mm, respectively. Maximum deviations from the expected rotation values from trajectory log files and Identify log files were 0.01 and -0.2 degrees, respectively. The proposed method is a simple automated way to complete monthly dynamic localization QA of SGRT systems.

Feasibility of a Novel Surface-Guided Setup Technique to Reproduce Neck Curvature Using two Regions of Interest.

To improve the setup reproducibility of neck curvature using real-time optical surface imaging (OSI) guidance on 2 regions of interest (ROIs) to infer cervical spine (c-spine) curvature for surface-guided radiotherapy (SGRT) of head-and-neck (HN) and c-spine cancer. A novel SGRT setup approach was designed to reproduce neck curvature with 2 ROIs: upper-chest ROI and open-face ROI. It was hypothesized that the neck curvature could be reproduced if both ROIs were aligned within ±3 mm/2˚ tolerance. This was tested prospectively in 7 volunteers using real-time 3D-OSI guidance and lateral 2D-photography verification after the 3D and 2D references were captured from the initial conventional setup. Real-time SGRT was performed to align chest-ROI and face-ROI, and the longitudinal distance between them was adjustable using a head-support slider. Verification of neck curvature anteriorly and posteriorly was achieved by overlaying edge-extracted lateral pictures. Retrospectively, the relationship between anterior surface and spinal canal alignment was checked in 11 patients using their simulation CT (simCT) and setup cone-beam CT (CBCT). After the anterior surface was rigidly aligned, the spinal canal alignment was checked and quantified using the mean-distance-to-agreement (MDA) and DICE similarity index, and surface-to-spine correlation was calculated. The reproducibility of neck curvatures using the 2xROI SGRT setup is verified and the mean neck-outline-matching difference is within ±2 mm in lateral photographic overlays. The chest-ROI alignment takes 110 ± 58 s and the face-ROI takes 60 ± 35 s. When the anterior body surface is aligned (MDA = 1.1 ± 0.6 mm, DICE = 0.96 ± 0.02,) the internal spinal canal is also aligned (MDA = 1.0 ± 0.3 mm, DICE = 0.84 ± 0.04) in 11 patients. The surface-to-spine correlation is c = 0.90 (MDA) and c = 0.85 (DICE). This study demonstrates the feasibility of the novel 2-ROI SGRT setup technique to achieve reproducible neck and c-spine curvature regardless of neck visibility and availability as ROI. Staff training is needed to adopt this unconventional SGRT technique to improve patient setup.

FLASH radiotherapy and the associated dosimetric challenges

At Lund University and Skåne University Hospital in Lund, Sweden, we have, as the first clinic, modified a clinical Elekta Precise linear accelerator for convertible delivery of ultra-high dose rate (FLASH) irradiation. Whereas recently published reviews highlighted the need for standardised protocols for ultra-high dose rate beam dosimetry to be able to determine the true potential of FLASH irradiation, several dosimetry studies as well as in-vitro and in-vivo experiments have been carried out at our unit. Dosimetric procedures for verification of accurate dose delivery of FLASH irradiation to cell cultures, zebrafish embryos and small animals have been established using radiochromic films and thermo-luminescent dosimeters. Also, recently the first experience of electron FLASH radiotherapy (FLASH-RT) in canine patients in our clinical setting was published. Our research facilities also include a laboratory for 3D polymer gel manufacturing. Recently, we started investigating the feasibility of a NIPAM polymer gel dosimeter for ultra-high dose rate dosimetry. Furthermore, in the bunker of the modified Elekta linear accelerator, a Surface Guided Radiotherapy (SGRT) system is accessible. The Catalyst™ system (C-Rad Positioning, Uppsala, Sweden) provides optical surface imaging for patient setup, real-time motion monitoring and breathing adapted treatment. Aiming at treating patients using ultra-high dose rates, a real-time validation of the alignment between the beam and the target is crucial as the dose is delivered in a fraction of a second. Our research group has during the last decade investigated and developed SGRT workflows which improved patient setup and breathing adapted treatment for several cancer patient groups. Recently, we also started investigating the feasibility of a real-time motion monitoring system for surface guided FLASH-RT. Both FLASH related studies; 3D polymer gel dosimetry and surface guided FLASH-RT are to our knowledge the first of their kind. Following an introduction to the field of FLASH and the associated dosimetric challenges, we here aim to present the two ongoing studies including some preliminary results.

