Technical evaluation of a laser-based optical surface scanning system for prospective and retrospective breathing adapted computed tomography

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.

AAPM task group report 302: Surface guided radiotherapy

The clinical use of surface imaging has increased dramatically with demonstrated utility for initial patient positioning, real-time motion monitoring, and beam gating in a variety of anatomical sites. The Therapy Physics Subcommittee and the Imaging for Treatment Verification Working Group of the American Association of Physicists in Medicine commissioned Task Group 302 to review the current clinical uses of surface imaging and emerging clinical applications. The specific charge of this task group was to provide technical guidelines for clinical indications of use for general positioning, breast deep-inspiration breath-hold (DIBH) treatment, and frameless stereotactic radiosurgery (SRS). Additionally, the task group was charged with providing commissioning and on-going quality assurance (QA) requirements for surface guided radiation therapy (SGRT) as part of a comprehensive QA program including risk assessment. Workflow considerations for other anatomic sites and for computed tomography (CT) simulation, including motion management are also discussed. Finally, developing clinical applications such as stereotactic body radiotherapy (SBRT) or proton radiotherapy are presented. The recommendations made in this report, which are summarized at the end of the report, are applicable to all video-based SGRT systems available at the time of writing. Review current use of non-ionizing surface imaging functionality and commercially available systems. Summarize commissioning and on-going quality assurance (QA) requirements of surface image-guided systems, including implementation of risk or hazard assessment of surface guided radiotherapy as a part of a total quality management program (e.g., TG-100). Provide clinically relevant technical guidelines that include recommendations for the use of SGRT for general patient positioning, breast DIBH, and frameless brain SRS, including potential pitfalls to avoid when implementing this technology. Discuss emerging clinical applications of SGRT and associated QA implications based on evaluation of technology and risk assessment. This article is protected by copyright. All rights reserved.

Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy

Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1–2 minutes.

Clinical paradigms and challenges in surface guided radiation therapy: Where do we go from here?

Surface guided radiotherapy (SGRT) is becoming a routine tool for patient positioning for specific clinical sites in many clinics. However, it has not yet gained its full potential in terms of widespread adoption. This vision paper first examines some of the difficulties in transitioning to SGRT before exploring the current and future role of SGRT alongside and in concert with other imaging techniques. Finally, future horizons and innovative ideas that may shape and impact the direction of SGRT going forward are reviewed.

Recent advanced in Surface Guided Radiation Therapy

The growing acceptance and recognition of Surface Guided Radiation Therapy (SGRT) as a promising imaging technique has supported its recent spread in a large number of radiation oncology facilities. Although this technology is not new, many aspects of it have only recently been exploited. This review focuses on the latest SGRT developments, both in the field of general clinical applications and special techniques.SGRT has a wide range of applications, including patient positioning with real-time feedback, patient monitoring throughout the treatment fraction, and motion management (as beam-gating in free-breathing or deep-inspiration breath-hold). Special radiotherapy modalities such as accelerated partial breast irradiation, particle radiotherapy, and pediatrics are the most recent SGRT developments.The fact that SGRT is nowadays used at various body sites has resulted in the need to adapt SGRT workflows to each body site. Current SGRT applications range from traditional breast irradiation, to thoracic, abdominal, or pelvic tumor sites, and include intracranial localizations.Following the latest SGRT applications and their specifications/requirements, a stricter quality assurance program needs to be ensured. Recent publications highlight the need to adapt quality assurance to the radiotherapy equipment type, SGRT technology, anatomic treatment sites, and clinical workflows, which results in a complex and extensive set of tests.Moreover, this review gives an outlook on the leading research trends. In particular, the potential to use deformable surfaces as motion surrogates, to use SGRT to detect anatomical variations along the treatment course, and to help in the establishment of personalized patient treatment (optimized margins and motion management strategies) are increasingly important research topics. SGRT is also emerging in the field of patient safety and integrates measures to reduce common radiotherapeutic risk events (e.g. facial and treatment accessories recognition).This review covers the latest clinical practices of SGRT and provides an outlook on potential applications of this imaging technique. It is intended to provide guidance for new users during the implementation, while triggering experienced users to further explore SGRT applications.

