Search

Background: Delta radiomics is a high‐throughput computational technique used to describe quantitative changes in serial, time‐series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. Purpose: The purpose of this work was to show a proof‐of‐concept of a new radiomics paradigm for sparse, time‐series imaging data, where features are extracted from a spatial‐temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). Methods: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t=0$t = 0$ and t>0$t > 0$. These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker–Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial‐temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker–Planck estimation and simulated ground‐truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F‐FDG‐PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre‐treatment and 2‐weeks intra‐treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k‐means clustering and compared by Kaplan–Meier analyses with log‐rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. Results: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan–Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray‐Level‐Size‐Zone‐Matrix gray‐level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). Conclusions: We developed, verified, and demonstrated the prognostic value of a novel, physics‐based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations. [ABSTRACT FROM AUTHOR]

Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground‐truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U‐Net segmentation uncertainty quantification method based on spherical image projection of multi‐parametric MRI (MP‐MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection‐based U‐Net (SPU‐Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel‐wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU‐Net model was implemented on the basis of 369 glioma patients with MP‐MRI scans (T1, T1‐Ce, T2, and FLAIR). Three SPU‐Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU‐Net model was compared with (1) the classic U‐Net model with test‐time augmentation (TTA) and (2) linear scaling‐based U‐Net (LSU‐Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU‐Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U‐Net. Quantitatively, the SPU‐Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U‐Net with TTA and 0.743/0.702/0.876 with the LSU‐Net (scaling factor = 2). The SPU‐Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. Conclusion: The SPU‐Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image‐related deep‐learning applications for uncertainty evaluation. [ABSTRACT FROM AUTHOR]

Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi‐parametric MRI‐based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4‐modality multi‐parametric MRI protocol: T1, contrast‐enhanced T1 (T1‐Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The key MRI modalities with significant utilization by deep neural networks were identified based on ACC analysis. Segmentation results by deep neural networks using only the key MRI modalities were compared to those using all four MRI modalities in terms of Dice coefficient, accuracy, sensitivity, and specificity. Results: All Neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1‐Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U‐Net results using all four MRI modalities, the Dice coefficient of ET (0.784→0.775), TC (0.760→0.758), and WT (0.841→0.837) using the key modalities only had minimal differences without significance. Accuracy, sensitivity, and specificity results demonstrated the same patterns. Conclusion: The Neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image‐related deep‐learning applications. [ABSTRACT FROM AUTHOR]

Purpose: To develop a radiomics filtering technique for characterizing spatial‐encoded regional pulmonary ventilation information on lung computed tomography (CT). Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial‐encoded image information. Fifty‐three radiomic features were extracted within the kernel, resulting in a fourth‐order tensor object. As such, each voxel coordinate of the original lung was represented as a 53‐dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA‐SPECT) based on the Spearman correlation (ρ) analysis. Results: The radiomic feature maps GLRLM‐based Run‐Length Non‐Uniformity and GLCOM‐based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. Conclusions: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis‐generating data for future studies. [ABSTRACT FROM AUTHOR]

Background: Although four‐dimensional cone‐beam computed tomography (4D‐CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D‐CBCT scanning time while maintaining high‐quality images. Current motion‐compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling. Purpose: In this work, we aim to propose an alternative feature‐compensated method to realize the fast 4D‐CBCT with high‐quality images. Methods: We proposed a feature‐compensated deformable convolutional network (FeaCo‐DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo‐DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high‐quality images of the target phase. The proposed FeaCo‐DCN was evaluated using lung cancer patient data. Results: (1) FeaCo‐DCN generated high‐quality images with accurate and clear structures for a fast 4D‐CBCT scan; (2) 4D‐CBCT images reconstructed by FeaCo‐DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo‐DCN achieved superior performance by all metrics compared to the top‐ranked techniques in the AAPM SPARE Challenge. Conclusion: The proposed FeaCo‐DCN is effective and efficient in reconstructing 4D‐CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

Objective: Multiparametric magnetic resonance imaging (MRI) renders rich and complementary anatomical and functional information, which is often utilized separately. This study aimed to propose an adaptive multiparametric MRI (mpMRI) fusion method, and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients. Methods: An adaptive mpMRI fusion method was developed with five components: image pre‐processing, fusion algorithm, database, adaptation rules, and fused MRI. The linear‐weighted summation algorithm was used for fusion. Weight‐driven and feature‐driven adaptations were designed for different applications. A clinical‐friendly graphic user interface (G was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast, and enhancement of image signal and contrast were examined in patient cases. Tumor contrast‐to‐noise ratio (CNR) and liver signal‐to‐noise ratio (SNR) were evaluated and compared before and after mpMRI fusion. Results: The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft‐tissue boundary, vertebral body, tumor, and composition of multiple image features in one image, were achieved. Tumor CNR improved from –1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1‐weighted (T1‐w), from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2‐w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1‐w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for diffusion‐weighted MRI. The coefficient of variation of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1‐w, T2‐w, and T2/T1‐w MRI, respectively. Conclusion: A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features, such as tumor contrast and liver signal. Synthesis of novel image contrasts, including the composition of multiple image features into a single image set, was achieved. [ABSTRACT FROM AUTHOR]

Objective: Despite its prevalence, cone beam computed tomography (CBCT) has poor soft‐tissue contrast, making it challenging to localize liver tumors. We propose a patient‐specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. Methods: A key innovation is using patient‐specific CBCT‐MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT‐MRI images were augmented using translations and rotations to generate enough patient‐specific training data. A U‐Net‐based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground‐truth MRI. Results: The synthetic MRI demonstrated superb soft‐tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal‐to‐noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Conclusion: Our study demonstrated the feasibility of a patient‐specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID‐19 and non‐COVID‐19 pneumonia detection using chest x‐ray images. Methods: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x‐ray image; thus, each feature is rendered as a 2D map in the same dimension as the x‐ray image. Based on each of the three investigated deep neural network architectures, including VGG‐16, VGG‐19, and DenseNet‐121, a pilot model was trained using x‐ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross‐correlation analysis in reference to the pilot model saliency map results. The radiomics‐boosted model was then trained based on the same deep neural network architecture using x‐ray images plus the selected RFMs as input. The proposed radiomics‐boosted design was developed using 812 chest x‐ray images with 262/288/262 COVID‐19/non‐COVID‐19 pneumonia/healthy cases, and 649/163 cases were assigned as training‐validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training‐validation set. Sensitivity, specificity, accuracy, and ROC curves together with area‐under‐the‐curve (AUC) from all three deep neural network architectures were evaluated. Results: After radiomics‐boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID‐19 and healthy individual classifications. VGG‐16 showed the largest improvement in COVID‐19 classification ROC (AUC from 0.963 to 0.993), and DenseNet‐121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non‐COVID‐19 pneumonia classification task, radiomics‐boosted implementation of VGG‐16 (AUC from 0.918 to 0.969) and VGG‐19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet‐121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classifications were 0.973 (VGG‐19)/0.936 (VGG‐19)/ 0.933 (VGG‐16), respectively. Conclusions: The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID‐19/non‐COVID‐19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI) technique that expands single image contrast 4D‐MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. Methods and materials: The MC‐4D‐MRI technique integrates multi‐parametric MRI fusion, 4D‐MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre‐processing, fusion algorithm, adaptation, and fused multi‐contrast MRI as output. Four‐dimensional deformation vector fields (4D‐DVF) were generated from an original T2/T1‐w 4D‐MRI by deforming end‐of‐inhalation (EOI) to nine other phase volumes via DIR. The 4D‐DVF were applied to multi‐contrast MRI to generate a spectrum of 4D‐MRI in different image contrasts. The MC‐4D‐MRI technique was evaluated in five liver tumor patients on tumor contrast‐to‐noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D‐DIR introduced errors in tumor motion range, centroid location, extent, and volume. Results: MC‐4D‐MRI consisting of 4D‐MRIs in native image contrasts (T1‐w, T2‐w, and T2/T1‐w) and synthetic image contrasts, such as tumor‐enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1‐w MRI, to ‐4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1‐w, T2‐w, and TEC MRI, respectively. Patient ITV inter‐observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1‐w 4D‐MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1‐w, T2‐w, and TEC 4D‐MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D‐MRIs and original T2/T1‐w 4D‐MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase‐averaged absolute differences between the three 4D‐MRIs and the original 4D‐MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior‐inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior‐posterior (AP) direction, respectively. In the digital phantoms, phase‐averaged absolute tumor centroid shift caused by the 4D‐DIR were at or below 0.5 mm in SI, AP, and left‐right (LR) directions. Conclusion: We developed an MC‐4D‐MRI technique capable of expanding single image contrast 4D‐MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC‐4D‐MRI. The technique might offer new perspectives on the image contrast of MRI and 4D‐MRI in MR‐guided radiotherapy. [ABSTRACT FROM AUTHOR]

