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This study aims to propose a novel treatment planning methodology for multi-isocenter volumetric modulated arc therapy (VMAT) craniospinal irradiation (CSI) using the special feasibility dose–volume histogram (FDVH)-guided auto-planning (AP) technique. Three different multi-isocenter VMAT -CSI plans were created, including manually based plans (MUPs), conventional AP plans (CAPs) and FDVH-guided AP plans (FAPs). The CAPs and FAPs were specially designed by combining multi-isocenter VMAT and AP techniques in the Pinnacle treatment planning system. Specially, the personalized optimization parameters for FAPs were generated using the FDVH function implemented in PlanIQ software, which provides the ideal organs at risk (OARs) sparing for the specific anatomical geometry based on the valuable assumption of the dose fall-off. Compared to MUPs, CAPs and FAPs significantly reduced the dose for most of the OARs. FAPs achieved the best homogeneity index (0.092 ± 0.013) and conformity index (0.980 ± 0.011), while CAPs were slightly inferior to the FAPs but superior to the MUPs. As opposed to MUPs, FAPs delivered a lower dose to OARs, whereas the difference between FAPs and CAPs was not statistically significant except for the optic chiasm and inner ear_L. The two AP approaches had similar MUs, which were significantly lower than the MUPs. The planning time of FAPs (145.00 ± 10.25 min) was slightly lower than that of CAPs (149.83 ± 14.37 min) and was substantially lower than that of MUPs (157.92 ± 16.11 min) with P

Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly.To develop a DVH-based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA.A DL model with a three-dimensional squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head-and-neck (HN) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training.The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for DThe successful prediction of PSQA doses and classification of PoF for HN VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Background Controversial conclusions had been reported in studies trying to confirm the impact of heart dose on overall survival (OS) reported in RTOG 0167 for non-small cell lung cancer (NSCLC) patients who underwent radiotherapy (RT). The purpose of this study is to investigate the association of lung and heart dosimetric parameters with OS in NSCLC patients treated by volumetric modulated arc therapy (VMAT). Methods Inoperable NSCLC patients treated by VMAT from March 2012 to December 2015 were retrospectively reviewed. OS and progression-free survival (PFS) were estimated with the Kaplan-Meier method. Univariate and multivariate analyses were conducted with Cox proportional hazards model. Multivariate model building was conducted using stepwise regression for variables with p-value smaller than 0.2 in the univariate analysis. Results There were 130 NSCLC patients enrolled in this study with a median age of 63 years (range from 34 to 82 y). The median prescription dose for these patients was 56 Gy (range 40-70 Gy) with a mean heart and lung dose of 14.8±8.5 Gy and 13.6±4.4 Gy, respectively. The rates of patients with above grade III radiation pneumonitis (RP) and fibrosis were 8.5% and 8.5%, respectively. The 2-year PFS and OS of these patients were 15.2% and 39.8%, with a median PFS and OS of 7.2 and 18.8 months, respectively. RP was correlated with OS (p=0.048) and lung V20 was associated with PFS (p=0.04) according to the univariate analysis. Multivariate analysis demonstrated that RP (HR 1.39, 95%CI 1.010-1.909, p=0.043) and heart V15 (HR 1.02, 95%CI 1.006-1.025 p=0.002) were progression factors of OS, and no factor was associated with PFS. Conclusions RP and heart V15 were associated with OS for patients with stage III NSCLC who underwent VMAT. Heart and lung dosimetric parameters were highly correlated with each other, sparing of heart and lung should be considered equally during the treatment planning.

Background: Published data on the effects and toxicities of volumetric modulated arc therapy (VMAT) in the management of inoperable lung cancer are scarce. Materials and methods: The clinical outcomes and pulmonary toxicities of 134 patients with consecutive inoperable lung cancer who underwent VMAT from March 2011 to September 2016 were retrospectively reviewed. The dosimetric and characteristic factors associated with acute radiation pneumonitis (RP) and pulmonary fibrosis were evaluated with univariate and multivariate analysis. Results: The average prescription doses to these 134 patients were 57.07±6.27 Gy (range 52-64 Gy). The overall median follow-up time was 18.6 months (range, 2-45 mo), with a median follow-up time for the surviving patients of 20 months (range, 7-45 mo). The 2-year progression-free survival (PFS) and overall survival (OS) for all patients were 18.2% and 38.4%, with a median PFS and OS of 7.6 months and 18.6 months, respectively. The percent of patients with grade III/higher RP and pulmonary fibrosis were 10.5% and 9.0%, respectively. V13 (p=0.02) and age (p=0.02) were independently associated with acute RP according to multivariate analysis. The constraints for lung dosimetric metrics V10,V13,V20 and V30 were approximately 49%,41%,26% and 17% in VMAT treatment of lung cancer to limit the RP rate < 10%. Conclusion: VMAT can be delivered safely with acceptable acute and late toxicities for lung cancer patients. Lung dosimetric metrics were valuable in predicting acute RP. A lung V13 constraint of 40% was helpful to limit the RP rate < 10% in VMAT treatment of lung cancer patients.