Automatic calibration of an arbitrarily-set near-infrared camera for patient surface respiratory monitoring

PURPOSE

A patient's respiratory monitoring is one of the key techniques in radiotherapy for a moving target. Generally, such monitoring systems are permanently set to a fixed geometry during the installation. This study aims to enable a temporary setup of such a monitoring system by developing a fast method to automatically calibrate the geometrical position by a quick measurement of calibration markers.

METHODS

One calibration marker was placed on the isocenter and the other six markers were placed at positions 5-cm apart from the isocenter to the left, right, anterior, posterior, superior, and inferior directions. A near-infrared (NIR) camera (NIC) [Kinect v2 (Microsoft Corp.)] was arbitrarily set with ten different angles around the calibration phantom with a fixed tilting-down angle at approximately 45° in a linear accelerator treatment vault. The three-dimensional (3D) coordinates in the camera (Cam) coordinate system (CS; x and y are the horizontal and vertical coordinates of the image, respectively, and z is a coordinate along the NIR time-of-flight) were taken for 1 min with 30 frames per second. The data corresponding to the measurement times of 1, 3, 10, 30, and 60 s were created to mimic various measurement times. These data were used to calculate the initial matrix elements, which included six parameters of the pitching, yawing, and rolling angles; horizontal two-dimensional translation in the treatment room; and the source-to-axis distance of NIC, for a conversion from the Cam CS to the treatment room CS for which the origin was defined at the isocenter (Iso coordinate). The six parameters were then optimized to minimize the displacements of the calculated marker coordinates from the actual positions in the Iso CS. The 3D positional accuracy and angular accuracy of the conversion were evaluated. The random error of the Iso coordinates was analyzed through a relation with the angle of each measurement setup.

RESULTS

Three angles of NIC and relative translation vectors were successfully calculated from the measurement data of the calibration markers. The achieved spatial and angular accuracies were 0.02 mm and 1.6°, respectively, after the optimization. Among the mimicked measurement times investigated in this study, both spatial and angular accuracies had no dependence on the measurement time. The average random error of a static marker was 0.46 mm after the optimization.

CONCLUSION

We developed an automatic method to calibrate the 3D patient surface monitoring system. The procedure developed in this study enabled a quick calibration of NIC, which can be easily repeated multiple times for a frequent and quick setup of the monitoring system.

An effective method to reduce the interplay effects between respiratory motion and a uniform scanning proton beam irradiation for liver tumors: A case study

Purpose

For scanning particle beam therapy, interference between scanning patterns and interfield organ motion may result in suboptimal dose within target volume. In this study, we developed a simple offline correction technique for uniform scanning proton beam (USPB) delivery to compensate for the interplay between scanning patterns and respiratory motion and demonstrate the effectiveness of our technique in treating liver cancer.

Methods

The computed tomography (CT) and respiration data of two patients who had received stereotactic body radiotherapy for hepatocellular carcinoma were used. In the simulation, the relative beam weight delivered to each respiratory phase is calculated for each beam layer after treatment of each fraction. Respiratory phases with beam weights higher than 50% of the largest weight are considered “skipped phases” for the next fraction. For the following fraction, the beam trigger is regulated to prevent beam layers from starting irradiation in skipped phases by extending the interval between each layer. To calculate dose‐volume histogram (DVH), the dose of the target volume at end‐exhale (50% phase) was calculated as the sum of each energy layer, with consideration of displacement due to respiratory motion and relative beam weight delivered per respiratory phase.

Results

For a single fraction, D_1%, D_99%, and V_100% were 114%, 88%, and 32%, respectively, when 8 Gy/min of dose rate was simulated. Although these parameters were improved with multiple fractions, dosimetric inhomogeneity without motion management remained even at 30 fractions, with V_100% 86.9% at 30 fractions. In contrast, the V_100% values with adaptation were 96% and 98% at 20 and 30 fractions, respectively. We developed an offline correction technique for USPB therapy to compensate for the interplay effects between respiratory organ motion and USPB beam delivery.

Conclusions

For liver tumor, this adaptive therapy technique showed significant improvement in dose uniformity even with fewer treatment fractions than normal USPB therapy.

Dynamic CTA in Native Kidneys Using a Multiphase CT Protocol-Potential of Significant Reduction of Contrast Medium

RATIONALE AND OBJECTIVE

The objective of this study was to assess an optimized renal multiphase computed tomography angiography (MP-CTA) protocol regarding reduction of contrast volume.

