Yelin Suh

An analysis of thoracic and abdominal tumour motion for stereotactic body radiotherapy patients

An analysis of thoracic and abdominal tumour motion for stereotactic body radiotherapy patients was performed using more than 70 h of tumour motion estimated from the correlation between the external and internal motion for 143 treatment fractions in 42 patients. The tumour sites included lungs (30 patients) and retroperitoneum (12 patients). The overall mean respiratory-induced peak-to-trough distance was 0.48 cm, with individual treatment fraction means ranging from 0.02 to 1.44 cm. The overall mean respiratory period was 3.8 s, with individual treatment fraction means ranging from 2.2 to 6.4 s. In 57 treatment fractions (40%), the mean respiratory-induced peak-to-trough distance was greater than 0.5 cm. In general, tumour motion was predominantly superior-inferior (60% of all the treatment fractions), while anterior-posterior and left-right motion were 22% and 18%, respectively. The motion was predominantly linear, and the overall mean of the first principal component was 94%. However, for motion magnitude, direction and linearity, large variations were observed from patient to patient, fraction to fraction and cycle to cycle.

Pancreatic tumor motion on a single planning 4D-CT does not correlate with intrafraction tumor motion during treatment

Purpose:To quantify pancreas tumor motion on both a planning 4D-CT and during a single fraction treatment using the CyberKnife linear accelerator and Synchrony respiratory tracking software, and to investigate whether a single 4D-CT study is reliable for determining radiation treatment margins for patients with locally advanced pancreas cancer. Methods and Materials:Twenty patients underwent fiducial placement, biphasic pancreatic protocol CT scan and 4D-CT scan in the treatment position while free-breathing. Patients were then treated with a single 25 Gy fraction of stereotactic body radiotherapy. Predicted pancreas motion in the superior-inferior (SI), left-right (LR), and anterior-posterior (AP) directions was calculated from the maximum inspiration and maximum expiration 4D-CT scan. For CyberKnife treatments, mean respiratory cycle motion and maximum respiratory cycle motion was determined in the SI, LR, and AP directions. Results:The range of centroid movement based on 4D-CT in the SI, LR, and AP directions were 0.9 to 28.8 mm, 0.1 to 13.7 mm, and 0.2 to 7.6 mm, respectively. During CyberKnife treatment, in the SI direction, the mean motion of the centroid ranged from 0.5 to 12.7 mm. In the LR direction, the mean motion range was 0.4 to 9.4 mm. In the AP direction, the mean motion range was 0.6 to 5.5 mm. The maximum range of movement (mean) during CyberKnife treatment in the SI, LR, and AP directions were 4.5 to 48.8 mm (mean 20.8 mm), 1.5 to 41.3 mm (mean 11.3 mm), and 1.6 to 68.1 mm (mean 13.4 mm), respectively. Neither the maximum or mean motion correlated with the 4D-CT movement. Conclusions:There is substantial respiratory associated motion of pancreatic tumors. The 4D-CT planning scans cannot accurately predict the movement of pancreatic tumors during actual treatment on CyberKnife.

Development and preliminary evaluation of a prototype audiovisual biofeedback device incorporating a patient-specific guiding waveform.

The aim of this research was to investigate the effectiveness of a novel audio-visual biofeedback respiratory training tool to reduce respiratory irregularity. The audiovisual biofeedback system acquires sample respiratory waveforms of a particular patient and computes a patient-specific waveform to guide the patients subsequent breathing. Two visual feedback models with different displays and cognitive loads were investigated: a bar model and a wave model. The audio instructions were ascending/descending musical tones played at inhale and exhale respectively to assist in maintaining the breathing period. Free-breathing, bar model and wave model training was performed on ten volunteers for 5 min for three repeat sessions. A total of 90 respiratory waveforms were acquired. It was found that the bar model was superior to free breathing with overall rms displacement variations of 0.10 and 0.16 cm, respectively, and rms period variations of 0.77 and 0.33 s, respectively. The wave model was superior to the bar model and free breathing for all volunteers, with an overall rms displacement of 0.08 cm and rms periods of 0.2 s. The reduction in the displacement and period variations for the bar model compared with free breathing was statistically significant (p = 0.005 and 0.002, respectively); the wave model was significantly better than the bar model (p = 0.006 and 0.005, respectively). Audiovisual biofeedback with a patient-specific guiding waveform significantly reduces variations in breathing. The wave model approach reduces cycle-to-cycle variations in displacement by greater than 50% and variations in period by over 70% compared with free breathing. The planned application of this device is anatomic and functional imaging procedures and radiation therapy delivery.

