Research ArticleClinical Studies
Open Access

Dosimetric Evaluation of CyberKnife Synchrony System for Liver Tumors With Respiratory Phase Shifts

SAKIKO YOSHIOKA, YUICHI AKINO, HIROYA SHIOMI, TAKERO HIRATA, NAOKI KAI, KAZUHIKO OGAWA and MASAHIKO KOIZUMI
In Vivo November 2022, 36 (6) 2861-2868; DOI: https://doi.org/10.21873/invivo.13026

Abstract

Background/Aim: This study evaluated the effects of the respiratory phase shifts between liver tumor and chest wall motions on the dose distribution for the CyberKnife Synchrony respiratory tracking system (SRTS). Patients and Methods: Eight patients who received stereotactic body radiotherapy for hepatocellular carcinoma or liver metastases were analyzed. Three-dimensional (3D) motion of the implanted fiducial markers and vertical motion of the sternal bone were derived from the four-dimensional computed tomography (4D-CT) images acquired with a 320-row area detector CT. For each patient, Gaussian random numbers were generated for the standard deviation of the tracking error calculated from the phase shift and a literature. For each voxel of the target, the dose delivered from each beam was calculated 100 times with the random 3D offsets representing the tracking error. Results: The median respiratory phase shifts were 6.0% and 4.6% for the anterior-posterior (AP) and superior-inferior (SI) directions, respectively. The median motion tracking errors influenced by respiratory phase shifts were 1.21 mm and 0.96 mm for the AP and SI directions, respectively. The change in the dose covering 90% of the target (D90%) was within 1.1% when median phase shifts were considered. When evaluating the 90th percentile of the phase shifts, the D90% decreased up to 6.6%. Conclusion: We have developed a technique to estimate the impact of the respiratory phase shifts on the dose distribution of a liver tumor treated with the SRTS. The calculation of the respiratory phase shifts from the area-detector 4D-CT will be valuable to improve the tracking accuracy of the SRTS.

Key Words:

Stereotactic body radiotherapy (SBRT) for liver tumors has shown good local control (1-3). However, respiratory motion of the liver leads to extended irradiated volume of healthy tissue and inhomogeneous dose distribution (4, 5). Various methods have been introduced to reduce the effects of respiratory motion, including abdominal compression, respiratory gating, deep inspiration breath hold irradiation, and respiratory motion tracking (6-8). CyberKnife is a stereotactic radiotherapy system with a linear accelerator mounted on a robotic arm (9). This system allows irradiations from multiple directions to achieve excellent conformity of the dose delivered to the tumor. Synchrony Respiratory Tracking System (SRTS) enables irradiation with tracking a moving tumor (8). The SRTS acquires respiratory signals from surrogate light-emitting diode (LED) markers placed on patient abdomen. The relationship between the respiratory phase and the positions of the fiducial markers implanted near the tumor is modelled to predict the position of the tumor 115 ms ahead of time.

Some literature reported that some patients exhibit a phase shift between the tumor and surrogate marker motions (10, 11). Furthermore, Akino et al. previously reported that the tracking accuracy of the SRTS deteriorates for motion patterns with the respiratory phase shifts (12). Evaluation of respiratory phase shifts for individual patients is challenging. Usually four-dimensional computed tomography (4D-CT) cannot acquire true dynamic images because the scanner repeats axial image acquisitions at multiple subsections and generates 3D volumes for multiple respiratory phases by extracting images for each phase from subsections (13, 14). Although cine-magnetic resonance imaging (MRI) can provide dynamic images at the intersection of the tumor, patient midline is not simultaneously visualized if the tumor is located at off-center (15, 16).

Aquilion ONE (Canon Medical Systems, Otawara, Japan) is a 320-row area-detector CT, which enables volumetric image acquisition for a range of 16 cm with one rotation (17). This scanner enables dynamic 4D image acquisitions by repeat of scans at the same position. The purpose of this study was to evaluate the respiratory phase shifts between fiducial gold markers implanted into the liver and patient skin surface motions by the dynamic 4D-CT images for patients who received SBRT for liver tumors. In addition, the impact of the respiratory phase shifts on the dose distribution of the target was evaluated.