Application of surface-guided radiation therapy in prostate cancer: comparative analysis of differences with skin marking-guided patient setup.

Surface-guided radiation therapy is an image-guided method using optical surface imaging that has recently been adopted for patient setup and motion monitoring during treatment. We aimed to determine whether the surface guide setup is accurate and efficient compared to the skin-marking guide in prostate cancer treatment. The skin-marking setup was performed, and vertical, longitudinal, and lateral couch values (labeled as "M") were recorded. Subsequently, the surface-guided setup was conducted, and couch values (labeled as "S") were recorded. After performing cone-beam computed tomography (CBCT), the final couch values was recorded (labeled as "C"), and the shift value was calculated (labeled as "Gap (M-S)," "Gap (M-C)," "Gap (S-C)") and then compared. Additionally, the setup times for the skin marking and surface guides were also compared. One hundred and twenty-five patients were analyzed, totaling 2,735 treatment fractions. Gap (M-S) showed minimal differences in the vertical, longitudinal, and lateral averages (-0.03 cm, 0.07 cm, and 0.06 cm, respectively). Gap (M-C) and Gap (S-C) exhibited a mean difference of 0.04 cm (p = 0.03) in the vertical direction, a mean difference of 0.35 cm (p = 0.52) in the longitudinal direction, and a mean difference of 0.11 cm (p = 0.91) in the lateral direction. There was no correlation between shift values and patient characteristics. The average setup time of the skin-marking guide was 6.72 minutes, and 7.53 minutes for the surface guide. There was no statistically significant difference between the surface and skin-marking guides regarding final CBCT shift values and no correlation between translational shift values and patient characteristics. We also observed minimal difference in setup time between the two methods. Therefore, the surface guide can be considered an accurate and time-efficient alternative to skin-marking guides.

Real-Time 3D Liver Tumor Localization via Combined Optical Surface Imaging and an X-Ray Projection from Arbitrary Imaging Angles

A deep learning (DL)-based, deformable registration-driven liver tumor localization technique was developed for onboard deformable motion tracking. The technique (Surf-X360-Bio) uses in-room optical surface imaging and an x-ray projection at an arbitrary scan angle to solve volumetric liver and liver tumor motion in real-time. Surf-X360-Bio solves the volumetric motion of the liver and localizes the liver tumor, through deforming liver and liver tumor meshes segmented on prior 4D-CTs/CBCTs. It uses real-time onboard information from an optical surface image and a simultaneously-acquired x-ray projection (from an arbitrary scan angle). Surf-X360-Bio localizes tumors via two steps: liver boundary motion estimation and intra-liver motion derivation. Surf-X360-Bio first estimates liver boundary motion by a patient-specific surface imaging model (Surf), utilizing the correlation between the external body surface motion and the internal liver boundary motion. As the correlation can be imperfect, the residual motion estimation errors were corrected by a patient-specific, angle-agnostic x-ray imaging model (X360). X360 deformed the liver boundary to match motion-related imaging features encoded in an arbitrarily-angled x-ray projection, using the Surf output for initialization. X360 adopted a geometry-aware learning module to extract and adapt to angle-varying features of arbitrarily-angled x-ray images, by calculating the projection system matrix of each x-ray image on-the-fly during model training and inference. After the liver boundary motion estimation by Surf and X360, intra-liver deformation was solved by a biomechanical model (Bio) to propagate the liver boundary motion inside to localize the tumors. The DL-based Bio model used domain knowledge of tissue biomechanics and finite element analysis (FEA) to solve intra-liver motion, with a much faster speed than conventional FEA methods to meet the real-time constraint. Surf-X360-Bio was evaluated using a dataset of 34 liver patients. Liver tumor localization accuracy was evaluated by the Dice similarity coefficient (DSC), the 95-percentile Hausdorff distance (HD95), and the center-of-mass error (COME). Using 3,306 motion scenarios spanning the 360 degree x-ray scan angles for each testing patient, Surf-X360-Bio localized the liver tumors to 0.81 (mean) ± 0.16 (s.d.) in DSC, 2.5 ± 1.7 mm in HD95, and 2.1 ± 1.8 mm in COME. In comparison, the prior reference image without deformable registration-driven localization yielded 0.42 ± 0.29 in DSC, 8.1 ± 5.2 mm in HD95, and 8.5 ± 5.2 mm in COME. Via Surf-X360-Bio, the overall inference time was below 230 ms for each case. Combining optical surface imaging and x-ray imaging, Surf-X360-Bio achieved fast (<250 ms inference time), accurate (mean error < 2.1 mm), and robust liver tumor localizations at arbitrary x-ray scan angles for real-time, marker-less 3D deformable motion tracking.

