Dataset Information

Intrafractional motion in stereotactic body radiotherapy of spinal metastases utilizing cone beam computed tomography image guidance.

ABSTRACT:

Background and purpose

Spine stereotactic body radiotherapy (SBRT) requires a high degree of accuracy due to steep dose gradients close to the spinal cord. This study aimed to (1) evaluate intrafractional motion in spine SBRT utilizing flattening filter free (FFF) beam delivery and cone beam computed tomography (CBCT) image guidance and (2) evaluate if adding another CBCT acquisition and corrections prior to treatment improves the overall position accuracy.

Materials and methods

Intrafractional motion was retrospectively analyzed for 78 fractions in 54 patients. All patients were immobilized with an evacuated cushion. Before treatment, a CBCT was acquired, a bony fusion with the planning CT was performed and translational and rotational errors were corrected. For 30 of the patients (39 fractions) acquisition of another CBCT and corrections were performed before treatment. A post treatment CBCT was acquired for all patients, and translational and rotational errors measured by fusion of the post treatment CBCT with the planning CT were recorded to calculate means and standard deviations (SDs).

Results

The positional errors were significantly smaller in 4 out of 6 error values in the patient group treated with verification CBCT. In this group, translational and rotational SDs ranged from 0.5 to 0.6?mm and 0.3°, respectively. Corresponding values in the group treated without verification CBCT were 0.7-1.0?mm and 0.4-0.7°.

Conclusion

With proper CBCT image guidance, patient immobilization and FFF-beam delivery, one can obtain very high patient position accuracy in spine SBRT. Inclusion of a verification CBCT prior to treatment increases the overall position accuracy.

SUBMITTER: Graadal Svestad J

PROVIDER: S-EPMC7807636 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Intrafractional motion in stereotactic body radiotherapy of spinal metastases utilizing cone beam computed tomography image guidance.

Graadal Svestad Jørund J Ramberg Christina C Skar Birgitte B Paulsen Hellebust Taran T

Physics and imaging in radiation oncology 20191001

<h4>Background and purpose</h4>Spine stereotactic body radiotherapy (SBRT) requires a high degree of accuracy due to steep dose gradients close to the spinal cord. This study aimed to (1) evaluate intrafractional motion in spine SBRT utilizing flattening filter free (FFF) beam delivery and cone beam computed tomography (CBCT) image guidance and (2) evaluate if adding another CBCT acquisition and corrections prior to treatment improves the overall position accuracy.<h4>Materials and methods</h4>I ...[more]

PMID: 33458287

Similar Datasets

Project description:BackgroundMotion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.PurposeThis study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.MethodsA 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).ResultsThe trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.ConclusionsCNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Project description:PurposeIn our conventional image registration workflow, the four-dimensional (4D) CBCT was directly registered to the reference helical CT (HCT) using a dual registration approach within the Elekta XVI software. In this study, we proposed a new HCT-CBCT auto-registration strategy using a previously registered CBCT (CBCTpre) as the reference image and tested its clinical feasibility.MethodsFrom a previous CBCT session, the registered average 4D CBCT was selected as CBCTpre and the HCT-CBCTpre registration vector from the clinician's manual registration result was recorded. In the new CBCT session, auto-registration was performed between the new average 4D CBCT (CBCTtx) and CBCTpre (CBCTpre-CBCTtx). The overall HCT-CBCTtx registration result was then derived by combing the results from two registrations (i.e., HCT-CBCTpre + CBCTpre-CBCTtx). The results from the proposed method were compared with clinician's manually adjusted HCT-CBCTtx registration results ("ground truth") to evaluate its accuracy using a test dataset consisting of 32 challenging registration cases.ResultsThe uncertainty of the proposed auto-registration method was -0.1 ± 0.5, 0.1 ± 1.0, and -0.1 ± 0.7 mm in three translational directions (lateral, longitudinal, and vertical) and 0.0° ± 0.9°, 0.3° ± 0.9°, and 0.4° ± 0.7° in three rotation directions, respectively. Two patients (6.3%) had translational uncertainty > 2 mm (max = 3.1 mm) and both occurred in the longitudinal direction. Meanwhile, the uncertainty of the conventional direct HCT-CBCTtx auto-registration was -0.4 ± 2.6, -0.2 ± 7.4, -1.4 ± 3.6 mm for translations and -0.3° ± 1.2°, 0.0° ± 1.6°, and 0.1 ± 1.1° for rotations. Eleven patients (34.4%) had translation uncertainty > 2 mm (max = 26.2 mm) in at least one direction. Accuracy in translation was improved with the new method, while rotation accuracy stayed in the same order.ConclusionWe demonstrated the feasibility of incorporating prior clinical registration knowledge into the online HCT-CBCT registration process. The proposed auto-registration method provides a quick and reliable starting solution for online HCT-CBCT registration.