The charges on this task group (TG) were as follows: (a) provide specific procedural guidelines for performing the tests recommended in TG 142; (b) provide estimate of the range of time, appropriate personnel, and qualifications necessary to complete the tests in TG 142; and (c) provide sample daily, weekly, monthly, or annual quality assurance (QA) forms. Many of the guidelines in this report are drawn from the literature and are included in the references. When literature was not available, specific test methods reflect the experiences of the TG members (e.g., a test method for door interlock is self‐evident with no literature necessary). In other cases, the technology is so new that no literature for test methods was available. Given broad clinical adaptation of volumetric modulated arc therapy (VMAT), which is not a specific topic of TG 142, several tests and criteria specific to VMAT were added. [ABSTRACT FROM AUTHOR]

Background and purpose: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient‐specific anatomy. This study examined these two techniques for dose‐volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge‐based planning (KBP) technique and another first principle (FP) technique. Methods: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi‐institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. Results: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re‐optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ‐at‐risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. Conclusions: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the prognostic potential of intra‐treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence‐free survival (RFS). Materials and Methods: Sixty‐four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB‐approved study. Investigational 18F‐FDG‐PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven‐week course of therapy. Fifty‐five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan‐Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. Results: While pre‐treatment radiomics were not prognostic, intra‐treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ2 test) and RFS (HR = 7.53, 95% CI = 2.54–22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26–5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03–7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. Conclusions: Our data illustrate the prognostic value of intra‐treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow‐up times, and unsupervised clustering formalism that is less prone to hyper‐parameter tuning and over‐fitting compared to supervised learning. [ABSTRACT FROM AUTHOR]

Purpose: To develop an artificial intelligence (AI) agent for fully automated rapid head‐and‐neck intensity‐modulated radiation therapy (IMRT) plan generation without time‐consuming dose‐volume‐based inverse planning. Methods: This AI agent was trained via implementing a conditional generative adversarial network (cGAN) architecture. The generator, PyraNet, is a novel deep learning network that implements 28 classic ResNet blocks in pyramid‐like concatenations. The discriminator is a customized four‐layer DenseNet. The AI agent first generates multiple customized two‐dimensional projections at nine template beam angles from a patient's three‐dimensional computed tomography (CT) volume and structures. These projections are then stacked as four‐dimensional inputs of PyraNet, from which nine radiation fluence maps of the corresponding template beam angles are generated simultaneously. Finally, the predicted fluence maps are automatically postprocessed by Gaussian deconvolution operations and imported into a commercial treatment planning system (TPS) for plan integrity check and visualization. The AI agent was built and tested upon 231 oropharyngeal IMRT plans from a TPS plan library. 200/16/15 plans were assigned for training/validation/testing, respectively. Only the primary plans in the sequential boost regime were studied. All plans were normalized to 44 Gy prescription (2 Gy/fx). A customized Harr wavelet loss was adopted for fluence map comparison during the training of the PyraNet. For test cases, isodose distributions in AI plans and TPS plans were qualitatively evaluated for overall dose distributions. Key dosimetric metrics were compared by Wilcoxon signed‐rank tests with a significance level of 0.05. Results: All 15 AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable to those of the TPS plans. After PTV coverage normalization, Dmean of left parotid (DAI = 23.1 ± 2.4 Gy; DTPS = 23.1 ± 2.0 Gy), right parotid (DAI = 23.8 ± 3.0 Gy; DTPS = 23.9 ± 2.3 Gy), and oral cavity (DAI = 24.7 ± 6.0 Gy; DTPS = 23.9 ± 4.3 Gy) in the AI plans and the TPS plans were comparable without statistical significance. AI plans achieved comparable results for maximum dose at 0.01cc of brainstem (DAI = 15.0 ± 2.1 Gy; DTPS = 15.5 ± 2.7 Gy) and cord + 5mm (DAI = 27.5 ± 2.3 Gy; DTPS = 25.8 ± 1.9 Gy) without clinically relevant differences, but body Dmax results (DAI = 121.1 ± 3.9 Gy; DTPS = 109.0 ± 0.9 Gy) were higher than the TPS plan results. The AI agent needed ~3 s for predicting fluence maps of an IMRT plan. Conclusions: With rapid and fully automated execution, the developed AI agent can generate complex head‐and‐neck IMRT plans with acceptable dosimetry quality. This approach holds great potential for clinical applications in preplanning decision‐making and real‐time planning. [ABSTRACT FROM AUTHOR]

Purpose: To characterize key plan quality metrics in multi‐target stereotactic radiosurgery (SRS) plans treated using single‐isocenter volumetric modulated arc therapy (VMAT) in comparison to dynamic conformal arc (DCA) plans treating single target. To investigate the feasibility of quality improvement in VMAT planning based on previous planning knowledge. Materials and methods: 97 VMAT plans of multi‐target and 156 DCA plans of single‐target treated in 2017 at a single institution were reviewed. A total of 605 targets were treated with these SRS plans. The prescription dose was normalized to 20 Gy in all plans for this analysis. Two plan quality metrics, target conformity index (CI) and normal tissue volume receiving more than 12 Gy (V12Gy), were calculated for each target. The distribution of V12Gy per target was plotted as a function of the target volume. For multi‐target VMAT plans, the number of targets being treated in the same plan and the distance between targets were calculated to evaluate their impact on V12Gy. VMAT plans that had a large deviation of V12Gy from the average level were re‐optimized to determine the possibility of reducing the variation of V12Gy in VMAT planning. Results: Conformity index of multi‐target VMAT plans were lower than that of DCA plans while the mean values of 12 Gy were comparable. The V12Gy for a target in VMAT plan did not show apparent dependence on the total number of targets or the distance between targets. The distribution of V12Gy exhibited a larger variation in VMAT plans compared to DCA plans. Re‐optimization of outlier plans reduced V12 Gy by 33.9% and resulted in the V12Gy distribution in VMAT plans more closely resembling that of DCA plans. Conclusion: The benchmark data on key plan quality metrics were established for single‐isocenter multi‐target SRS planning. It is feasible to use this knowledge to guide VMAT planning and reduce high V12Gy outliers. [ABSTRACT FROM AUTHOR]

Purpose: This technical note aims to investigate the dosimetric impact of stray radiation on the Common Control Unit (CCU) of the IBA Blue Phantom2 and the measured beam data. Methods: Three CCUs of the same model were used for the study. The primary test CCU was placed at five distances from the radiation beam central axis. At each distance, a set of depth dose and beam profiles for two open and two wedge fields were measured. The field sizes were 10 × 10 cm2 and 30 × 30 cm2 for the open fields, and 30 × 30 cm2 and 15 × 15 cm2 for the 30° and 60° wedges, respectively. The other two CCUs were used to cross check the data of the primary CCU. Assuming the effect of stray radiation on the data measured at the farthest reachable distance 4.5 m is negligible, the dosimetric impact of stray radiation on the CCU and consequently on the measured data can be extracted for analysis by comparing it with those measured at shorter distances. Results: The results of three CCUs were consistent. The dosimetric impact of stray radiation was greater for lower energies at larger field sizes. For open fields, the data variation was up to 4.5% for depth dose curves and 7.1% for beam profiles. For wedge fields, the data variation was up to 9.3% for depth dose curves and 10.6% for beam profiles. Moreover, for wedge field profiles in the wedge direction, they became flatter as the CCU was placed closer to the primary radiation beam, manifesting smaller wedge angles. Conclusion: The stray radiation added a uniform background noise on all measured data. The magnitude of the noise is inversely proportional to the square of the distance of the CCU to the primary radiation beam, approximately following the inverse square law. [ABSTRACT FROM AUTHOR]