A Radiotherapy Positioning Method for Both Coarse Guidance and Precise Verification Based on Integration of AR and Optical Surface Imaging

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. We proposed a noninvasive radiotherapy positioning method integrating augmented reality (AR) and optical surface, and evaluated its feasibility in clinical workflow. AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, i) the AR-based coarse guidance. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. ii) optical surface-based precise verification. The point clouds were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in 6 degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, i) quality assurance workflow, ii) errors of both steps and correlation analysis, and iii) receiver operating characteristic (ROC). The maximum errors in phantom evaluation were 3.4±2.5 mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9 mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.3 vs. 1.7±1.1, p<0.01), Lng (2.4±1.3 vs. 1.4±0.1, p<0.01) and Rtn (0.8±0.5 vs. 0.6±0.4, p = 0.03) while relatively similar in Lat (1.7±1.0 vs. 1.9±1.1, p = 0.13). The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Clinical Implementation of Optical Surface Imaging Systems in Vulvar Cancer to Reduce Setup Error

Optical surface monitoring system (OSMS) could achieve continuous monitoring of patient position through a nonradiographic and noninvasive technology, which has been widely applied to many sites of radiation therapy including breast, intracranial, head and neck, abdomen, and the extremities. However, no study has investigated the application of OSMS in radiation therapy of vulvar cancer. In this study, we hypothesized using OSMS would reduce setup error in the vulvar cancer compared to standard laser-based setup (SLS). A single-center prospective trial included 10 patients with vulvar cancer treated with intensity modulated radiation therapy (IMRT) for a total of 250 fractions. Every patient was immobilized with Styrofoam in a frog-leg position and used marks on the patient's skin to align to the room lasers for pre-setup. The treatment couch values of lateral (Lat), longitudinal (Lng), vertical (Vrt) in three translation directions and Rtn (the rotation along the z axis), Pitch (the rotation along the x axis), Roll (the rotation along the y axis) in three rotations directions were recorded. Then completed the setup of patient with information from a surface imaging system and obtained deviation values. Setup is followed by pretreatment cone-beam computed tomography (CBCT) to further verify patient and target position by registering to the planning CT with respect to bony anatomy. With CBCT as a gold standard for patient setup, the absolute values of setup error and error distribution of SLS method and OSMS method from the same patient in above six directions were compared. The mean of absolute values of setup errors in the SLS method were significantly higher than those of OSMS method in Lat (0.34cm vs 0.20cm), Lng (0.42cm vs 0.29cm), Vrt (0.43cm vs 0.28cm) and Rtn (0.90° vs 0.67°) directions (P<0.05). The mean errors in SLS and OSMS methods in Pitch and Roll directions were 0.93° and 1.02° (P = 0.16), 0.42° and 0.45° (P = 0.48), respectively, with no significant difference. In the OSMS method, the distribution rates of the setup errors within 0.5cm in three translation directions and within 1.5° in three rotations directions were significantly higher in Lat (90.8% vs 80.4%), Lng (82.0% vs 68.8%), Vrt (83.2% vs 65.6%), and Rtn (91.2% vs 80.4%) directions than those of SLS method (P<0.05). In the Pitch (79.2% vs 80.4%) and Roll (97.2% vs 98.0%) directions, the distribution rates of the OSMS method were slightly lower than those of the SLS method, with no significant difference (P = 0.738 and P = 0.559). The implementation of OSMS in IMRT for vulvar cancer could reduce setup error and increase precision of setup, especially for the directions of Lat, Lng, Vrt and Rtn.