MATERIALS AND METHODS

Thirty patients underwent MP-CTA (12 phases, every 3.5 seconds, 80 kV/120 mAs) using 30 mL of contrast medium. The quality of MP-CTA was assessed quantitatively measuring vessel attenuation, image noise, and contrast-to-noise ratio. MP-CTA was evaluated qualitatively regarding depiction of vessels, cortex differentiation, and motion artifacts (grades 1-4, 1 = best). Mean effective radiation dose was registered. Results were compared to standard renal computed tomography angiography (CTA) (80 mL). Student t test was applied, if variables followed normal distribution. For other variables, nonparametric Mann-Whitney U test was used.

RESULTS

All acquisitions were successfully performed, and no patient had to be excluded from the study. MP-CTA enabled high attenuation (aorta: 503 ± 91 HU, renal arteries: 450 ± 73 HU/456 ± 72 HU) at adequate image noise (13.7 ± 1.5) and good contrast-to-noise ratio (34.2 ± 10.2). Good attenuation of renal veins was observed (286 ± 43 HU/282 ± 42 HU). Arterial enhancement was significantly higher compared to renal CTA (aorta: 396 ± 90 HU, renal arteries: 331 ± 74 HU/333 ± 80 HU; P < .001). MP-CTA protocol enabled good image quality of renal arteries (1.5 ± 0.6) and veins (1.7 ± 0.6). Cortex differentiation and motion artifacts were ranked 1.8 ± 0.8 and 1.6 ± 0.8. The mean effective radiation dose was 9 mSv (MP-CTA).

CONCLUSIONS

Compared to standard renal CTA, the renal MP-CTA enabled the significant reduction of contrast volume and simultaneously provided a significantly higher arterial attenuation.

Stereoscopic X-ray imaging, cone beam CT, and couch positioning in stereotactic radiotherapy of intracranial tumors: preliminary results from a cross-modality pilot installation

Background

To assess the accuracy and precision of a fully integrated pilot installation of stereoscopic X-ray imaging and kV-CBCT for automatic couch positioning in stereotactic radiotherapy of intracranial tumors. Positioning errors as detected by stereoscopic X-ray imaging are compared to those by kV-CBCT (i.e. the accuracy of the new method is verified by the established method), and repeated X-ray images are compared (i.e. the precision of new method is determined intra-modally).

Methods

Preliminary results are reported from a study with 32 patients with intracranial tumors. Patients were treated with stereotactic radiotherapy guided by stereoscopic X-ray imaging and kV-CBCT. Patient positioning was automatically corrected by a robotic couch. Cross-modal discrepancies in position detection were measured (N = 42). Intra-modal improvements after correction and re-verification by stereoscopic X-ray imaging were measured (N = 70). The accuracy and precision of stereoscopic X-ray imaging and the accuracy and precision of CBCT were confirmed in phantom measurements (N = 12 shifts of a ball bearing phantom, N = 24 shifts of a head phantom).

Results

After correction based on stereoscopic X-ray imaging 95% of residual mean errors were below 0.4, 0.4, 0.5, and 0.7 mm (lateral, longitudinal, vertical, radial, respectively). Stereoscopic X-ray imaging and CBCT were in close agreement with an average discrepancy of 0.1, 0.5, 0.3 and 0.8 mm, respectively. 95% of discrepancies were below 0.8, 1.2, 1.0, and 1.4 mm, respectively. After correction and re-verification by stereoscopic X-ray imaging, the remaining intra-modal residual error was consistent with zero (p = 0.31, p = 0.48, p = 0.81 in lateral, longitudinal, and vertical direction; p-values from two-tailed t-test). The inherent technical accuracy and precision of stereoscopic X-ray imaging and the accuracy and precision of CBCT were found to be of the order of 0.1 mm in controlled phantom settings.

Conclusions

In a routine clinical setting, both stereoscopic X-ray imaging and CBCT were able to reduce positioning errors by an order of magnitude. The end-to-end precision of the system, measured from the discrepancy (mean) between ExacTrac and CBCT, in a clinical setting seems to be about 0.8 mm radially, including couch positioning. The precision (measured from repeatability of ExacTrac, intra-modal) was found to be about 0.7 mm radially in a clinical setting.