The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18F‐fluorodeoxyglucose ([18F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18F]FDG‐PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18F]FDG‐PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18F]FDG‐PET for RT. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop an accurate and reliable dose volume histogram (DVH) prediction method for external beam radiation therapy plans with multiple planning target volumes (PTVs). Materials and methods: We present a novel DVH prediction workflow, including new features and a modeling methodology, that makes better use of multiple PTVs: (a) We propose a generalized feature to characterize the geometric relationship of organ‐at‐risk (OARs) with respect to two or more PTVs with different prescribed dose levels; (b) We incorporate a novel data augmentation method to improve the data distribution in the feature space; (c) A similarity metric that leverages such information is subsequently used to select a subset of similar cases from the training dataset for model building; (d) Finally, a DVH prediction model is trained with these selected cases. To evaluate this new modeling workflow, we used 120 head and neck (HN) cases to tune the model, and used a separate dataset consisting of 148 cases for validation. The proposed model has been compared with the conventional knowledge‐based model in terms of model prediction accuracy, which was measured by the root mean squared error (RMSE) between the predicted DVHs and the actual clinical plan DVHs. Furthermore, 25 randomly selected plans were replanned guided by the proposed model and evaluated against clinical plans using clinical evaluation criteria. Results: The proposed modeling workflow significantly improved DVH prediction accuracy for brainstem (P < 0.001), cord (P < 0.001), larynx (P = 0.004), mandible (P < 0.001), oral cavity (P = 0.011), parotid (P < 0.001) and pharynx (P = 0.001). Cases replanned with the guidance of the proposed model spared OARs significantly better by clinical evaluation criteria. The replanned cases showed a 15% increase in the number of satisfied criteria, compared with clinical plans. Conclusions: The proposed modeling workflow generates DVH predictions with improved accuracy and robustness when multiple PTVs exist in a plan. It has demonstrated that the improvement in the DVH prediction model translates into better plan quality in knowledge‐based planning. [ABSTRACT FROM AUTHOR]

Purpose: MRI‐based treatment planning is a promising technique for liver stereotactic‐body radiation therapy (SBRT) treatment planning to improve target volume delineation and reduce radiation dose to normal tissues. MR geometric distortion, however, is a source of potential error in MRI‐based treatment planning. The aim of this study is to investigate dosimetric uncertainties caused by MRI geometric distortion in MRI‐based treatment planning for liver SBRT. Materials and Methods: The study was conducted using computer simulations. 3D MR geometric distortion was simulated using measured data in the literature. Planning MR images with distortions were generated by integrating the simulated 3D MR geometric distortion onto planning CT images. MRI‐based treatment plans were then generated on the planning MR images with two dose calculation methods: (1) using original CT numbers; and (2) using organ‐specific assigned CT numbers. Dosimetric uncertainties of various dose‐volume‐histogram parameters were determined as their differences between the simulated MRI‐based plans and the original clinical CT‐based plans for five liver SBRT cases. Results: The average simulated distortion for the five liver SBRT cases was 2.77 mm. In the case of using original CT numbers for dose calculation, the average dose uncertainties for target volumes and critical structures were <0.5 Gy, and the average target volume percentage at prescription dose uncertainties was 0.97%. In the case of using assigned CT numbers, the average dose uncertainties for target volumes and critical structures were <1.0 Gy, and the average target volume percentage at prescription dose uncertainties was 2.02%. Conclusions: Dosimetric uncertainties caused by MR geometric distortion in MRI‐based liver SBRT treatment planning was generally small (<1 Gy) when the distortion is 3 mm. [ABSTRACT FROM AUTHOR]

Background: Deformable image registration (DIR)‐based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR‐based lung ventilation. Method and Material: Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t‐MRI) and 3D proton MRI (p‐MRI) using balanced steady‐state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t‐MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR‐based DVFs from the p‐MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR‐based DVFs using the Jacobian method and then compared using cross correlation and mutual information. Results: The DIR‐based lung ventilations calculated using p‐MRI varied considerably from the reference lung ventilations based on t‐MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323). Conclusion: We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR‐based lung ventilation. [ABSTRACT FROM AUTHOR]

Purpose: On‐board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on‐board MRI in clinics is extremely limited. On the contrary, on‐board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on‐board kV imaging system and patient prior knowledge to generate on‐board four‐dimensional (4D)‐MRI for target localization in liver SBRT. Methods: Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRIprior) and all other phases. MRIprior was used to generate a synthetic CT at EOE phase (sCTprior). On‐board 4D MRI at each respiratory phase was considered a deformation of MRIprior. The deformation field map (DFM) was estimated by matching DRRs of the deformed sCTprior to on‐board kV projections using a motion modeling and free‐form deformation optimization algorithm. The on‐board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on‐board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results: In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 ± 1.31%/0.95 ± 0.01/0.88 ± 0.15 mm using orthogonal‐view 30° scan angles with 102 projections. The on‐board 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on‐board 4D MRI was generated successfully when compared to the “reference on‐board 4D MRI” for the liver patient case. Conclusions: A method was developed to generate on‐board 4D MRI using prior 4D MRI and on‐board limited kV projections. Preliminary results demonstrated the potential for MRI‐based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC. [ABSTRACT FROM AUTHOR]

Purpose: Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four‐dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. Method: We introduce a framework of fixed‐point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. Results: The NTDC‐adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis‐based predictions. Conclusion: Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to generate cine CT images (i.e., 4D images with high‐temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. Methods: In the proposed PCR method, the matrix factorization is utilized as an explicit low‐rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy‐based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real‐time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. Results: The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state‐of‐art method, that is, PICCS, using both simulation and experimental data with the on‐board cone‐beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. Conclusion: With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. [ABSTRACT FROM AUTHOR]