A monoscopic method for real-time tumour tracking using combined occasional x-ray imaging and continuous respiratory monitoring.

Three major linear accelerator vendors offer gantry-mounted single (monoscopic) x-ray imagers. The use of monoscopic imaging to estimate three-dimensional (3D) target positions has not been fully explored. The purpose of this work is to develop and investigate a robust monoscopic method for real-time tumour tracking, combining occasional x-ray imaging and continuous external respiratory monitoring, and compare this with an established stereoscopic method. Monoscopic estimation of 3D target positions is a two-step procedure. Step (1) is similar to the stereoscopic approach using combined occasional x-ray imaging and real-time external respiratory monitoring, i.e. to establish the correlation between the target coordinates T(x, y, z) and the external respiratory signal (R) (sECM: stereoscopic external correlation model). However, in monoscopic estimation, the correlation between the two coordinates (xp, yp) projected on the imager plane and the external respiratory signal (mECM: monoscopic external correlation model) is established. With only a single projection, the component of the 3D target position, which is along the x-ray imaging direction, is unresolved. Therefore, step (2) is used to estimate the unresolved component (z( parallel)) by building a correlation model between the unresolved component and the two other components projected on the imager (ICM: internal correlation model) with a prior 3D target trajectory that may be obtained by 4DCT, MV/kV imaging or 4DCBCT. At the time of prediction, (xp, yp) are estimated from (R) using the correlation model in step (1), and then z( parallel) is estimated from the estimated (xp, yp) using the correlation model in step (2). The performance of the proposed method was evaluated under various model update intervals and compared with the stereoscopic estimation method using 160 tumour trajectory and external respiratory motion data recorded at 25 Hz from 46 thoracic and abdominal cancer patients who underwent hypofractionated stereotactic radiotherapy by a CyberKnife system. The precision of the input data used in this study to represent tumour motion was assessed using x-ray imaging to be 1.5 +/- 0.8 mm. Monoscopic imaging every 30/60 s with updating ICM every 120/180 s can estimate target positions with a 1 mm root-mean-square error (RMSE) for 63/53% or a 2 mm RMSE for 93/91%, respectively. In contrast, stereoscopic x-ray imaging every 30/60 s can estimate target motion within a 1 mm RMSE for 72/58% or a 2 mm RMSE for 95/92%, respectively. The overall 3D error of the monoscopic estimation is approximately 10% higher than comparable stereoscopic imaging methods when the period between imaging is 1 s or more, and 40% higher for continuous imaging. The promising result may be explained by the fact that superior/inferior motion-the major axis of tumour motion-is fully resolved even in the monoscopic view for coplanar treatments, and tumour motion in each dimension is relatively well correlated.

Geometric uncertainty of 2D projection imaging in monitoring 3D tumor motion

The purpose of this study was to investigate the accuracy of two-dimensional (2D) projection imaging methods in three-dimensional (3D) tumor motion monitoring. Many commercial linear accelerator types have projection imaging capabilities, and tumor motion monitoring is useful for motion inclusive, respiratory gated or tumor tracking strategies. Since 2D projection imaging is limited in its ability to resolve the motion along the imaging beam axis, there is unresolved motion when monitoring 3D tumor motion. From the 3D tumor motion data of 160 treatment fractions for 46 thoracic and abdominal cancer patients, the unresolved motion due to the geometric limitation of 2D projection imaging was calculated as displacement in the imaging beam axis for different beam angles and time intervals. The geometric uncertainty to monitor 3D motion caused by the unresolved motion of 2D imaging was quantified using the root-mean-square (rms) metric. Geometric uncertainty showed interfractional and intrafractional variation. Patient-to-patient variation was much more significant than variation for different time intervals. For the patient cohort studied, as the time intervals increase, the rms, minimum and maximum values of the rms uncertainty show decreasing tendencies for the lung patients but increasing for the liver and retroperitoneal patients, which could be attributed to patient relaxation. Geometric uncertainty was smaller for coplanar treatments than non-coplanar treatments, as superior-inferior (SI) tumor motion, the predominant motion from patient respiration, could be always resolved for coplanar treatments. Overall rms of the rms uncertainty was 0.13 cm for all treatment fractions and 0.18 cm for the treatment fractions whose average breathing peak-trough ranges were more than 0.5 cm. The geometric uncertainty for 2D imaging varies depending on the tumor site, tumor motion range, time interval and beam angle as well as between patients, between fractions and within a fraction.