Patients and Methods

Imaging datasets. Following approval of the institutional review board, eight patients who received CyberKnife SBRT to liver tumors were investigated. Table I shows the tumor locations and the prescribed doses. Gold Anchor (Naslund Medical AB, Huddinge, Sweden) fiducial markers were implanted to all patients. The planning CT images were acquired for free breathing with the Aquilion ONE, and 4D-CT images were subsequently acquired. The scan length, slice thickness, and image acquisition interval of the 4D-CT were 16 cm, 1 mm, and 0.5 s, respectively. The use of the SRTS is limited to few patients due to small motion amplitude of the tumor or inappropriate gold marker implantation. Patients 1-7 were immobilized with Hip-Fix (Civco Medical Solutions, Orange City, IA, USA) and treated without SRTS because of small motion amplitude of the tumor. Patient 8 was treated with SRTS. In this study, we simulated the impact of the tracking errors for all cases.

View this table:
Table I.

Therapeutic information for all patients.

Evaluation of respiratory phase shifts. The 3D coordinates of the implanted fiducial markers were calculated by a projection method using a MATLAB R2020a (Math Works, Natick, MA, USA) as illustrated in Figure 1. For each patient, regions of interest (ROIs) large enough to include the fiducial markers for all respiratory phases were set on the Sagittal images. The images inside the ROIs were binarized and the image processing of the opening (dilation followed by erosion) and closing (erosion followed by dilation) were used to improve the detection of the centroid of the marker. In this study, the motions in anterior-posterior (AP) and superior-inferior (SI) directions extracted from the Sagittal images were evaluated because many studies have reported that the liver tumor motion in lateral direction is small (15, 18, 19).

Figure 1.
Figure 1.

Schematic image of the projection method for detection of the implanted fiducial markers.

The area-detector CT does not require surrogate markers on the patient body for acquisition of 4D-CT images. In this study, the motion of the sternal bone in AP direction was assessed as the patient surface motion. The sagittal images of the patient mid plane were generated for each respiratory phase, and the motion of the xiphoid process was analyzed by the template matching technique.

To investigate the accuracy of the detection of the implanted fiducial markers, the markers on the CT images were delineated on the Eclipse ver. 15.1 (Varian Medical Systems, Palo Alto, CA, USA), a commercial treatment planning system (TPS), and the calculated centroid of the contour was compared with that analyzed with the projection method.

The respiratory phases were defined by the peaks of the end-inhalation as 0% (or 100%), and the other phases between each end-inhalation peak were interpolated. The respiratory phase shifts were defined as the difference between the implanted fiducial maker and chest respiratory phases. The patterns of respiratory phase shifts were defined as “delay” for the chest wall movement followed by the tumor movement and “early” for the tumor movement followed by the chest wall movement.

Evaluation of dose distribution influenced by respiratory phase shifts. The effects of the motion tracking accuracy on the dose distribution due to the respiratory phase shifts were analyzed using a ShioRIS 2.0 software (RADLab Inc., Osaka, Japan), as illustrated in Figure 2. The software calculates the dose distributions of the individual CyberKnife beams separately using the pencil beam algorithm. The calculation accuracy of the software has been reported elsewhere (20). The dose-volume histogram (DVH) of the static target volume is calculated by following two steps: (i) the dose distribution is calculated for each beam with a grid size of 1 mm for all directions, (ii) the target contour is segmented into voxels with the resolution of the CT images, (iii) for each voxel, the dose at the 3D coordinate of the voxel is collected from each beam, and summed dose is calculated. The DVH for inaccurate beam delivery is simulated by adding an offset value to the 3D coordinate of all voxels during step iii. In this study, the motion tracking error of the SRTS was assumed as a Gaussian distribution. The standard deviation (SD) for generation of the Gaussian random numbers was determined from the previous report (12). The SD values are listed in Table II. For each voxel of the target, the dose delivered from each beam was calculated 100 times with the random 3D offsets representing the tracking error, and the mean value was calculated as the voxel dose.

Figure 2.
Figure 2.

Schematic image of the calculation of DVH with consideration of the tracking errors.

View this table:
Table II.

SD of the motion tracking errors influenced by respiratory phase shifts.

Results

Accuracy of marker detections. Figure 3A shows an example case of the implanted fiducial marker positions detected by the projection method and calculated on the Eclipse TPS. The marker positions derived from these two methods showed good agreement. Figure 3B shows the differences between the marker positions detected by these two methods. The accuracy of marker detection was 0.21±0.53 mm and 0.30±0.87 mm for AP and SI directions, respectively. Figure 3C shows the motion amplitude of the implanted fiducial markers of eight patients in AP and SI directions. Patient #8 showed the largest motion amplitude exceeding 20 mm in SI direction.

Figure 3.
Figure 3.

Accuracy of the marker detection and the motion amplitude of the marker. (A) Positions of the implanted fiducial markers in AP direction determined by the projection method and Eclipse TPS. (B) Differences of the fiducial marker positions between the projection method and the Eclipse TPS. Points and bars represent mean and SD, respectively. (C) Maximum motion amplitude of eight patients in AP and SI directions.