Purpose: Limited‐angle intrafraction verification (LIVE) has been previously developed for four‐dimensional (4D) intrafraction target verification either during arc delivery or between three‐dimensional (3D)/IMRT beams. Preliminary studies showed that LIVE can accurately estimate the target volume using kV/MV projections acquired over orthogonal view 30° scan angles. Currently, the LIVE imaging acquisition requires slow gantry rotation and is not clinically optimized. The goal of this study is to optimize the image acquisition parameters of LIVE for different patient respiratory periods and gantry rotation speeds for the effective clinical implementation of the system. Method: Limited‐angle intrafraction verification imaging acquisition was optimized using a digital anthropomorphic phantom (XCAT) with simulated respiratory periods varying from 3 s to 6 s and gantry rotation speeds varying from 1°/s to 6°/s. LIVE scanning time was optimized by minimizing the number of respiratory cycles needed for the four‐dimensional scan, and imaging dose was optimized by minimizing the number of kV and MV projections needed for four‐dimensional estimation. The estimation accuracy was evaluated by calculating both the center‐of‐mass‐shift (COMS) and three‐dimensional volume‐percentage‐difference (VPD) between the tumor in estimated images and the ground truth images. The robustness of LIVE was evaluated with varied respiratory patterns, tumor sizes, and tumor locations in XCAT simulation. A dynamic thoracic phantom (CIRS) was used to further validate the optimized imaging schemes from XCAT study with changes of respiratory patterns, tumor sizes, and imaging scanning directions. Results: Respiratory periods, gantry rotation speeds, number of respiratory cycles scanned and number of kV/MV projections acquired were all positively correlated with the estimation accuracy of LIVE. Faster gantry rotation speed or longer respiratory period allowed less respiratory cycles to be scanned and less kV/MV projections to be acquired to estimate the target volume accurately. Regarding the scanning time minimization, for patient respiratory periods of 3–4 s, gantry rotation speeds of 1°/s, 2°/s, 3–6°/s required scanning of five, four, and three respiratory cycles, respectively. For patient respiratory periods of 5–6 s, the corresponding respiratory cycles required in the scan changed to four, three, and two cycles, respectively. Regarding the imaging dose minimization, for patient respiratory periods of 3–4 s, gantry rotation speeds of 1°/s, 2–4°/s, 5–6°/s required acquiring of 7, 5, 4 kV and MV projections, respectively. For patient respiratory periods of 5–6 s, 5 kV and 5 MV projections are sufficient for all gantry rotation speeds. The optimized LIVE system was robust against breathing pattern, tumor size and tumor location changes. In the CIRS study, the optimized LIVE system achieved the average center‐of‐mass‐shift (COMS)/volume‐percentage‐difference (VPD) of 0.3 ± 0.1 mm/7.7 ± 2.0% for the scanning time priority case, 0.2 ± 0.1 mm/6.1 ± 1.2% for the imaging dose priority case, respectively, among all gantry rotation speeds tested. LIVE was robust against different scanning directions investigated. Conclusion: The LIVE system has been preliminarily optimized for different patient respiratory periods and treatment gantry rotation speeds using digital and physical phantoms. The optimized imaging parameters, including number of respiratory cycles scanned and kV/MV projection numbers acquired, provide guidelines for optimizing the scanning time and imaging dose of the LIVE system for its future evaluations and clinical implementations through patient studies. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases. We also aimed to develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers. Methods Four groups (G1-G4) of pelvic plans were sampled in this study. These include the following three groups of clinical IMRT cases: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases) and G3 (37 prostate bed cases). Cases in G4 were planned in accordance with dynamic-arc radiation therapy procedure and include 10 prostate cases in addition to those from G1. The workflow was separated into two parts: 1. identifying geometric outliers, assessing outlier impact, and outlier cleaning; 2. identifying dosimetric outliers, assessing outlier impact, and outlier cleaning. G2 and G3 were used to analyze the effects of geometric outliers (first experiment outlined below) while G1 and G4 were used to analyze the effects of dosimetric outliers (second experiment outlined below). A baseline model was trained by regarding all G2 cases as inliers. G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverages of inliers (G2) and outliers (G3). A receiver-operating-characteristic ( ROC) analysis was performed to determine the optimal threshold. The experiment was repeated by training the baseline model with all G3 cases as inliers and perturbing the model with G2 cases as outliers., A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outlier) was subsequently added to perturb the model. Predictions of dose-volume histograms ( DVHs) were made using these perturbed models for the remaining 5 G1 cases. A Weighted Sum of Absolute Residuals ( WSAR) was used to evaluate the impact of the dosimetric outliers., Results The leverage of inliers and outliers was significantly different. The Area-Under-Curve ( AUC) for differentiating G2 (outliers) from G3 (inliers) was 0.98 (threshold: 0.27) for the bladder and 0.81 (threshold: 0.11) for the rectum. For differentiating G3 (outlier) from G2 (inlier), the AUC (threshold) was 0.86 (0.11) for the bladder and 0.71 (0.11) for the rectum. Significant increase in WSAR was observed in the model with 3 dosimetric outliers for the bladder ( P < 0.005 with Bonferroni correction), and in the model with only 1 dosimetric outlier for the rectum ( P < 0.005). Conclusions We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for outlier detection. Results validated the necessity for outlier detection and clean-up to enhance model quality in clinical practice. [ABSTRACT FROM AUTHOR]

The American Association of Physicists in Medicine ( AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8,000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline., Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances., Approved by AAPM Professional Council 3-31-2017 and Executive Committee 4-4-2017. [ABSTRACT FROM AUTHOR]

Purpose To investigate the feasibility of using structural-based principal component analysis ( PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D- CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy. Methods A technique for lung 4D- CBCT reconstruction has been previously developed using a deformation field map ( DFM)-based strategy. In the previous method, each phase of the 4D- CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion model extracted by a global PCA and free-form deformation ( GMM- FD) technique, using a data fidelity constraint and deformation energy minimization. In this study, a new structural PCA method was developed to build a structural motion model ( SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4 DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation ( WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respiratory changes from planning 4D- CT to on-board volume to evaluate the method. The estimation accuracy was evaluated by the volume percent difference ( VPD)/center-of-mass-shift ( COMS) between lesions in the estimated and 'ground-truth' on-board 4D- CBCT. Different on-board projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. The method was also evaluated against three lung patients. Results The SMM- WFD method achieved substantially better accuracy than the GMM- FD method for CBCT estimation using extremely small scan angles or projections. Using orthogonal 15° scanning angles, the VPD/ COMS were 3.47 ± 2.94% and 0.23 ± 0.22 mm for SMM- WFD and 25.23 ± 19.01% and 2.58 ± 2.54 mm for GMM- FD among all eight XCAT scenarios. Compared to GMM- FD, SMM- WFD was more robust against reduction of the scanning angles down to orthogonal 10° with VPD/ COMS of 6.21 ± 5.61% and 0.39 ± 0.49 mm, and more robust against reduction of projection numbers down to only 8 projections in total for both orthogonal-view 30° and orthogonal-view 15° scan angles. SMM- WFD method was also more robust than the GMM- FD method against increasing levels of noise in the projection images. Additionally, the SMM- WFD technique provided better tumor estimation for all three lung patients compared to the GMM- FD technique. Conclusion Compared to the GMM- FD technique, the SMM- WFD technique can substantially improve the 4D- CBCT estimation accuracy using extremely small scan angles and low number of projections to provide fast low dose 4D target verification. [ABSTRACT FROM AUTHOR]

Purpose Diffusion-weighted Magnetic Resonance Imaging (DWI) has been shown to be a powerful tool for cancer detection with high tumor-to-tissue contrast. This study aims to investigate the feasibility of developing a four-dimensional DWI technique (4D-DWI) for imaging respiratory motion for radiation therapy applications. Materials/Methods Image acquisition was performed by repeatedly imaging a volume of interest (VOI) using an interleaved multislice single-shot echo-planar imaging (EPI) 2D-DWI sequence in the axial plane. Each 2D-DWI image was acquired with an intermediately low b-value (b = 500 s/mm2) and with diffusion-encoding gradients in x, y, and z diffusion directions. Respiratory motion was simultaneously recorded using a respiratory bellow, and the synchronized respiratory signal was used to retrospectively sort the 2D images to generate 4D-DWI. Cine MRI using steady-state free precession was also acquired as a motion reference. As a preliminary feasibility study, this technique was implemented on a 4D digital human phantom (XCAT) with a simulated pancreas tumor. The respiratory motion of the phantom was controlled by regular sinusoidal motion profile. 4D-DWI tumor motion trajectories were extracted and compared with the input breathing curve. The mean absolute amplitude differences (D) were calculated in superior-inferior (SI) direction and anterior-posterior (AP) direction. The technique was then evaluated on two healthy volunteers. Finally, the effects of 4D-DWI on apparent diffusion coefficient (ADC) measurements were investigated for hypothetical heterogeneous tumors via simulations. Results Tumor trajectories extracted from XCAT 4D-DWI were consistent with the input signal: the average D value was 1.9 mm (SI) and 0.4 mm (AP). The average D value was 2.6 mm (SI) and 1.7 mm (AP) for the two healthy volunteers. Conclusion A 4D-DWI technique has been developed and evaluated on digital phantom and human subjects. 4D-DWI can lead to more accurate respiratory motion measurement. This has a great potential to improve the visualization and delineation of cancer tumors for radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Methods: Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. Results: Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89±0.03, and single cycle sorting, DSC = 0.83±0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. Conclusions: In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management. [ABSTRACT FROM AUTHOR]

Purpose: Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods. Methods: Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance. Results: For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference. Conclusions: A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT. [ABSTRACT FROM AUTHOR]

Purpose: The recent development of multifocal stereotactic radiosurgery (SRS) using a single isocenter volumetric modulated arc theory (VMAT) technique warrants a re-examination of the quality assurance (QA) tolerances for routine mechanical QA recommended by the American Association of Physicists in Medicine Task Group Report Number 142. Multifocal SRS can result in targets with small volumes being at a large off-axis distance from the treatment isocenter. Consequently, angular errors in the collimator, patient support assembly (PSA), or gantry could have an increased impact on target coverage. Methods: The authors performed a retrospective analysis of dose deviations caused by systematic errors in PSA, collimator, and gantry angle at the tolerance level for routine linear accelerator QA as recommended by TG-142. Dosimetric deviations from multifocal SRS plans (N = 10) were compared to traditional single target SRS using dynamic conformal arcs (N = 10). The chief dosimetric quantities used in determining clinical impact were V100% and D99% of the individual planning target volumes and V12Gy of the healthy brain. Results: Induced errors at tolerance levels showed the greatest change in multifocal SRS target coverage for collimator rotations (±1.0?) with the average changes to V100% and D99% being 5% and 6%, respectively, with maximum changes of 33% and 20%. A reduction in the induced error to half the TG-142 tolerance (±0.5?) demonstrated similar changes in coverage loss to traditional single target SRS assessed at the recommended tolerance level. The observed change in coverage for multifocal SRS was reduced for gantry errors (±1.0?) at 2% and 4.5% for V100% and D99%, respectively, with maximum changes of 18% and 12%. Minimal change in coverage was noted for errors in PSA rotation. Conclusions: This study indicates that institutions utilizing a single isocenter VMAT technique for multifocal disease should pay careful attention to the angular mechanical tolerances in designing a robust and complete QA program. [ABSTRACT FROM AUTHOR]

Purpose To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies. Methods A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed to analytically solve for the PK parameters. Noise in the expression was minimized through low-pass filtering. Simulation studies were conducted in which the proposed method was compared with two existing methods in terms of accuracy and efficiency. Five in vivo brain studies were demonstrated for potential clinical application. Results In the simulation studies using chosen parameter values, the calculated percent difference of Ktrans by the proposed method was <5.0% with a temporal resolution (Δ t) < 5 s, and the accuracies of all parameter results were better or comparable to existing methods. When analyzed within certain parameter intensity ranges, the proposed method was more accurate than the existing methods and improved the efficiency by a factor of up to 458 for a Δ t = 1 s and up to 38 for a Δ t = 5 s. In the in vivo study, the calculated parameters using the proposed method were comparable to those using the existing methods with improved efficiencies. Conclusions An efficient method was developed for the accurate and efficient calculation of parameters in brain DCE-MRI permeability studies. Magn Reson Med 75:739-749, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration. Methods: A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential- and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes. Results: D increased faster for cine-mode (D = 1.17×Ir+0.23) than sequential-mode (D = 0.47 ×Ir+0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine- and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine- and sequential-modes, respectively, with a p-value < 0.001. Conclusions: Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Methods: Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the "gold-standard" on-board images (gold-standard doses). The absolute deviations of minimum dose (?Dmin), maximum dose (?Dmax), and mean dose (?Dmean), and the absolute deviations of prescription dose coverage (?V100%) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Results: Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ?Dmin, ?Dmax, ?Dmean, and ?V100% (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ?Dmin, ?Dmax, ?Dmean, and ?V100% of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). Conclusions: MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: To develop a framework for accurate electron Monte Carlo dose calculation. In this study, comprehensive validations of vendor provided electron beam phase space files for Varian TrueBeam Linacs against measurement data are presented. Methods:In this framework, the Monte Carlo generated phase space files were provided by the vendor and used as input to the downstream plan-specific simulations including jaws, electron applicators, and water phantom computed in the EGSnrc environment. The phase space files were generated based on open field commissioning data. A subset of electron energies of 6, 9, 12, 16, and 20 MeV and open and collimated field sizes 3 × 3, 4 × 4, 5 × 5, 6 × 6, 10 × 10, 15 × 15, 20 × 20, and 25 × 25 cm2 were evaluated. Measurements acquired with a CC13 cylindrical ionization chamber and electron diode detector and simulations from this framework were compared for a water phantom geometry. The evaluation metrics include percent depth dose, orthogonal and diagonal profiles at depths R100,R50,Rp, and Rp+ for standard and extended source-to-surface distances (SSD), as well as cone and cut-out output factors. Results:Agreement for the percent depth dose and orthogonal profiles between measurement and Monte Carlo was generally within 2% or 1 mm. The largest discrepancies were observed within depths of 5 mm from phantom surface. Differences in field size, penumbra, and flatness for the orthogonal profiles at depths R100, R50, and Rp were within 1 mm, 1 mm, and 2%, respectively. Orthogonal profiles at SSDs of 100 and 120 cm showed the same level of agreement. Cone and cut-out output factors agreed well with maximum differences within 2.5% for 6 MeV and 1% for all other energies. Cone output factors at extended SSDs of 105, 110, 115, and 120 cm exhibited similar levels of agreement. Conclusions:We have presented a Monte Carlo simulation framework for electron beam dose calculations for Varian TrueBeam Linacs. Electron beam energies of 6 to 20 MeV for open and collimated field sizes from 3 × 3 to 25 × 25 cm² were studied and results were compared to the measurement data with excellent agreement. Application of this framework can thus be used as the platform for treatment planning of dynamic electron arc radiotherapy and other advanced dynamic techniques with electron beams. [ABSTRACT FROM AUTHOR]

Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering. Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 x 256; number of k-space segments (NKS) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated "4D-MRI" acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients' respiratory profiles to study the relationships between data completeness (Cp) and a number of impacting factors: the number of repeated scans (NR), number of slices (NS), number of respiratory phase bins (NP), NKS, F, R, and initial respiratory phase at image acquisition (P0). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer. Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior-inferior and anterior-posterior directions, respectively). The relationship between Cp and NR was found best represented by an exponential function (CP = 100(1 - e-018NR), when NS = 30, NP = 6). At a CP value of 95%, the relative error in tumor volume was 0.66%, indicating that NR at a CP value of 95% (NR,95%) is sufficient. It was found that NR,95% is approximately linearly proportional to NP (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing. Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers. [ABSTRACT FROM AUTHOR]