Can Optical Surface Imaging Replace Non-coplanar Cone-beam Computed Tomography for Non-coplanar Set-up Verification in Single-isocentre Non-coplanar Stereotactic Radiosurgery and Hypofractionated Stereotactic Radiotherapy for Single and Multiple Brain Metastases?

AimsTo conduct a direct comparison regarding the non-coplanar positioning accuracy between the optical surface imaging system Catalyst HDTM and non-coplanar cone-beam computed tomography (NC-CBCT) in intracranial single-isocentre non-coplanar stereotactic radiosurgery (SRS) and hypofractionated stereotactic radiotherapy (HSRT). Materials and methodsTwenty patients with between one and five brain metastases who underwent single-isocentre non-coplanar volumetric modulated arc therapy (NC-VMAT) SRS or HSRT were enrolled in this study. For each non-zero couch angle, both Catalyst HDTM and NC-CBCT were used for set-up verification prior to beam delivery. The set-up error reported by Catalyst HDTM was compared with the set-up error derived from NC-CBCT, which was defined as the gold standard. Additionally, the dose delivery accuracy of each non-coplanar field after using Catalyst HDTM and NC-CBCT for set-up correction was measured with SRS MapCHECKTM. ResultsThe median set-up error differences (absolute values) between the two positioning methods were 0.30 mm, 0.40 mm, 0.50 mm, 0.15°, 0.10° and 0.10° in the vertical, longitudinal, lateral, yaw, pitch and roll directions, respectively. The largest absolute set-up error differences regarding translation and rotation were 1.5 mm and 1.1°, which occurred in the longitudinal and yaw directions, respectively. Only 35.71% of the pairs of measurements were within the tolerance of 0.5 mm and 0.5° simultaneously. In addition, the non-coplanar field with NC-CBCT correction yielded a higher gamma passing rate than that with Catalyst HDTM correction (P < 0.05), especially for evaluation criteria of 1%/1 mm with a median increase of 12.8%. ConclusionsCatalyst HDTM may not replace NC-CBCT for non-coplanar set-up corrections in single-isocentre NC-VMAT SRS and HSRT for single and multiple brain metastases. The potential role of Catalyst HDTM in intracranial SRS/HSRT needs to be further studied in the future.

Comparison of Tribological Behaviours of Nano SiO2 and ZrO2 Reinforced Polyester Matrix Composite Materials

This paper aimed to examine and develop the tribological behaviour by adding nano reinforcements into the polyester matrix. Nanocomposite materials were obtained by adding nano-sized, 0.5%, 1%, 2% SiO2 and ZrO2 reinforcements to isophthalic polyester. These materials were mixed with mechanical and ultrasonic methods and poured into open moulds. The coefficient of friction and wear rates were obtained using the pin-on-disc test device. Optical and SEM surface images of the composites were examined. The experiments were conducted at room temperature and an average of 70% humidity. Tribological studies were made under a load of 2, 5, 10N and at a sliding speed of 0.5 and 0.1 m/s. It was seen that the nano reinforcements to the polyester matrix improved the tribological properties of the material.

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.

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy

BackgroundRespiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy. MethodBased on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively. ResultsHigh consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively. ConclusionsThis work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.

Innovative integration of augmented reality and optical surface imaging: A coarse-to-precise system for radiotherapy positioning.

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses, and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. To propose a noninvasive radiotherapy positioning system integrating augmented reality (AR) and optical surface, and to evaluate its feasibility in clinical workflow. AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, the AR-based coarse guidance and the optical surface-based precise verification. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association, and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. The point clouds of body surface were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in six degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, (i) quality assurance workflow, (ii) errors of both steps and correlation analysis, (iii) receiver operating characteristic (ROC), (iv) distance characteristics of accuracy, and (v) clinical positioning efficiency. The maximum errors in phantom evaluation were 3.4±2.5mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation, respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.1vs. 1.7±0.8, p<0.01), Lng (2.3±1.1vs. 1.4±0.9, p<0.01), and Rtn (0.8±0.4vs. 0.6±0.3, p=0.01) while relatively similar in Lat (1.8±0.9vs. 1.7±0.8, p=0.07). The translation displacement range, after coarse guidance step, required for high accuracy of the optical surface component of the integrated system was 0-42mm, and the average positioning duration of the integrated system was significantly less than that of conventional workflow (355.7±21.7 vs. 387.7±26.6 s, p<0.01). The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection. Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm. Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

Reproducibility and stability of spirometer-guided deep inspiration breath-hold in left-breast treatments using an optical surface monitoring system.