Respiratory phase shifts. Figure 4A shows the motions of the implanted fiducial markers and chest wall. The respiratory phase shift between these two motions is clearly shown. Figure 4B shows the respiratory phase shifts of eight patients in AP and SI directions. Negative and positive values represent the delay and early phase shifts, respectively. Many patients showed systematic positive or negative phase shifts. Patient #2 did not show the phase shift in SI direction. Patient #4 and #8 showed large SDs with respect to the mean values. The median respiratory phase shifts of the eight patients were 6.00% (range=2.63-26.67%) and 4.57% (range=0-6.25%) for AP and SI directions, respectively.

Figure 4.
Figure 4.

Evaluation of the respiratory phase shifts. (A) Examples of implanted fiducial marker and chest wall motions showing respiratory phase shifts. (B) Respiratory phase shifts of eight patients. Negative and positive values represent the delay and early phase shifts, respectively. Columns and bars represent mean and SD, respectively. (C) SDs of the tracking errors calculated from the respiratory phase shifts and the literature (12).

Figure 4C shows the SD of the motion tracking accuracy of the SRTS derived from the literature (12). Although many patients showed SD within 1.5 mm, patient #1 and #8 showed large values in AP direction. The median motion tracking errors were 1.21 mm (range=0.77-2.66 mm) and 0.96 mm (range=0.74-1.62 mm) for AP and SI directions, respectively.

Evaluation of dose distribution influenced by respiratory phase shifts. In this study, 100 Gaussian random numbers were generated for each beam and for both AP and SI directions to simulate the tracking errors of the SRTS. To investigate the validity of this iteration number, the DVH was calculated for the iteration numbers of 1, 3, 5, 10, 30, and 100 and the DVH calculations were repeated 10 times. The maximum variation of the PTV D95% for 10 calculations were 1.81%, 1.52%, 1.49%, 1.06%, 0.41%, 0.31% for 1, 3, 5, 10, 30, and 100 iterations, indicating that 100 iterations were sufficient to obtain statistically reliable data.

Figure 5A and B show the DVHs of patient #8 and #5, respectively. The original plan and 10 simulations with consideration of tracking errors are shown. For each patient, median respiratory phase shift was used to calculate the SD of the tracking error. Patient #5 showed negligibly small variations between original and simulated DVHs. Of note, patients #1-7 showed similar data, while patient #8 showed a decrease in the target dose coverage owing to the large tracking error.

Figure 5.
Figure 5.

Example of the DVHs with and without consideration of the tracking errors. (A) DVH of patient #8 calculated for the 50th percentile of respiratory phase shifts. DVH of patient #5 calculated for the (B) 50th, (C) 75th, and (D) 90th percentile of respiratory phase shifts. The horizontal axis represents the percentage of the prescribed dose. Original DVH curves calculated for static target are also shown.

Patient breathing is not often stable. To simulate the near-worst cases, the tracking errors for the 75th and 90th percentile values of the respiratory phase shifts were also evaluated. Figure 5C and D show example DVHs of patient #5 with consideration of the tracking errors for 75th- and 90th-percentile of the respiratory phase shifts. The median differences between D90% of the original and simulated doses were −0.35%±0.32%, −0.6%±1.11%, and −0.9%±2.12% for the tracking errors calculated for the 50th, 75th, and 90th percentiles of phase shifts, respectively. The median differences of D90% are summarized in Figure 6. For most cases, the motion tracking errors influenced by the respiratory phase shifts were small owing likely to the small motion amplitude. As shown in the case of patient #8, large motion amplitude and phase shifts in patients caused significant tracking errors.

Figure 6.
Figure 6.

Change in D90% of the target.

Discussion

In this study, we evaluated the impact of the respiratory phase shifts on the target coverage of the CyberKnife SRTS. Although the median respiratory phase shifts resulted in limited impact on the D90% of the target, 90th percentile representing near worst cases showed larger decrease of D90% up to 6.6%.

