A patient alignment solution for lung SBRT setups based on a deformable registration technique

Impact of scanning parameters and breathing patterns on image quality and accuracy of tumor motion reconstruction in 4D CBCT: a phantom study

Four-dimensional, cone-beam CT (4D CBCT) substantially reduces respiration-induced motion blurring artifacts in three-dimension (3D) CBCT. However, the image quality of 4D CBCT is significantly degraded which may affect its accuracy in localizing a mobile tumor for high-precision, image-guided radiation therapy (IGRT). The purpose of this study was to investigate the impact of scanning parameters hereinafter collectively referred to as scanning sequence) and breathing patterns on the image quality and the accuracy of computed tumor trajectory for a commercial 4D CBCT system, in preparation for its clinical implementation. We simulated a series of periodic and aperiodic sinusoidal breathing patterns with a respiratory motion phantom. The aperiodic pattern was created by varying the period or amplitude of individual sinusoidal breathing cycles. 4D CBCT scans of the phantom were acquired with a manufacturer-supplied scanning sequence (4D-S-slow) and two in-house modified scanning sequences (4D-M-slow and 4D-M-fast). While 4D-S-slow used small field of view (FOV), partial rotation (200°), and no imaging filter, 4D-M-slow and 4D-M-fast used medium FOV, full rotation, and the F1 filter. The scanning speed was doubled in 4D-M-fast (100°/min gantry rotation). The image quality of the 4D CBCT scans was evaluated using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and motion blurring ratio (MBR). The trajectory of the moving target was reconstructed by registering each phase of the 4D CBCT with a reference CT. The root-mean-squared-error (RMSE) analysis was used to quantify its accuracy. Significant decrease in CNR and SNR from 3D CBCT to 4D CBCT was observed. The 4D-S-slow and 4D-M-fast scans had comparable image quality, while the 4D-M-slow scans had better performance due to doubled projections. Both CNR and SNR decreased slightly as the breathing period increased, while no dependence on the amplitude was observed. The difference of both CNR and SNR between periodic and aperiodic breathing patterns was insignificant (p > 0.48). At end-exhale phases, the motion blurring was negligible for both periodic and aperiodic breathing patterns; at mid-inhale phase, the motion blurring increased as the period, the amplitude or the amount of cycle-to-cycle variation on amplitude increased. Overall, the accuracy of localizing the moving target in 4D CBCT was within 2 mm under all studied cases. No difference in the RMSEs was noticed among the three scanning sequences. The 4D-M-fast scans, free of volume truncation artifacts, exhibited comparable image quality and accuracy in tumor motion reconstruction as the 4D-S-slow scans with reduced imaging dose (0.60 cGy vs. 0.99 cGy) due to the use of faster gantry rotation and the F1 filter, suggesting its suitability for clinical use.

A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy

PURPOSE

Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient's body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model.

METHODS

The principle-component-analysis (PCA) method was employed as the framework to build the authors' statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the "best represented" columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4 DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution.

RESULTS

The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4 DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4 DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms.

CONCLUSIONS

The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.

Respiration-phase-matched digital tomosynthesis imaging for moving target verification: a feasibility study

PURPOSE

To develop a respiration-phase-matched digital tomosynthesis (DTS) technique to monitor moving targets, and to evaluate its accuracy for various imaging parameters and anatomical characteristics.

METHODS

Previously developed 3D-DTS techniques, registering onboard DTS (OB-DTS, reconstructed from onboard projections) to reference DTS (R-DTS, reconstructed from DRRs of 3D reference CT), are inadequate to monitor moving targets. The authors' proposed respiration-phase-matched DTS technique registers OB-DTS to R-DTS reconstructed from DRRs generated by the same phase images of 4D reference CT as the corresponding onboard projections. To evaluate the improved accuracy of the author's technique, the authors performed thoracic phantom studies using (1) simulation with the 4D digital extended-cardiac-torso (XCAT) phantom, and (2) experiments with an anthropomorphic motion phantom. The studies were performed for various: respiratory cycle (RC), scan angle, and fraction of RC contained therein. Also, the authors assessed the accuracy of their technique relative to target size/location, and respiration inconsistencies from the R-DTS to OB-DTS.

RESULTS

In both simulation and experimental studies, the respiration-phase-matched DTS technique is significantly more accurate in determining moving target positions. For 324 different scenarios simulated by XCAT, the respiration-phase-matched DTS technique localizes the 3D target position to errors of 1.07 ± 0.57 mm (mean ± S.D.), as compared to (a) 2.58 ± 1.37 and (b) 7.37 ± 4.18 mm, for 3D-DTS using 3D reference CT of (a) average intensity projection and (b) free-breathing CT. For 60 scenarios evaluated through experimental study, the uncertainties corresponding to those above are 1.24 ± 0.87, 2.42 ± 1.80, and 5.77 ± 6.45 mm, respectively. For a given scan angle, the accuracy of respiration-phase-matched DTS technique is less dependent on RC and the fraction of RC included in the scan. Increasing scan angle improves its accuracy. For different target locations, the targets near the chest wall or in the middle of lung provide higher registration accuracy compared to those near the mediastinum and diaphragm. Larger targets provide higher registration accuracy than small targets. Different respiratory cycle inconsistencies from R-DTS to OB-DTS minimally affect the registration accuracy. Increasing the respiratory amplitude inconsistencies will decrease the accuracy.

CONCLUSIONS

The respiration-phase-matched DTS is more accurate and robust in determining moving target positions than 3D-DTS. It has potential application in pretreatment setup, post-treatment analysis, and intrafractional target verification.