The aim of this study was to investigate the feasibility and sensitivity of using individual volume-based 3D gamma indices for composite dose-volume histogram (DVH)-based intensity-modulated radiation therapy (IMRT) quality assurance (QA). Composite IMRT QA for 15 cervical cancer patients was performed with ArcCHECK. The percentage dosimetric errors (%DEs) of DVH metrics when comparing treatment planning system and QA-reconstructed dose distribution, percentage gamma passing rates (%GPs) with different criteria for individual volumes and global gamma indices were evaluated, as well as their correlations. Receiver operating characteristic (ROC) curves were applied in order to study the sensitivities of the global and individual volume gamma indices. Most %DEs of the DVH metrics were within 3%. The γPTV and γrectum were

Purpose: To investigate the feasibility of partial arc volumetric modulated arc therapy (VMAT) in lung cancer stereotactic body radiotherapy (SBRT), as well the volumetric and dosimetric effects of different internal target volume (ITV) definitions with 4D CT. Methods: Fourteen patients with primary and metastatic lung cancer underwent SBRT were enrolled. Full and partial arc VMAT plans were generated with four different ITVs: ITVall, ITVMIP, ITVAIP and ITV2phases, representing ITVs generated from all 10 respiratory phases, maximum intensity projection (MIP), average intensity projection (AIP), and 2 extreme respiratory phases. Volumetric and dosimetric differences, as well as MU and delivery time were investigated. Results: Partial arc VMAT irradiated more dose at 2 cm away from planning target volume (PTV) (P = 0.002), however, it achieved better protection on mean lung dose , lung V5, spinal cord, heart and esophagus compared with full arc VMAT. The average MU and delivery time of partial arc VMAT were 240 and 1.6 min less than those of full arc VMAT. There were no significant differences on target coverage and organ at risks (OARs) sparing among four ITVs. The average percent volume differences of ITVMIP, ITVAIP and ITV2phases to ITVall were 8.6%, 13.4%, and 25.2%, respectively. Conclusions: Although partial arc VMAT delivered more dose 2 cm out of PTV, it decreases the dose to lung, spinal cord, and esophagus, as well decreased the total MU and delivery time compared with full arc VMAT without sacrificing target coverage. Partial arc VMAT was feasible and more efficient for lung SBRT.

Purpose Dynamic myocardial perfusion computed tomography (DM-PCT) imaging offers benefits over quantitative assessment of myocardial blood flow (MBF) for diagnosis and risk stratification of coronary artery disease. However, one major drawback of DM-PCT imaging is that a high radiation level is imparted by repeated scanning. To address this issue, in this work, we developed a statistical iterative reconstruction algorithm based on the penalized weighted least-squares (PWLS) scheme by incorporating a motion adaptive sparsity prior (MASP) model to achieve high-quality DM-PCT imaging with low tube current dynamic data acquisition. For simplicity, we refer to the proposed algorithm as “PWLS-MASP’’. Methods The MASP models both the spatial and temporal structured sparsity of DM-PCT sequence images with the assumption that the differences between adjacent frames after motion correction are sparse in the gradient image domain. To validate and evaluate the effectiveness of the present PWLS-MASP algorithm thoroughly, a modified XCAT phantom and preclinical porcine DM-PCT dataset were used in the study. Results The present PWLS-MASP algorithm can obtain high-quality DM-PCT images in both phantom and porcine cases, and outperforms the existing filtered back-projection algorithm and PWLS-based algorithms with total variation regularization (PWLS-TV) and robust principal component analysis regularization (PWLS-RPCA) in terms of noise reduction, streak artifacts mitigation, and time density curve estimation. Moreover, the PWLS-MASP algorithm can yield more accurate diagnostic hemodynamic parametric maps than the PWLS-TV and PWLS-RPCA algorithms. Conclusions The study indicates that there is a substantial advantage in using the present PWLS-MASP algorithm for low-dose DM-PCT, and potentially in other dynamic tomography areas.

Background Controversial conclusions had been reported in studies trying to confirm the impact of heart dose on overall survival (OS) reported in RTOG 0167 for non-small cell lung cancer (NSCLC) patients who underwent radiotherapy (RT). The purpose of this study is to investigate the association of lung and heart dosimetric parameters with OS in NSCLC patients treated by volumetric modulated arc therapy (VMAT). Methods Inoperable NSCLC patients treated by VMAT from March 2012 to December 2015 were retrospectively reviewed. OS and progression-free survival (PFS) were estimated with the Kaplan–Meier method. Univariate and multivariate analyses were conducted with Cox proportional hazards model. Multivariate model building was conducted using stepwise regression for variables with p-value smaller than 0.2 in the univariate analysis. Results There were 130 NSCLC patients enrolled in this study with a median age of 63 years (range from 34 to 82 y). The median prescription dose for these patients was 56 Gy (range 40–70 Gy) with a mean heart and lung dose of 14.8±8.5 Gy and 13.6±4.4 Gy, respectively. The rates of patients with above grade III radiation pneumonitis (RP) and fibrosis were 8.5% and 8.5%, respectively. The 2-year PFS and OS of these patients were 15.2% and 39.8%, with a median PFS and OS of 7.2 and 18.8 months, respectively. RP was correlated with OS (p=0.048) and lung V20 was associated with PFS (p=0.04) according to the univariate analysis. Multivariate analysis demonstrated that RP (HR 1.39, 95%CI 1.010–1.909, p=0.043) and heart V15 (HR 1.02, 95%CI 1.006–1.025 p=0.002) were progression factors of OS, and no factor was associated with PFS. Conclusions RP and heart V15 were associated with OS for patients with stage III NSCLC who underwent VMAT. Heart and lung dosimetric parameters were highly correlated with each other, sparing of heart and lung should be considered equally during the treatment planning.

Purpose: The purpose of the present study was first to apply the progressive optimization algorithm based automatic volumetric modulated arc therapy (POA-VMAT) technology to accelerate and improve the radiotherapy of cervicothoracic esophageal cancer (CTEC). We comprehensive analyze the feasibility, normal tissue complication probability (NTCP) and dosimetric results of POA-VMAT, manual based VMAT and step-shoot intensity-modulated radiation therapy (IMRT) plans in the treatment of CTEC. Methods: Sixty patients with CTEC with or without concomitant chemotherapy at our institution between 2017 and 2019 were retrospectively identified. The manual 7field-IMRT (7f-IMRT), Single-arc-VMAT and Double-arc-VMAT (Single-Arc/Double-Arc) plans were generated in all cases. The POA-VMAT was designed using the automatic dual-arc VMAT technology of Pinnacle3 9.10 planning system based on progressive optimization algorithm. Specially, it includes the selection of treatment techniques, the running of automated planning scripts, and the evaluation of the final radiotherapy regimen. Subsequently, quantitative evaluation of plans was performed by means of standard dose–volume histograms, homogeneity index (HI) and conformity index (CI). Results: Target dose conformity of the 7f-IMRT plan was inferior to all plans, whereas the Double-Arc plan was slightly inferior to the POA-VMAT but superior to the Single-Arc and 7f-IMRT plan. The HI for 7f-IMRT, Single-Arc, Double-Arc and POA-VMAT were 0.17 ± 0.08, 0.28 ± 0.06, 0.29 ± 0.06 and 0.28 ± 0.03, respectively. For the NTCP results, there was significant statistical difference among POA-VMAT, IMRT and VMAT plans. The total MU was reduced by 48.3% and 42.1% in Single-Arc and POA-VMAT plans compare to IMRT plans. Conclusions: By comprehensive consideration, POA-VMAT efficiently generate acceptable treatment plans for CTEC without dose escalation to OARs and overall superior to manual planning which is a good option for treating CTEC.

To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning. The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters. A total of 42 radiomic features and 18 dosimetric parameters plus the patients’ characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model. The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412). • The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.

Dynamic myocardial perfusion CT (DMP-CT) imaging provides quantitative functional information for diagnosis and risk stratification of coronary artery disease by calculating myocardial perfusion hemodynamic parameter (MPHP) maps. However, the level of radiation delivered by dynamic sequential scan protocol can be potentially high. The purpose of this work is to develop a pre-contrast normal-dose scan induced structure tensor total variation regularization based on the penalized weighted least-squares (PWLS) criteria to improve the image quality of DMP-CT with a low-mAs CT acquisition. For simplicity, the present approach was termed as 'PWLS-ndiSTV'. Specifically, the ndiSTV regularization takes into account the spatial-temporal structure information of DMP-CT data and further exploits the higher order derivatives of the objective images to enhance denoising performance. Subsequently, an effective optimization algorithm based on the split-Bregman approach was adopted to minimize the associative objective function. Evaluations with modified dynamic XCAT phantom and preclinical porcine datasets have demonstrated that the proposed PWLS-ndiSTV approach can achieve promising gains over other existing approaches in terms of noise-induced artifacts mitigation, edge details preservation, and accurate MPHP maps calculation.