Purpose: An important challenge facing online adaptive radiation therapy is the development of feasible and efficient quality assurance (QA). This project aimed to validate the deliverability of online adapted plans and develop a proof-of-concept online delivery monitoring system for online adaptive radiation therapy QA. Methods: The first part of this project benchmarked automatically online adapted prostate treatment plans using traditional portal dosimetry IMRT QA. The portal dosimetry QA results of online adapted plans were compared to original (unadapted) plans as well as randomly selected prostate IMRT plans from our clinic. In the second part, an online delivery monitoring system was designed and validated via a simulated treatment with intentional multileaf collimator (MLC) errors. This system was based on inputs from the dynamic machine information (DMI), which continuously reports actual MLC positions and machine monitor units (MUs) at intervals of 50 ms or less during delivery. Based on the DMI, the system performed two levels of monitoring/verification during the delivery: (1) dynamic monitoring of cumulative fluence errors resulting from leaf position deviations and visualization using fluence error maps (FEMs); and (2) verification of MLC positions against the treatment plan for potential errors in MLC motion and data transfer at each control point. Validation of the online delivery monitoring system was performed by introducing intentional systematic MLC errors (ranging from 0.5 to 2 mm) to the DMI files for both leaf banks. These DMI files were analyzed by the proposed system to evaluate the system's performance in quantifying errors and revealing the source of errors, as well as to understand patterns in the FEMs. In addition, FEMs from 210 actual prostate IMRT beams were analyzed using the proposed system to further validate its ability to catch and identify errors, as well as establish error magnitude baselines for prostate IMRT delivery. Results: Online adapted plans were found to have similar delivery accuracy in comparison to clinical IMRT plans when validated with portal dosimetry IMRT QA. FEMs for the simulated deliveries with intentional MLC errors exhibited distinct patterns for different MLC error magnitudes and directions, indicating that the proposed delivery monitoring system is highly specific in detecting the source of errors. Implementing the proposed QA system for online adapted plans revealed excellent delivery accuracy: over 99% of leaf position differences were within 0.5 mm, and >99% of pixels in the FEMs had fluence errors within 0.5 MU. Patterns present in the FEMs and MLC control point analysis for actual patient cases agreed with the error pattern analysis results, further validating the system's ability to reveal and differentiate MLC deviations. Calculation of the fluence map based on the DMI was performed within 2 ms after receiving each DMI input. Conclusions: The proposed online delivery monitoring system requires minimal additional resources and time commitment to the current clinical workflow while still maintaining high sensitivity to leaf position errors and specificity to error types. The presented online delivery monitoring system therefore represents a promising QA system candidate for online adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: To construct a robotic SPECT system and to demonstrate its capability to image a thorax phantom on a radiation therapy flat-top couch, as a step toward onboard functional and molecular imaging in radiation therapy. Methods: A robotic SPECT imaging system was constructed utilizing a gamma camera detector (Digirad 2020tc) and a robot (KUKA KR150 L110 robot). An imaging study was performed with a phantom (PET CT Phantom™), which includes five spheres of 10, 13, 17, 22, and 28 mm diameters. The phantom was placed on a flat-top couch. SPECT projections were acquired either with a parallel-hole collimator or a single-pinhole collimator, both without background in the phantom and with background at 1/10th the sphere activity concentration. The imaging trajectories of parallel-hole and pinhole collimated detectors spanned 180° and 228°, respectively. The pinhole detector viewed an off-centered spherical common volume which encompassed the 28 and 22 mm spheres. The common volume for parallel-hole system was centered at the phantom which encompassed all five spheres in the phantom. The maneuverability of the robotic system was tested by navigating the detector to trace the phantom and flat-top table while avoiding collision and maintaining the closest possible proximity to the common volume. The robot base and tool coordinates were used for image reconstruction. Results: The robotic SPECT system was able to maneuver parallel-hole and pinhole collimated SPECT detectors in close proximity to the phantom, minimizing impact of the flat-top couch on detector radius of rotation. Without background, all five spheres were visible in the reconstructed parallel-hole image, while four spheres, all except the smallest one, were visible in the reconstructed pinhole image. With background, three spheres of 17,22, and 28 mm diameters were readily observed with the parallel-hole imaging, and the targeted spheres (22 and 28 mm diameters) were readily observed in the pinhole region-of-interest imaging. Conclusions: Onboard SPECT could be achieved by a robot maneuvering a SPECT detector about patients in position for radiation therapy on a flat-top couch. The robot inherent coordinate frames could be an effective means to estimate detector pose for use in SPECT image reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: The authors have recently developed a novel 4D-MRI technique for imaging organ respiratory motion employing cine acquisition in the axial plane and using body area (BA) as a respiratory surrogate. A potential disadvantage associated with axial image acquisition is the space-dependent phase shift in the superior-inferior (SI) direction, i.e., different axial slice positions reach the respiratory peak at different respiratory phases. Since respiratory motion occurs mostly in the SI and anterior-posterior (AP) directions, sagittal image acquisition, which embeds motion information in these two directions, is expected to be more robust and less affected by phase-shift than axial image acquisition. This study aims to develop and evaluate a 4D-MRI technique using sagittal image acquisition. Methods: The authors evaluated axial BA and sagittal BA using both 4D-CT images (11 cancer patients) and cine MR images (6 healthy volunteers and 1 cancer patient) by comparing their corresponding space-dependent phase-shift in the SI direction (δSI SPS ) and in the lateral direction (δLAT SPS ), respectively. To evaluate sagittal BA 4D-MRI method, a motion phantom study and a digital phantom study were performed. Additionally, six patients who had cancer(s) in the liver were prospectively enrolled in this study. For each patient, multislice sagittal MR images were acquired for 4D-MRI reconstruction. 4D retrospective sorting was performed based on respiratory phases. Single-slice cine MRI was also acquired in the axial, coronal, and sagittal planes across the tumor center from which tumor motion trajectories in the SI, AP, and medial-lateral (ML) directions were extracted and used as references from comparison. All MR images were acquired in a 1.5 T scanner using a steady-state precession sequence (frame rate ~3 frames/s). Results: 4D-CT scans showed that δSI SPS was significantly greater than δLAT SPS (p-value: 0.012); the median phase-shift was 16.9% and 7.7%, respectively. Body surface motion measurement from axial and sagittal MR cines also showed δSI SPS was significantly greater than δLAT SPS . The median δSI SPS and δLAT SPS was 11.0% and 9.2% (p-value = 0.008), respectively. Tumor motion trajectories from 4D-MRI matched with those from single-slice cine MRI: the mean (±SD) absolute differences in tumor motion amplitude between the two were 1.5 ± 1.6 mm, 2.1 ± 1.9 mm, and 1.1 ± 1.0 mm in the SI, ML, and AP directions from this patient study. Conclusions: Space-dependent phase shift is less problematic for sagittal acquisition than for axial acquisition. 4D-MRI using sagittal acquisition was successfully carried out in patients with hepatic tumors. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of and challenges yet to be addressed to measure dose from low energy (effective energy <50 keV) brachytherapy sources (Pd-103, Cs-131, and I-125) using polyurethane based 3D dosimeters with optical CT. Methods: The authors' evaluation used the following sources: models 200 (Pd-103), CS-1 Rev2 (Cs- 131), and 6711 (I-125). The authors used the Monte Carlo radiation transport code MCNP5, simulations with the ScanSim optical tomography simulation software, and experimental measurements with PRESAGE ® dosimeters/optical CT to investigate the following: (1) the water equivalency of conventional (density = 1.065 g/cm³) and deformable (density = 1.02 g/cm³) formulations of polyurethane dosimeters, (2) the scatter conditions necessary to achieve accurate dosimetry for low energy photon seeds, (3) the change in photon energy spectrum within the dosimeter as a function of distance from the source in order to determine potential energy sensitivity effects, (4) the optimal delivered dose to balance optical transmission (per projection) with signal to noise ratio in the reconstructed dose distribution, and (5) the magnitude and characteristics of artifacts due to the presence of a channel in the dosimeter. Monte Carlo simulations were performed using both conventional and deformable dosimeter formulations. For verification, 2.8 Gy at 1 cm was delivered in 92 h using an I-125 source to a PRESAGE ® dosimeter with conventional formulation and a central channel with 0.0425 cm radius for source placement. The dose distribution was reconstructed with 0.02 and 0.04 cm³ voxel size using the Duke midsized optical CT scanner (DMOS). Results: While the conventional formulation overattenuates dose from all three sources compared to water, the current deformable formulation has nearly water equivalent attenuation properties for Cs-131 and I-125, while underattenuating for Pd-103. The energy spectrum of each source is relatively stable within the first 5 cm especially for I-125. The inherent assumption of radial symmetry in the TG43 geometry leads to a linear increase in sample points within the 3D dosimeter as a function of distance away from the source, which partially offsets the decreasing signal. Simulations of dose reconstruction using optical CT showed the feasibility of reconstructing dose out to a radius of 10 cm without saturating projection images using an optimal dose and high dynamic range scanning; the simulations also predicted that reconstruction artifacts at the channel surface due to a small discrepancy in refractive index should be negligible. Agreement of the measured with calculated radial dose function for I-125 was within 5% between 0.3 and 2.5 cm from the source, and the median difference of measured from calculated anisotropy function was within 5% between 0.3 and 2.0 cm from the source. Conclusions: 3D dosimetry using polyurethane dosimeters with optical CT looks to be a promising application to verify dosimetric distributions surrounding low energy brachytherapy sources. [ABSTRACT FROM AUTHOR]

Stereotactic body radiotherapy (SBRT) involves the treatment of extracranial primary tumors or metastases with a few, high doses of ionizing radiation. In SBRT, tumor kill is maximized and dose to surrounding tissue is minimized, by precise and accurate delivery of multiple radiation beams to the target. This is particularly challenging, because extracranial lesions often move with respiration and are irregular in shape, requiring careful treatment planning and continual management of this motion and patient position during irradiation. This review presents the rationale, process workflow, and technology for the safe and effective administration of SBRT, as well as the indications, outcome, and limitations for this technique in the treatment of lung cancer, liver cancer, and metastatic disease. Cancer 2014;120:942-954. © 2013 American Cancer Society. [ABSTRACT FROM AUTHOR]