Four-dimensional IMRT treatment planning using a DMLC motion-tracking algorithm

The purpose of this study is to develop a four-dimensional (4D) intensity-modulated radiation therapy (IMRT) treatment-planning method by modifying and applying a dynamic multileaf collimator (DMLC) motion-tracking algorithm. The 4D radiotherapy treatment scenario investigated is to obtain a 4D treatment plan based on a 4D computed tomography (CT) planning scan and to have the delivery flexible enough to account for changes in tumor position during treatment delivery. For each of 4D CT planning scans from 12 lung cancer patients, a reference phase plan was created; with its MLC leaf positions and three-dimensional (3D) tumor motion, the DMLC motion-tracking algorithm generated MLC leaf sequences for the plans of other respiratory phases. Then, a deformable dose-summed 4D plan was created by merging the leaf sequences of individual phase plans. Individual phase plans, as well as the deformable dose-summed 4D plan, are similar for each patient, indicating that this method is dosimetrically robust to the variations of fractional time spent in respiratory phases on a given 4D CT planning scan. The 4D IMRT treatment-planning method utilizing the DMLC motion-tracking algorithm explicitly accounts for 3D tumor motion and thus hysteresis and nonlinear motion, and is deliverable on a linear accelerator.

A Deliverable Four-Dimensional Intensity-Modulated Radiation Therapy-Planning Method for Dynamic Multileaf Collimator Tumor Tracking Delivery

PURPOSE To develop a deliverable four-dimensional (4D) intensity-modulated radiation therapy (IMRT) planning method for dynamic multileaf collimator (MLC) tumor tracking delivery. METHODS AND MATERIALS The deliverable 4D IMRT planning method involves aligning MLC leaf motion parallel to the major axis of target motion and translating MLC leaf positions by the difference in the target centroid position between respiratory phases of the 4D CT scan. This method ignores nonlinear respiratory motion and deformation. A three-dimensional (3D) optimal method whereby an IMRT plan on each respiratory phase of the 4D CT scan was independently optimized was used for comparison. For 12 lung cancer patient 4D CT scans, individual phase plans and deformable dose-summed 4D plans using the two methods were created and compared. RESULTS For each of the individual phase plans, the deliverable method yielded similar isodose distributions and dose-volume histograms. The deliverable and 3D optimal methods yielded statistically equivalent dose-volume metrics for both individual phase plans and 4D plans (p > 0.05 for all metrics compared). The deliverable method was affected by 4D CT artifacts in one case. Both methods were affected by high vector field variations from deformable registration. CONCLUSIONS The deliverable method yielded similar dose distributions for each of the individual phase plans and statistically equivalent dosimetric values compared with the 3D optimal method, indicating that the deliverable method is dosimetrically robust to the variations of fractional time spent in respiratory phases on a given 4D CT scan. Nonlinear target motion and deformation did not cause significant dose discrepancies.

On the accuracy of a moving average algorithm for target tracking during radiation therapy treatment delivery.

