Dataset Information

Estimation of patient setup uncertainty using BrainLAB Exatrac X-Ray 6D system in image-guided radiotherapy.

ABSTRACT: The purpose of this study was to evaluate setup uncertainties for brain sites with ExacTrac X-Ray 6D system and to provide optimal margin guidelines. Fifteen patients with brain tumor were included in this study. Two X-ray images with ExacTrac X-Ray 6D system were used to verify patient position and tumor target localization before each treatment. The 6D fusion software first generates various sets of DRRs with position variations in both three translational and three rotational directions (six degrees of freedom) for the CT images. Setup variations (translation and rotation) after correction were recorded and corrected before treatment. The 3D deviations are expressed as mean ± standard deviation. The random error (?(?i)), systematic error (?i), and group systematic error (M(?i)) for the different X-ray were calculated using the definitions of van Herk.(1) Mean setup errors were calculated from X-ray images acquired after all fractions. There is moderate patient-to-patient variation in the vertical direction and small variations in systematic errors and magnitudes of random errors are smaller. The global systematic errors were measured to be less than 2.0 mm in each direction. Random component of all patients are smaller ranging from 0.1-0.3 mm small. The safety margin (SM) to the lateral, is 0.5 mm and 2.6 mm for van Herk(1) and Stroom et al.,(2) respectively, craniocaudal axis is 1.5 mm and 3.4 mm, respectively, and with respect to the antero-posterior axis, 2.3 mm and 3.9 mm. Daily X-ray imaging is essential to compare and assess the accuracy of treatment delivery to different anatomical locations.

SUBMITTER: Infusino E

PROVIDER: S-EPMC5690103 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Estimation of patient setup uncertainty using BrainLAB Exatrac X-Ray 6D system in image-guided radiotherapy.

Infusino Erminia E Trodella Lucio L Ramella Sara S D'Angelillo Rolando M RM Greco Carlo C Iurato Aurelia A Trodella Luca E LE Nacca Alessandro A Cornacchione Patrizia P Mameli Alessandra A

Journal of applied clinical medical physics 20150308 2

The purpose of this study was to evaluate setup uncertainties for brain sites with ExacTrac X-Ray 6D system and to provide optimal margin guidelines. Fifteen patients with brain tumor were included in this study. Two X-ray images with ExacTrac X-Ray 6D system were used to verify patient position and tumor target localization before each treatment. The 6D fusion software first generates various sets of DRRs with position variations in both three translational and three rotational directions (six ...[more]

PMID: 26103179

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Project description:Multi-modality image-guided radiotherapy is the standard of care in contemporary cancer management; however, it is not common in preclinical settings due to both hardware and software limitations. Soft tissue lesions, such as orthotopic prostate tumors, are difficult to identify using cone beam computed tomography (CBCT) imaging alone. In this study, we characterized a research magnetic resonance (MR) scanner for preclinical studies and created a protocol for combined MR-CBCT image-guided small animal radiotherapy. Two in-house dual-modality, MR and CBCT compatible, phantoms were designed and manufactured using 3D printing technology. The phantoms were used for quality assurance tests and to facilitate end-to-end testing for combined preclinical MR and CBCT based treatment planning. MR and CBCT images of the phantoms were acquired utilizing a Varian 4.7 T scanner and XRad-225Cx irradiator, respectively. The geometry distortion was assessed by comparing MR images to phantom blueprints and CBCT. The corrected MR scans were co-registered with CBCT and subsequently used for treatment planning. The fidelity of 3D printed phantoms compared to the blueprint design yielded favorable agreement as verified with the CBCT measurements. The geometric distortion, which varied between -5% and 11% throughout the scanning volume, was substantially reduced to within 0.4% after correction. The distortion free MR images were co-registered with the corresponding CBCT images and imported into a commercial treatment planning software SmART Plan. The planning target volume (PTV) was on average 19% smaller when contoured on the corrected MR-CBCT images relative to raw images without distortion correction. An MR-CBCT based preclinical workflow was successfully designed and implemented for small animal radiotherapy. Combined MR-CBCT image-guided radiotherapy for preclinical research potentially delivers enhanced relevance to human radiotherapy for various disease sites. This novel protocol is wide-ranging and not limited to the orthotopic prostate tumor study presented in the study.

Project description:Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting unsuccessful registration solutions. The RQE is not patient-specific, so a single RQE can be constructed and used for a particular application (e.g., the registration for images acquired on the same anatomical site). Implementation of the RQE in a 2D/3D registration system is clinically feasible.