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trained with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physicianˈs single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models ( p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy ( p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models ( p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases ( p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model. [ABSTRACT FROM AUTHOR]

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beamˈs eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments. [ABSTRACT FROM AUTHOR]

Purpose: Onboard imaging-currently performed primarily by x-ray transmission modalities-is essential in modern radiation therapy. As radiation therapy moves toward personalized medicine, molecular imaging, which views individual gene expression, may also be important onboard. Nuclear medicine methods, such as single photon emission computed tomography (SPECT), are premier modalities for molecular imaging. The purpose of this study is to investigate a robotic multipinhole approach to onboard SPECT. Methods: Computer-aided design (CAD) studies were performed to assess the feasibility of maneuvering a robotic SPECT system about a patient in position for radiation therapy. In order to obtain fast, high-quality SPECT images, a 49-pinhole SPECT camera was designed which provides high sensitivity to photons emitted from an imaging region of interest. This multipinhole system was investigated by computer-simulation studies. Seventeen hot spots 10 and 7 mm in diameter were placed in the breast region of a supine female phantom. Hot spot activity concentration was six times that of background. For the 49-pinhole camera and a reference, more conventional, broad field-of-view (FOV) SPECT system, projection data were computer simulated for 4-min scans and SPECT images were reconstructed. Hot-spot localization was evaluated using a nonprewhitening forced-choice numerical observer. Results: The CAD simulation studies found that robots could maneuver SPECT cameras about patients in position for radiation therapy. In the imaging studies, most hot spots were apparent in the 49-pinhole images. Average localization errors for 10-mm- and 7-mm-diameter hot spots were 0.4 and 1.7 mm, respectively, for the 49-pinhole system, and 3.1 and 5.7 mm, respectively, for the reference broad-FOV system. Conclusions: A robot could maneuver a multipinhole SPECT system about a patient in position for radiation therapy. The system could provide onboard functional and molecular imaging with 4-min scan times. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (∼2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning). Methods: Simulated treatments of 10 patients were reoptimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency. Results: All online reoptimized plans were finished within ∼2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased the monitor units by 17%. Differences of only 2cc or less in OAR V50%/V70Gy/V76Gy were observed between 100 and 50 iterations. Conclusions: It is feasible to perform automatic online reoptimization in ∼2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency, and time allowed for plan adaptation. [ABSTRACT FROM AUTHOR]

Purpose: Cone-beam x-ray imaging with flat panel detectors is used for target localization in image guided radiation therapy. This imaging includes cone-beam computed tomography (CBCT) and planar imaging. Use of two orthogonal x-ray systems could reduce imaging time for CBCT, provide simultaneous orthogonal views in planar imaging, facilitate dual-energy methods, and be useful in alleviating cone-beam artifacts by providing two axially offset focal-spot trajectories. However, the potential advantages of a second cone-beam system come at the cost of cross scatter, i.e., scatter of photons originating from one tube into the noncorresponding detector. Herein, cross scatter is characterized for dual cone-beam imaging, and a method for avoiding cross scatter is proposed and evaluated. Methods: A prototype dual-source CBCT system has been developed that models the geometry of a gantry-mounted kV imaging device used in radiation therapy. Cross scatter was characterized from 70 to 145 kVp in projections and reconstructed images using this system and three cylindrical phantoms (15, 20, and 30 cm) with a common Catphan core. A novel strategy for avoiding cross scatter in dual CBCT was developed that utilized interleaved data acquisition on each imaging chain. Interleaving, while maintaining similar angular sampling, can be achieved by either doubling the data acquisition rate or, as presented herein, halving the rotation speed. Results: The ratio of cross scatter to the total detected signal was found to be as high as 0.59 in a 30 cm diameter phantom. The measured scatter-to-primary ratio in some cases exceeded 4. In the 30 cm phantom, reconstructed contrast was reduced across all ROIs by an average of 48.7% when cross scatter was present. These cross-scatter degradations were almost entirely avoided by the method of interleaved exposures. Conclusions: Cross scatter is substantial in dual cone-beam imaging, but its effects can be largely removed by interleaved acquisition, which can be achieved at the same angular sampling rate either by doubling the data acquisition rate or halving the rotation speed. [ABSTRACT FROM AUTHOR]

Purpose: The authors present an evidence-based approach to quantify the effects of an array of patient anatomical features of the planning target volumes (PTVs) and organs-at-risk (OARs) and their spatial relationships on the interpatient OAR dose sparing variation in intensity modulated radiation therapy (IMRT) plans by learning from a database of high-quality prior plans. Methods: The authors formulized the dependence of OAR dose volume histograms (DVHs) on patient anatomical factors into feature models which were learned from prior plans by a stepwise multiple regression method. IMRT plans for 64 prostate, 82 head-and-neck (HN) treatments were used to train the models. Two major groups of anatomical features were considered in this study: the volumetric information and the spatial information. The geometry of OARs relative to PTV is represented by the distance-to-target histogram, DTH. Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. The final models were tested by additional 24 prostate and 24 HN plans. Results: Significant patient anatomical factors contributing to OAR dose sparing in prostate and HN IMRT plans have been analyzed and identified. They are: the median distance between OAR and PTV, the portion of OAR volume within an OAR specific distance range, and the volumetric factors: the fraction of OAR volume which overlaps with PTV and the portion of OAR volume outside the primary treatment field. Overall, the determination coefficients R2 for predicting the first principal component score (PCS1) of the OAR DVH by the above factors are above 0.68 for all the OARs and they are more than 0.53 for predicting the second principal component score (PCS2) of the OAR DVHs except brainstem and spinal cord. Thus, the above set of anatomical features combined has captured significant portions of the DVH variations for the OARs in prostate and HN plans. To test how well these features capture the interpatient organ dose sparing variations in general, the DVHs and specific dose-volume indices calculated from the regression models were compared with the actual DVHs and dose-volume indices from each patient's plan in the validation dataset. The dose-volume indices compared were V99%, V85%, and V50% for bladder and rectum in prostate plans and parotids median dose in HN plans. The authors found that for the bladder and rectum models, 17 out of 24 plans (71%) were within 6% OAR volume error and 21 plans (85%) were within 10% error; For the parotids model, the median dose values for 30 parotids out of 48 (63%) were within 6% prescription dose error and the values in 40 parotids (83%) were within 10% error. Conclusions: Quantitative analysis of patient anatomical features and their correlation with OAR dose sparing has identified a number of important factors that explain significant amount of interpatient DVH variations in OARs. These factors can be incorporated into evidence-based learning models as effective features to provide patient-specific OAR dose sparing goals. [ABSTRACT FROM AUTHOR]