Real-time tumor targeting involves the continuous realignment of the radiation beam with the tumor. Real-time tumor targeting offers several advantages such as improved accuracy of tumor treatment and reduced dose to surrounding tissue. Current limitations to this technique include mechanical motion constraints. The purpose of this study was to investigate an alternative treatment scenario using a moving average algorithm. The algorithm, using a suitable averaging period, accounts for variations in the average tumor position, but respiratory induced target position variations about this average are ignored during delivery and can be treated as a random error during planning. In order to test the method a comparison between five different treatment techniques was performed: (1) moving average algorithm, (2) real-time motion tracking, (3) respiration motion gating (at both inhale and exhale), (4) moving average gating (at both inhale and exhale) and (5) static beam delivery. Two data sets were used for the purpose of this analysis: (a) external respiratory-motion traces using different coaching techniques included 331 respiration motion traces from 24 lung-cancer patients acquired using three different breathing types [free breathing (FB), audio coaching (A) and audio-visual biofeedback (AV)]; (b) 3D tumor motion included implanted fiducial motion data for over 160 treatment fractions for 46 thoracic and abdominal cancer patients obtained from the Cyberknife Synchrony. The metrics used for comparison were the group systematic error (M), the standard deviation (SD) of the systematic error (sigma) and the root mean square of the random error (sigma). Margins were calculated using the formula by Stroom et al. [Int. J. Radiat. Oncol., Biol., Phys. 43(4), 905-919 (1999)]: 2sigma + 0.7sigma. The resultant calculations for implanted fiducial motion traces (all values in cm) show that M and sigma are negligible for moving average algorithm, moving average gating, and real-time tracking (i.e., M and sigma = 0 cm) compared to static beam (M = 0.02 cm and sigma = 0.16 cm) or gated beam delivery (M = -0.05 and 0.16 cm at both exhale and inhale, respectively, and sigma = 0.17 and 0.26 cm at both exhale and inhale, respectively). Moving average algorithm sigma = 0.22 cm has a slightly lower random error than static beam delivery sigma = 0.24 cm, though gating, moving average gating, and real-time tracking have much lower random error values for implanted fiducial motion. Similar trends were also observed for the results using the external respiratory motion data. Moving average algorithm delivery significantly reduces M and sigma compared with static beam delivery. The moving average algorithm removes the nonstationary part of the respiration motion which is also achieved by AV, and thus the addition of the moving average algorithm shows little improvement with AV. Overall, a moving average algorithm shows margin reduction compared with gating and static beam delivery, and may have some mechanical advantages over real-time tracking when the beam is aligned with the target and patient compliance advantages over real-time tracking when the target is aligned to the beam.

Experimental investigation of a moving averaging algorithm for motion perpendicular to the leaf travel direction in dynamic MLC target tracking

PURPOSE In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. METHODS The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a tgamma-test with a 3%/3 mm criterion. RESULTS The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the gamma-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation. CONCLUSIONS The delivery efficiency of moving average tracking was up to four times higher than that of real-time tracking and approached the efficiency of no compensation for all cases. The geometric accuracy and dosimetric accuracy of the moving average algorithm was between real-time tracking and no compensation, approximately half the percentage of dosimetric points failing the gamma-test compared with no compensation.

IMRT treatment planning on 4D geometries for the era of dynamic MLC tracking.

The problem addressed here was to obtain optimal and deliverable dynamic multileaf collimator (MLC) leaf sequences from four-dimensional (4D) geometries for dynamic MLC tracking delivery. The envisaged scenario was where respiratory phase and position information of the target was available during treatment, from which the optimal treatment plan could be further adapted in real time. A tool for 4D treatment plan optimization was developed that integrates a commercially available treatment planning system and a general-purpose optimization system. The 4D planning method was applied to the 4D computed tomography planning scans of three lung cancer patients. The optimization variables were MLC leaf positions as a function of monitor units and respiratory phase. The objective function was the deformable dose-summed 4D treatment plan score. MLC leaf motion was constrained by the maximum leaf velocity between control points in terms of monitor units for tumor motion parallel to the leaf travel direction and between phases for tumor motion parallel to the leaf travel direction. For comparison and a starting point for the 4D optimization, three-dimensional (3D) optimization was performed on each of the phases. The output of the 4D IMRT planning process is a leaf sequence which is a function of both monitor unit and phase, which can be delivered to a patient whose breathing may vary between the imaging and treatment sessions. The 4D treatment plan score improved during 4D optimization by 34%, 4%, and 50% for Patients A, B, and C, respectively, indicating 4D optimization generated a better 4D treatment plan than the deformable sum of individually optimized phase plans. The dose-volume histograms for each phase remained similar, indicating robustness of the 4D treatment plan to respiratory variations expected during treatment delivery. In summary, 4D optimization for respiratory phase-dependent treatment planning with dynamic MLC motion tracking improved the 4D treatment plan score by 4–50% compared with 3D optimization. The 4D treatment plans had leaf sequences that varied from phase to phase to account for anatomic motion, but showed similar target dose distributions in each phase. The current method could in principle be generalized for use in offline replanning between fractions or for online 4D treatment planning based on 4D cone-beam CT images. Computation time remains a challenge.