Chan et al. previously reported the effects of the motion tracking accuracy of the SRTS for liver tumors (21). They analyzed the residual errors and rotation of the target from the fiducial markers and reported that the median PTV coverage decreased by 1.1%. In the present study, the tracking errors were considered as the 3D translation of the target, but the target rotation was not considered. To our best of knowledge, however, no study has investigated the impact of the respiratory phase shifts during SRTS on the DVH of the target. Tse et al. investigated the motion tracking accuracy of the Radixact Synchrony system by film dosimetry and reported that the respiratory phase shifts resulted in large tracking errors especially for patients with short respiration cycle (22). Zang et al. analyzed the treatment log files of the SRTS for liver tumors and reported that the hysteresis and area trajectory patterns of liver motion reduced the tracking accuracy of the SRTS (23). When large respiratory phase shifts or irregular breathing patterns are observed before treatment planning, the treatment accuracy would be improved by technical interventions. Akino et al. reported that slow breathing improves the tracking accuracy of the SRTS for patients with phase shift (12). Lee et al. showed that audiovisual biofeedback improved the correlation between lung tumor and external surrogate motions (11). In this study, the respiratory phase shift was evaluated using the 320-row area-detector 4D-CT. This technique will be valuable to assess the respiratory pattern of each patient before treatment planning.

On the 4D-CT images, the fiducial marker positions were accurately calculated by using the projection method. For some patients, the 4D-CT images were acquired after contrast-enhancement. Patient #2 showed a slightly larger difference (1.2 mm) probably because the marker was located near the portal vein with contrast reagent. For patients #1, #2, #5, and #8, who were implanted with line-shaped fiducial markers, relatively large variations of the marker detection accuracy were observed. Because the Eclipse TPS uses 3D mesh-based delineation algorithm, contouring of thin object is challenging. However, the positions of most of the markers were accurately detected.

The median motion tracking errors influenced by the respiratory phase shifts were 1.21 mm and 0.96 mm in AP and SI directions, respectively. Sumida et al. reported a method for evaluating the tracking errors of SRTS using a webcam mounted on a motion phantom and reported that the tracking error was 1-2 mm (24). Inoue et al. investigated the tracking error of SRTS by using a camera mounted on the CyberKnife® and reported that the accuracy for probability of >95% was 1.5 mm (25). The tracking errors evaluated in this study were similar to those of previous studies, although patient #8 showed larger error. Patients 1-7 were immobilized with Hip-Fix, whereas patient #8 was treated with SRTS without abdominal compression. The tracking error impact would be larger if the patients are treated without abdominal compression. Similarly, Marants et al. generated the respiratory phase shifts between the target and surrogate markers using two motion phantoms and reported large positional differences between the target positions predicted by the SRTS and the actual positions (26).

This study has the following limitations: first, the ShioRIS software calculates dose distribution using a pencil beam algorithm. Although this software considers the attenuation of the primary photons for heterogeneous tissue, it cannot calculate the lateral electron transport. However, all cases evaluated in this study were liver tumors. Because the target is surrounded by uniform soft tissue, the effects of the heterogeneity corrections would be small. Second, many of the patients evaluated in this study were treated with the spine-tracking method because of the limited number of liver tumor cases in our institutions. Finally, although correlation between the respiratory phase shifts and the motion tracking errors listed in Table II was determined for a respiratory cycle of 4 s, the respiratory cycle of the patients evaluated in this study ranged from 1.5 s to 7.5 s. Previous studies argued that tracking errors increase in cases of high velocity tumor motion (24, 27). More accurate results can be obtained if the motion tracking errors are measured using CyberKnife® and the motion phantom moves with the actual patient respiration data.

Conclusion

This study demonstrated a technique to evaluate the respiratory phase shifts using the dynamic 4D-CT acquired with the area-detector CT and to estimate the impact of the phase shifts on the dose distribution. This method would enable quick evaluation of the patient’s respiratory patterns before treatment planning and enable feedback to treatment strategies including extended margin and coaching slow breathing.

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP20K08022.

Footnotes

  • Authors’ Contributions

    Conceptualization: Sakiko Yoshioka and Yuichi Akino; Funding: Yuichi Akino; Data acquisition: Sakiko Yoshioka, Yuichi Akino, and Naoki Kai; Investigation: Sakiko Yoshioka, Yuichi Akino, and Hiroya Shiomi, Supervision: Yuichi Akino, Hiroya Shiomi, Takero Hirata, Naoki Kai, Kazuhiko Ogawa, and Masahiko Koizumi.

  • Conflicts of Interest

    The Author HS is a developer of the ShioRIS 2.0 software, which was used in this study.

  • Received August 20, 2022.
  • Revision received August 29, 2022.
  • Accepted August 30, 2022.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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Dosimetric Evaluation of CyberKnife Synchrony System for Liver Tumors With Respiratory Phase Shifts
SAKIKO YOSHIOKA, YUICHI AKINO, HIROYA SHIOMI, TAKERO HIRATA, NAOKI KAI, KAZUHIKO OGAWA, MASAHIKO KOIZUMI
In Vivo Nov 2022, 36 (6) 2861-2868; DOI: 10.21873/invivo.13026
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