Project description:Real-time surgical navigation relies on accurate image-to-world registration to align the coordinate systems of the image and patient. Conventional manual registration can present a workflow bottleneck and is prone to manual error and intraoperator variability. This work reports alternative means of automatic image-to-world registration, each method involving an automatic registration marker (ARM) used in conjunction with C-arm cone-beam CT (CBCT). The first involves a Known-Model registration method in which the ARM is a predefined tool, and the second is a Free-Form method in which the ARM is freely configurable.Studies were performed using a prototype C-arm for CBCT and a surgical tracking system. A simple ARM was designed with markers comprising a tungsten sphere within infrared reflectors to permit detection of markers in both x-ray projections and by an infrared tracker. The Known-Model method exercised a predefined specification of the ARM in combination with 3D-2D registration to estimate the transformation that yields the optimal match between forward projection of the ARM and the measured projection images. The Free-Form method localizes markers individually in projection data by a robust Hough transform approach extended from previous work, backprojected to 3D image coordinates based on C-arm geometric calibration. Image-domain point sets were transformed to world coordinates by rigid-body point-based registration. The robustness and registration accuracy of each method was tested in comparison to manual registration across a range of body sites (head, thorax, and abdomen) of interest in CBCT-guided surgery, including cases with interventional tools in the radiographic scene.The automatic methods exhibited similar target registration error (TRE) and were comparable or superior to manual registration for placement of the ARM within ∼200 mm of C-arm isocenter. Marker localization in projection data was robust across all anatomical sites, including challenging scenarios involving the presence of interventional tools. The reprojection error of marker localization was independent of the distance of the ARM from isocenter, and the overall TRE was dominated by the configuration of individual fiducials and distance from the target as predicted by theory. The median TRE increased with greater ARM-to-isocenter distance (e.g., for the Free-Form method, TRE increasing from 0.78 mm to 2.04 mm at distances of ∼75 mm and 370 mm, respectively). The median TRE within ∼200 mm distance was consistently lower than that of the manual method (TRE = 0.82 mm). Registration performance was independent of anatomical site (head, thorax, and abdomen). The Free-Form method demonstrated a statistically significant improvement (p = 0.0044) in reproducibility compared to manual registration (0.22 mm versus 0.30 mm, respectively).Automatic image-to-world registration methods demonstrate the potential for improved accuracy, reproducibility, and workflow in CBCT-guided procedures. A Free-Form method was shown to exhibit robustness against anatomical site, with comparable or improved TRE compared to manual registration. It was also comparable or superior in performance to a Known-Model method in which the ARM configuration is specified as a predefined tool, thereby allowing configuration of fiducials on the fly or attachment to the patient.

Dataset Information

Intrafractional motion in stereotactic body radiotherapy of spinal metastases utilizing cone beam computed tomography image guidance.

Background and purpose

Materials and methods

Results

Conclusion

Publications

Intrafractional motion in stereotactic body radiotherapy of spinal metastases utilizing cone beam computed tomography image guidance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets