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Evaluation of the geometric accuracy of surrogate-based gated VMAT using intrafraction kilovoltage x-ray images.

ABSTRACT: To evaluate the geometric accuracy of beam targeting in external surrogate-based gated volumetric modulated arc therapy (VMAT) using kilovoltage (kV) x-ray images acquired during dose delivery.Gated VMAT treatments were delivered using a Varian TrueBeam STx Linac for both physical phantoms and patients. Multiple gold fiducial markers were implanted near the target. The reference position was created for each implanted marker, representing its correct position at the gating threshold. The gating signal was generated from the RPM system. During the treatment, kV images were acquired immediately before MV beam-on at every breathing cycle, using the on-board imaging system. All implanted markers were detected and their 3D positions were estimated using in-house developed software. The positioning error of a marker is defined as the distance of the marker from its reference position for each frame of the images. The overall error of the system is defined as the average over all markers. For the phantom study, both sinusoidal motion (1D and 3D) and real human respiratory motion was simulated for the target and surrogate. In the baseline case, the two motions were synchronized for the first treatment fraction. To assess the effects of surrogate-target correlation on the geometric accuracy, a phase shift of 5% and 10% between the two motions was introduced. For the patient study, intrafraction kV images of five stereotactic body radiotherapy (SBRT) patients were acquired for one or two fractions.For the phantom study, a high geometric accuracy was achieved in the baseline case (average error: 0.8 mm in the superior-inferior or SI direction). However, the treatment delivery is prone to geometric errors if changes in the target-surrogate relation occur during the treatment: the average error was increased to 2.3 and 4.7 mm for the phase shift of 5% and 10%, respectively. Results obtained with real human respiratory curves show a similar trend. For a target with 3D motion, the technique is able to detect geometric errors in the left-right (LR) and anterior-posterior (AP) directions. For the patient study, the average intrafraction positioning errors are 0.8, 0.9, and 1.4 mm and 95th percentile errors are 1.7, 2.1, and 2.7 mm in the LR, AP, and SI directions, respectively.The correlation between external surrogate and internal target motion is crucial to ensure the geometric accuracy of surrogate-based gating. Real-time guidance based on kV x-ray images overcomes the potential issues in surrogate-based gating and can achieve accurate beam targeting in gated VMAT.

SUBMITTER: Li R

PROVIDER: S-EPMC3344884 | biostudies-other | 2012 May

REPOSITORIES: biostudies-other

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Project description:To provide a comprehensive assessment of patient setup accuracy in 6 degrees of freedom (DOFs) using 2-dimensional/3-dimensional (2D/3D) image registration with on-board 2-dimensional kilovoltage (OB-2 DkV) radiographic images, we evaluated cranial, head and neck (HN), and thoracic and abdominal sites under clinical conditions. A fast 2D/3D image registration method using graphics processing unit GPU was modified for registration between OB-2 DkV and 3D simulation computed tomography (simCT) images, with 3D/3D registration as the gold standard for 6 DOF alignment. In 2D/3D registration, body roll rotation was obtained solely by matching orthogonal OB-2 DkV images with a series of digitally reconstructed radiographs (DRRs) from simCT with a small rotational increment along the gantry rotation axis. The window/level adjustments for optimal visualization of the bone in OB-2 DkV and DRRs were performed prior to registration. Ideal patient alignment at the isocenter was calculated and used as an initial registration position. In 3D/3D registration, cone-beam CT (CBCT) was aligned to simCT on bony structures using a bone density filter in 6DOF. Included in this retrospective study were 37 patients treated in 55 fractions with frameless stereotactic radiosurgery or stereotactic body radiotherapy for cranial and paraspinal cancer. A cranial phantom was used to serve as a control. In all cases, CBCT images were acquired for patient setup with subsequent OB-2 DkV verification. It was found that the accuracy of the 2D/3D registration was 0.0 ± 0.5 mm and 0.1° ± 0.4° in phantom. In patient, it is site dependent due to deformation of the anatomy: 0.2 ± 1.6 mm and -0.4° ± 1.2° on average for each dimension for the cranial site, 0.7 ± 1.6 mm and 0.3° ± 1.3° for HN, 0.7 ± 2.0 mm and -0.7° ± 1.1° for the thorax, and 1.1 ± 2.6 mm and -0.5° ± 1.9° for the abdomen. Anatomical deformation and presence of soft tissue in 2D/3D registration affect the consistency with 3D/3D registration in 6 DOF: the discrepancy increases in superior to inferior direction.

Project description:BackgroundDual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features.MethodsIn this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy.ResultsMaterial decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features.ConclusionsThe work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

Project description:PurposeThe objective of this work was to evaluate phantom dosimetry of a novel kilovoltage (kV) X-ray source, which employs a stationary tungsten anode and a linearly swept scanning electron beam. The source utilizes converging X-ray collimation along with orthogonal mechanical rotation to distribute surface flux over large area. In this study, this was investigated as a potential solution to fast-falloff limitations expected with kV radiotherapy. This was done with the aim of future clinical development of a lower cost radiotherapy alternative to megavoltage (MV) linac systems.MethodsRadiochromic film was employed for dosimetry on the kV X-ray source of the linear-converging radiotherapy system (LCRS). The source utilizes charge particle optics to magnetically deflect and focus an electron beam along a stationary, reflection tungsten target in an ultra-high-vacuum stainless-steel chamber. Resulting X-rays were collimated into converging beamlets that span a large planar angle and converge at the system isocenter. In this study, radiochromic film dosimetry was done at 140 and 145 kVp for a designated planning treatment volume (PTV) of 4 cm diameter. An acrylic phantom was employed for dose distribution measurements of stationary and rotational delivery. Film dosimetry was evaluated in planes parallel to the source X-ray window at various depths, as well as in the plane of gantry rotation.ResultsAt 140 and 145 kVp and using a collimated 4 cm square field at depth, lesion-to-skin dose ratio was shown to improve with additional beams from different relative source positions, where the different beams are focused at the same isocenter and do not overlap at the phantom surface. It was only possible to achieve a 1:1 Dmax -to-surface ratio with four delivery beams, but the ratio improved to 4:1 with 12 beams, focused at the same isocenter depth of 7.8 cm in an acrylic phantom. For the tests conducted, the following Dmax -to-surface ratios were obtained: 0.4:1 lesion-to-skin ratio for stationary delivery from one entry beam, 0.71:1 lesion-to-skin ratio was obtained for two beams, 1.07:1 ratio for four beams, and 4:1 for 12 beams. Dose-depth profiles were evaluated for stationary and rotational dosimetry. Additionally, rotational dosimetry was measured for a case more analogous to a clinical scenario, where the isocenter was located at an off-center simulated lesion.ConclusionsThe results demonstrate potential dose-depth improvements with kV arc therapy by distributing the surface flux with a wide converging beam along with perpendicular mechanical source rotation of the LCRS. The system delivered tolerable dose to a large surface area when a threshold of multiple, separated beams was reached. The radiochromic film data support the feasibility of the construct of the LCRS kV radiotherapy system design.

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Evaluation of the geometric accuracy of surrogate-based gated VMAT using intrafraction kilovoltage x-ray images.

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