Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study

OBJECTIVES

The respiratory variations will lead to inconsistency between the actual delivery dose and the planning dose. How the minor interfractional amplitude changes affect the geometry and dose delivery accuracy remains to be investigated in the context of lung adaptive radiotherapy.

METHODS

Planning 4-dimensional-computed tomography and kV-cone beam computed tomography were scanned based on the Computerized Imaging Reference Systems phantom, which was employed to simulate the minor interfractional amplitude variations. The corresponding synthetic computed tomography for a particular motion pattern can be generated from Velocity program. Then a clinically meaningful synthetic computed tomography was analyzed through the geometrical and dosimetric assessment.

RESULTS

The image quality of synthetic computed tomography was improved obviously compared with cone beam computed tomography. Mean absolute error was minimized when no significant interfractional motion occurs and Velocity can be qualified for dealing with the regular breathing motion patterns. The mean percent hounsfield unit difference of the synthetic hounsfield unit values per organ relative to the planning 4-dimensional-computed tomography image was 22.3%. Under the same conditions, the mean percent hounsfield unit difference of the cone beam computed tomography hounsfield unit values per organ, relative to the planning 4-dimensional-computed tomography image was 83.9%. Overall, the accuracy of hounsfield unit in synthetic computed tomography was improved obviously and the variability of the synthetic image correlates with the planning 4-dimensional-computed tomography image variability. Meanwhile, the dose-volume histograms between planning 4-dimensional-computed tomography and synthetic computed tomography almost coincided each other, which indicates that Velocity program can qualify lung adaptive radiotherapy well when there were no interfractional respiratory variations. However, for cases with obvious interfractional amplitude change, the volume covered at least by 100% of the prescription dose was only 59.6% for that synthetic image.

CONCLUSION

The synthetic computed tomography images generated from Velocity were close to the real images in anatomy and dosimetry, which can make clinical lung adaptive radiotherapy possible based on the actual patient anatomy during treatment.

Basic evaluation of a novel 4D target and human body phantom

Stereotactic body radiation therapy (SBRT) is usually verified with a dynamic phantom or solid phantom, but there is a demand for phantoms that can accurately simulate tumor dynamics within an individual that would allow customized validation in every patient. We developed a new 4D dynamic target phantom (multi-cell 4D phantom) that allows simulation of tumor movement in patients. The basic quality and dynamic reproducibility of this new phantom was verified in this investigation. The newly developed multi-cell 4D phantom comprises four main components: soft tissue, bones, lungs, and tumor (target). The phantom structure was based on computed tomography (CT) data of a male. In this study, we investigated the basic performance of a multi-cell 4D phantom. All the CT numbers of the phantom were very close to those of human data. The geometric maximum amplitudes were 4.57 mm in the lateral direction, 4.59 mm in the ventrodorsal direction, and 3.68 mm in the cranio-caudal direction. Geometric errors were 0.84, 0.58, and 0.40 mm, respectively. Movements of the abdominal surface were stable for 60 s. Repeated measurements show no actual differences in target movements between multiple measurements and indicated high reproducibility (r  >  0.97). End-to-end tests using Gafchromic film revealed a gamma pass rate of 98% or above (2 mm/3%). Although our phantom performed limited reproducibility in the movement of the patient tumor at present, a satisfactory level of precision was confirmed in general. This is a very promising device for use in the verification of radiation therapy for moving targets.

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.