Purpose: A TrueBeam linear accelerator (TB-LINAC) is designed to deliver traditionally flattened and flattening-filter-free (FFF) beams. Although it has been widely adopted in many clinics for patient treatment, limited information is available related to commissioning of this type of machine. In this work, commissioning data of three units were measured, and multiunit comparison was presented to provide valuable insights and reliable evaluations on the characteristics of the new treatment system. Methods: The TB-LINAC is equipped with newly designed waveguide, carousel assembly, monitoring control, and integrated imaging systems. Each machine in this study has 4, 6, 8, 10, 15 MV flattened photon beams, and 6 MV and 10 MV FFF photon beams as well as 6, 9, 12, 16, 20, and 22 MeV electron beams. Dosimetric characteristics of the three new TB-LINAC treatment units are systematically measured for commissioning. High-resolution diode detectors and ion chambers were used to measure dosimetric data for a range of field sizes from 10 × 10 to 400 × 400 mm2. The composite dosimetric data of the three units are presented in this work. The commissioning of intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), image-guided radiation therapy, and gating systems are also illustrated. Critical considerations of Pion of FFF photon beams and small field dosimetric measurements were investigated. Results: The authors found all PDDs and profiles matched well among the three machines. Beam data were quantitatively compared and combined through average to yield composite beam data. The discrepancies among the machines were quantified using standard deviation (SD). The mean SD of the PDDs among the three units is 0.12%, and the mean SD of the profiles is 0.40% for 10 MV FFF open fields. The variations of Pion of the chamber CC13 is 1.2 ± 0.1% under 6 MV FFF and 2.0 ± 0.5% under 10 MV FFF from dmax to the 18 cm-off-axis point at 35 cm depth under 40 × 40 cm2. The mean penumbra of crossplane flattened photon beams at collimator angle of 0° is measured from 5.88 ± 0.09 to 5.99 ± 0.13 mm from 4 to 15 MV at 10 cm depth of 100 × 100 mm2. The mean penumbra of crossplane beams at collimator angle of 0° is measured as 3.70 ± 0.21 and 4.83 ± 0.04 mm for 6 MV FFF and 10 MV FFF, respectively, at 10 cm depth with a field size of 5 × 5 cm2. The end-to-end test procedures of both IMRT and VMAT were performed for various energy modes. The mean ion chamber measurements of three units showed less than 2% between measurement and calculation; the mean MultiCube ICA measurements demonstrated over 90% pixels passing gamma analysis (3%, 3 mm, 5% threshold). The imaging dosimetric data of KV planar imaging and CBCT demonstrated improved consistency with vendor specifications and dose reduction for certain imaging protocols. The gated output verification showed a discrepancy of 0.05% or less between gating radiation delivery and nongating radiation delivery. Conclusions: The commissioning data indicated good consistency among the three TB-LINAC units. The commissioning data provided us valuable insights and reliable evaluations on the characteristics of the new treatment system. The systematically measured data might be useful for future reference. [ABSTRACT FROM AUTHOR]

Purpose: To implement dual-energy imaging technique for virtual monochromatic (VM) and linearly mixed (LM) cone beam CTs (CBCTs) and to demonstrate their potential applications in metal artifact reduction and contrast enhancement in image-guided radiation therapy (IGRT). Methods: A bench-top CBCT system was used to acquire 80 kVp and 150 kVp projections, with an additional 0.8 mm tin filtration. To implement the VM technique, these projections were first decomposed into acrylic and aluminum basis material projections to synthesize VM projections, which were then used to reconstruct VM CBCTs. The effect of VM CBCT on the metal artifact reduction was evaluated with an in-house titanium-BB phantom. The optimal VM energy to maximize contrast-to-noise ratio (CNR) for iodine contrast and minimize beam hardening in VM CBCT was determined using a water phantom containing two iodine concentrations. The LM technique was implemented by linearly combining the low-energy (80 kVp) and high-energy (150 kVp) CBCTs. The dose partitioning between low-energy and high-energy CBCTs was varied (20%, 40%, 60%, and 80% for low-energy) while keeping total dose approximately equal to single-energy CBCTs, measured using an ion chamber. Noise levels and CNRs for four tissue types were investigated for dual-energy LM CBCTs in comparison with single-energy CBCTs at 80, 100, 125, and 150 kVp. Results: The VM technique showed substantial reduction of metal artifacts at 100 keV with a 40% reduction in the background standard deviation compared to a 125 kVp single-energy scan of equal dose. The VM energy to maximize CNR for both iodine concentrations and minimize beam hardening in the metal-free object was 50 keV and 60 keV, respectively. The difference of average noise levels measured in the phantom background was 1.2% between dual-energy LM CBCTs and equivalent-dose single-energy CBCTs. CNR values in the LM CBCTs of any dose partitioning are better than those of 150 kVp single-energy CBCTs. The average CNR for four tissue types with 80% dose fraction at low-energy showed 9.0% and 4.1% improvement relative to 100 kVp and 125 kVp single-energy CBCTs, respectively. CNRs for low-contrast objects improved as dose partitioning was more heavily weighted toward low-energy (80 kVp) for LM CBCTs. Conclusions: Dual-energy CBCT imaging techniques were implemented to synthesize VM CBCT and LM CBCTs. VM CBCT was effective at achieving metal artifact reduction. Depending on the dose-partitioning scheme, LM CBCT demonstrated the potential to improve CNR for low contrast objects compared to single-energy CBCT acquired with equivalent dose. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of a synchronized moving grid (SMOG) system to remove scatter artifacts, improve the contrast-to-noise ratio (CNR), and reduce image lag artifacts in cone-beam CT (CBCT). Methods: The SMOG system proposed here uses a rapidly oscillating, synchronized moving grid attached to the kV source. Multiple partial projections are taken at different grid positions to form a complete projection in each gantry position, before the gantry moves to the next position during a scan. The grid has a low transmission factor, and it is used for both scatter reduction and scatter measurement for postscan scatter correction. Experimental studies using a static grid and an enlarged CATphan phantom were performed to evaluate the potential CNR enhancement for different SMOG exposure numbers (1, 2, and 4). Simulation studies were performed to evaluate the image lag correction for different exposure numbers (2, 3, and 4) and grid interspace widths in SMOG using the data from an anthropomorphic pelvis phantom scan. Imaging dose of SMOG was also estimated by measuring the imaging dose in a CIRS CT dose phantom using a static grid. Results: SMOG can enhance the CNR by 16% and 13% when increasing exposure number from 1 to 2 and from 2 to 4, respectively. This enhancement was more dramatic for larger phantoms and smaller initial exposure numbers. Simulation results indicated that SMOG could reduce the lag to less than 4.3% for 2-exposure mode and to less than 0.8% for 3-exposure mode when the grid interspace width was 1.4 cm. Increasing the number of exposures in SMOG dramatically reduced the residual lag in the image. Reducing the grid interspace width somewhat reduced the residual lag. Skin line artifacts were removed entirely in SMOG. Point dose measurement showed that imaging dose of SMOG at isocenter was similar as that of a conventional CBCT. Conclusions: Compared to our previously developed static-grid dual-rotation method, the proposed SMOG technique has the advantages of enhancing the CNR, correcting the image lag, and reducing the delivery time. Once implemented, SMOG has the potential to remove scatter and image lag artifacts, and significantly enhance CNR for CBCT using the same scanning time as conventional CBCT. [ABSTRACT FROM AUTHOR]

Purpose: To determine whether there is a CT dataset may be more favorable for planning and dose calculation by comparing dosimetric characteristics between treatment plans calculated using free breathing (FB), maximum and average intensity projection (MIP and AIP, respectively) CTs for lung cancer patients receiving stereotactic body radiation therapy (SBRT). Methods: Twenty lung cancer SBRT patients, treated on a linac with 2.5 mm width multileaf-collimator (MLC), were analyzed retrospectively. Both FB helical and four-dimensional CT scans were acquired for each patient. Internal target volume (ITV) was delineated based on MIP CTs and modified based on both ten-phase datasets and FB CTs. Planning target volume (PTV) was then determined by adding additional setup margin to ITV. The PTVs and beams in the optimized treatment plan based on FB CTs were copied to MIP and AIP CTs, with the same isocenters, MLC patterns and monitor units. Mean effective depth (MED) of beams, and some dosimetric parameters for both PTVs and most important organ at risk (OAR), lung minus PTV, were compared between any two datasets using two-tail paired t test. Results: The MEDs in FB and AIP plans were similar but significantly smaller (Ps < 0.001) than that in MIP plans. Minimum dose, mean dose, dose covering at least 90% and 95% of PTVs in MIP plans were slightly higher than two other plans (Ps < 0.008). The absolute volume of lung minus PTV receiving greater than 5, 10, and 20 Gy in MIP plans were significantly smaller than those in both FB and AIP plans (Ps < 0.008). Conformity index for FB plans showed a small but statistically significantly higher. Conclusions: Dosimetric characteristics of AIP plans are similar to those of FB plans. Slightly better target volume coverage and significantly lower low-dose region (≤30 Gy) in lung was observed in MIP plans. The decrease in low-dose region in lung was mainly caused by the change of lung volume contoured on two datasets rather than the differences of dose distribution between AIP and MIP plans. Compare with AIP datasets, FB datasets were more prone to significant image artifacts and MIP datasets may overestimate or underestimate the target volume when the target is closer to the denser tissue, so AIP seems favorable for planning and dose calculation for lung SBRT. [ABSTRACT FROM AUTHOR]