Project description:Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lack reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients, respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure of less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on the Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability of segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p = 0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty, to effect ratio, only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with a log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain in its predictions for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation, without considering segmentation, uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing).

Project description:BackgroundWe reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artificial differences in co-occurrence probability (CoOCP) in training datasets, that is, focusing CNN attention on soft tissues while ignoring bones. However, the effectiveness of this attention-based data augmentation has not been confirmed by explainable techniques. Furthermore, compared to reasonable ground truths, the feasibility of tumor segmentation in clinical kilovolt (kV) X-ray fluoroscopic (XF) images has not been confirmed.PurposeThe first aim of this paper was to present evidence that the proposed method provides an explanation and control of DL behavior. The second purpose was to validate the real-time lung tumor segmentation in clinical kV XF images for IGRT.MethodsThis retrospective study included 10 patients with lung cancer. Patient-specific and XF angle-specific image pairs comprising digitally reconstructed radiographs (DRRs) and projected-clinical-target-volume (pCTV) images were calculated from four-dimensional computer tomographic data and treatment planning information. The training datasets were primarily augmented by random overlay (RO) and noise injection (NI): RO aims to differentiate positional CoOCP in soft tissues and bones, and NI aims to make a difference in the frequency of occurrence of local and global image features. The CNNs for each patient-and-angle were automatically optimized in the DL training stage to transform the training DRRs into pCTV images. In the inference stage, the trained CNNs transformed the test XF images into pCTV images, thus identifying target positions and shapes.ResultsThe visual analysis of DL attention heatmaps for a test image demonstrated that our method focused CNN attention on soft tissue and global image features rather than bones and local features. The processing time for each patient-and-angle-specific dataset in the training stage was ∼30 min, whereas that in the inference stage was 8 ms/frame. The estimated three-dimensional 95 percentile tracking error, Jaccard index, and Hausdorff distance for 10 patients were 1.3-3.9 mm, 0.85-0.94, and 0.6-4.9 mm, respectively.ConclusionsThe proposed attention-based data augmentation with both RO and NI made the CNN behavior more explainable and more controllable. The results obtained demonstrated the feasibility of real-time markerless lung tumor segmentation in kV XF images for IGRT.

Project description:BackgroundInterfractional organ position variation might differ for abdominal organs and this could have consequences for defining safety margins. Therefore, the purpose of this study is to quantify interfractional position variations of abdominal organs in children in order to investigate possible correlations between abdominal organs and determine whether position variation is location-dependent.MethodsFor 20 children (2.2-17.8 years), we retrospectively analyzed 113 CBCTs acquired during the treatment course, which were registered to the reference CT to assess interfractional position variation of the liver, spleen, kidneys, and both diaphragm domes. Organ position variation was assessed in three orthogonal directions and relative to the bony anatomy. Diaphragm dome position variation was assessed in the cranial-caudal (CC) direction only. We investigated possible correlations between position variations of the organs (Spearman's correlation test, ?), and tested if organ position variations in the CC direction are related to the diaphragm dome position variations (linear regression analysis, R2) (both tests: significance level p <?0.05). Differences of variations of systematic (?) and random errors (?) between organs were tested (Bonferroni significance level p <?0.004).ResultsIn all directions, correlations between liver and spleen position variations, and between right and left kidney position variations were weak (????0.43). In the CC direction, the position variations of the right and left diaphragm domes were significantly, and stronger, correlated with position variations of the liver (R2 =?0.55) and spleen (R2 =?0.63), respectively, compared to the right (R2 =?0.00) and left kidney (R2 =?0.25). Differences in ? and ? between all organs were small and insignificant.ConclusionsNo (strong) correlations between interfractional position variations of abdominal organs in children were observed. From present results, we concluded that diaphragm dome position variations could be more representative for superiorly located abdominal (liver, spleen) organ position variations than for inferiorly located (kidneys) organ position variations. Differences of systematic and random errors between abdominal organs were small, suggesting that for margin definitions, there was insufficient evidence of a dependence of organ position variation on anatomical location.

Dataset Information

Estimation of patient setup uncertainty using BrainLAB Exatrac X-Ray 6D system in image-guided radiotherapy.

Publications

Estimation of patient setup uncertainty using BrainLAB Exatrac X-Ray 6D system in image-guided radiotherapy.

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