The aim of this study was to evaluate the reproducibility and stability of left breast positioning during spirometer-guided deep-inspiration breath-hold (DIBH) radiotherapy using an optical surface imaging system (AlignRT). The AlignRT optical tracking system was used to monitor five left-sided breast cancer patients treated using the Active Breathing Coordinator spirometer with DIBH technique. Treatment plans were created using an automated hybrid-VMAT technique on DIBH CTs. A prescribed dose of 60Gy to the tumor bed and 50Gy to the breast in 25 fractions was planned. During each treatment session, the antero-posterior (VRT), superior-inferior (LNG), and lateral (LAT) motion of patients was continuously recorded by AlignRT. The intra-breath-hold stability and the intra- and inter-fraction reproducibility were analyzed for all breath-holds and treatment fractions. The dosimetric impact of the residual motion during DIBH was evaluated from the isocenter shifts amplitudes obtained from the 50%, 90%, and 100% cumulative distribution functions of intra-fractional reproducibility. The positional variations of 590 breath-holds as measured by AlignRT were evaluated. The mean intra-breath-hold stability during DIBH was 1.0±0.4mm, 2.1±1.9mm, and 0.7±0.5mm in the VRT, LNG, and LAT directions, with a maximal value of 8.8mm in LNG direction. Similarly, the mean intra-breath-hold reproducibility was 1.4±0.8mm, 1.7±1.0mm, and 0.8±0.5mm in the VRT, LNG, and LAT directions, with a maximal value of 4.1mm in LNG direction. Inter-fractional reproducibility showed better reliability, with difference in breathing levels in all fractions of 0.3mm on average. Based on tolerance limits corresponding to the 90% cumulative distribution level, gating window widths of 1mm, 2mm, and 5mm in the LAT, VRT, and LNG directions were considered an appropriate choice. In conclusion, despite the use of a dedicated spirometer at constant tidal volume, a non-negligible variability of the breast surface position has been reported during breath-holds. The real-time monitoring of breast surface using surface-guided optical technology is strongly recommended.

Evaluation of the accuracy of a six-degree-of-freedom robotic couch using optical surface and cone beam CT images of an SRS QA phantom

Abstract Purpose: To assess the accuracy of the Varian PerfectPitch six-degree-of-freedom (6DOF) robotic couch by using a Varian SRS QA phantom. Methods: The stereotactic radiosurgery (SRS) phantom has five tungsten carbide BBs each with 7·5 mm in diameter arranged with the known geometry. Optical surface images and cone beam CT (CBCT) images of the phantom were taken at different pitch, roll and rotation angles. The pitch, roll, and rotation angles were varied from −3 to 3 degrees by inputs from the linac console. A total of 39 Vision RT images with different rotation angle combinations were collected, and the Vision RT software was used to determine the rotation angles and translational shifts from those images. Eight CBCT images at most allowed rotational angles were analysed by in-house software. The software took the coordinates of the voxel of the maximum CT number inside a 7·5-mm sphere surrounding one BB to be the measured position of this BB. Expected BB positions at different rotation angles were determined by multiplying measured BB positions at zero pitch and roll values by a rotation matrix. Applying the rotation matrix to 5 BB positions yielded 15 equations. A linear least square method was used for regression analysis to approximate the solutions of those equations. Results: Of the eight calculations from CBCT images, the maximum rotation angle differences (degree) were 0·10 for pitch, 0·15 for roll and 0·09 for yaw. The maximum translation differences were 0·3 mm in the left–right direction, 0·5 mm in the anterior–posterior direction and 0·4 mm in the superior–inferior direction. Conclusions: The uncertainties of the 6-DOF couch were examined with the methods of optical surface imaging and CBCT imaging of the SRS QA phantom. The rotational errors were less than 0·2 degree, and the isocentre shifts were less than 0·8 mm.

Inhibition Reaction Behaviour of Combined Grapefruit and Lemongrass Plant Distillates on Plain Carbon Steel Degradation

Applications of harmless fluid derivative for the sustenance of carbon steel in corrosive environments are more appropriate for environmental sustainability and safety of personnel. Admixture of grapefruit and lemongrass distillates was assessed for their protective performance on plain carbon steel within 0.5 M of H2SO4 and HCl solution through weight loss analysis and optical macroscopy studies. Results shows the plant extracts effectively stifled corrosion in both acids with inhibition value range (lowest to highest) of 98.65%-99.5% in H2SO4 solution and 96.31%- 98.39% in HCl. PCS in H2SO4 and HCl electrolyte at 0% extract concentrate were 5.330 mm/y and 2.701 mm/y. Inclusion of the distillate to the electrolyte significantly reduced the corrosion rates in both acids to midpoint values of 0.0448 mm/y and 0.072 mm/y at 240 h of test. The oil extract strongly adsorbed unto the steel surface by means of chemisorption with midpoint Gibbs free energy value above -44 Kjmol-1 signifying strong covalent bonding. The extract adsorption aligned with Langmuir and Frumkin adsorption isotherms with interaction coefficient above 0.9. Optical images of steel surface without inhibitor showed a severely corroded morphology and significant reduction in thickness. The protected steel substantially differs the earlier observation due to the presence of the oil extract which passivated the steel exterior.

Deep Learning-Driven Real-Time Liver Tumor Localization via Optical Surface Imaging and Biomechanical Modeling

<h3>Purpose/Objective(s)</h3> To track liver tumors in real time via a deep learning-based framework, by exploring patient-specific correlations between patients' body surface maps and internal anatomical motion. <h3>Materials/Methods</h3> Real-time liver tumor localization was achieved via deformable registration-based motion estimation, in a two-step framework: (a) liver boundary motion estimation via deep features learnt from optical body surface imaging; and (b) intra-liver motion propagation via deep learning-based biomechanical modeling from liver boundary motion. In step (a), a patient-specific, fully-connected convolutional neural network (SurfCNN) predicts the motion of nodes residing on the boundary of a patient-specific reference liver mesh, by extracting saliency features from curvature entropy maps derived out of real-time optical body surface images. Subsequently, step (b) uses the liver boundary motion solved in (a) to infer intra-liver tumor motion, using a U-Net-style model (UNet-Bio) inspired by biomechanical modeling via finite element analysis (FEA). The cascaded framework was evaluated using a dataset of 8 liver cancer patients from our institute. Each patient had a 10-phase, contrast-enhanced 4D-CT set with liver and liver tumor contoured. To generate sufficient motion variations to train and test the patient-specific SurfCNN model, we augmented the dataset of each patient by simulating varying real-time motion patterns and magnitudes. The augmentation was achieved via a principal component analysis-based statistical model, expanding the nine inter-phase deformation-vector-fields (DVFs) of each 4D-CT into 1,728 different motion states including both rigid and deformable motion. Optical surface imaging was simulated from each motion state via extracted body surface maps, while corresponding liver boundary motion was derived via the augmented DVFs to supervise the training of SurfCNN. UNet-Bio, on the other hand, could be trained as a population-based model using supervisions from intra-liver DVFs solved by FEA-based biomechanical modeling. The liver tumor localization accuracy was assessed through Dice similarity coefficient (DSC), Hausdorff distance (HD), and center-of-mass-error (COME), by comparing the augmentation DVF-propagated ‘ground-truth' liver tumor volumes against ones deformed by the cascaded framework. <h3>Results</h3> Tested using 576 unseen scenarios for each patient case, the cascaded SurfCNN and UNet-Bio scheme can localize liver tumors to 0.791(mean) ± 0.141 (s.d.) in DSC, 3.6 ± 1.9 mm in HD, and 2.3 ± 1.7 mm in COME. In comparison, the prior reference image without deformable registration yielded 0.534 ± 0.277 in DSC, 8.3 ± 6.1 mm in HD and 6.8 ± 5.8 mm in COME. The whole model inference time was less than 100 milliseconds, satisfying the temporal constraints of real-time imaging. <h3>Conclusion</h3> The deep learning-based framework allows accurate 3D liver tumor tracking in real-time via non-ionizing, marker-less, and high frame-rate optical surface imaging.