Radiation-associated toxicities due to sophisticated ocular anatomy and shape variability of organs at risk (OARs) are major concerns during external beam radiation therapy (EBRT) of patients with intraocular cancer. A novel two-step image registration strategy between optical coherence tomography (OCT) and computed tomography (CT) images was proposed and validated to precisely localize the target in the EBRT of patients with intraocular cancer. Specifically, multiple features from OCT and CT images were extracted automatically, then spatial transformation based on thin-plate spline function was performed iteratively to achieve feature alignment between the CT and OCT images. Finally, an exclusive OR (XOR) algorithm was applied for precise 3D registration using a 3D-mesh model generated from OCT and CT volumes. The accuracy of the proposed novel registration strategy was validated and tested in a schematic-eye phantom with an artificially introduced tumor and in ten patients with confirmed primary and/or secondary intraocular cancer. There was an average registration error and computational time of 0.21 ± 0.05° and 259 ± 5 s, together with an average Dice similarity coefficient and Hausdorff distance of 88.4 ± 0.65 and 0.89 ± 0.09, respectively. The preliminary experimental results demonstrated that the proposed novel strategy to overcome current limitations on eye modeling and to localize precisely the tumor target during EBRT of intraocular cancer is promising.

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic images acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mAs data acquisitions. For simplicity, the presented method is termed as “MPD-AwTTV”. More specifically, the gains of the AwTTV regularization are from the anisotropic edge property of the sequential MPCT images over the original tensor total variation regularization. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate under the framework of iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifacts suppression, edge details preservation and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in the digital phantom studies, and the similar gains can be obtained in the porcine data experiment.

Multienergy computed tomography (MECT) has the potential to simultaneously offer multiple sets of energy- selective data belonging to specific energy windows. However, because sufficient photon counts are not available in the specific energy windows compared with that in the whole energy window, the MECT images reconstructed by the analytical approach often suffer from poor signal-to-noise (SNR) and strong streak artifacts. To eliminate this drawback, in this work we present a penalized weighted least-squares (PWLS) scheme by incorporating the new concept of structure tensor total variation (STV) regularization to improve the MECT images quality from low-milliampere-seconds (low-mAs) data acquisitions. Henceforth the present scheme is referred to as `PWLS- STV' for simplicity. Specifically, the STV regularization is derived by penalizing the eigenvalues of the structure tensor of every point in the MECT images. Thus it can provide more robust measures of image variation, which can eliminate the patchy artifacts often observed in total variation regularization. Subsequently, an alternating optimization algorithm was adopted to minimize the objective function. Experiments with a digital XCAT phantom clearly demonstrate that the present PWLS-STV algorithm can achieve more gains than the existing TV-based algorithms and the conventional filtered backpeojection (FBP) algorithm in terms of noise-induced artifacts suppression, resolution preservation, and material decomposition assessment.

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for diagnosis and risk stratification of coronary artery disease by assessing the myocardial perfusion hemodynamic maps (MPHM). Meanwhile, the repeated scanning of the same region results in a relatively large radiation dose to patients potentially. In this work, we present a robust MPCT deconvolution algorithm with adaptive-weighted tensor total variation regularization to estimate residue function accurately under the low-dose context, which is termed `MPD-AwTTV'. More specifically, the AwTTV regularization takes into account the anisotropic edge property of the MPCT images compared with the conventional total variation (TV) regularization, which can mitigate the drawbacks of TV regularization. Subsequently, an effective iterative algorithm was adopted to minimize the associative objective function. Experimental results on a modified XCAT phantom demonstrated that the present MPD-AwTTV algorithm outperforms and is superior to other existing deconvolution algorithms in terms of noise-induced artifacts suppression, edge details preservation and accurate MPHM estimation.

To develop a computed tomography myocardial perfusion (CT-MP) deconvolution algorithm by incorporating high-dimension total variation (HDTV) regularization.A perfusion deconvolution model was formulated for the low-dose CT-MPI data, followed by HDTV regularization to regularize the consistency of the solution by fusing the spatial correlation of the vascular structure and the temporal continuation of the blood flow signal.Both qualitative and quantitative studies were conducted using XCAT and pig myocardial perfusion data to evaluate the present algorithm. The experimental results showed that this algorithm achieved hemodynamic parameter maps with better performances than the existing methods in terms of streak-artifacts suppression, noise-resolution tradeoff, and diagnosis structure preservation.The proposed algorithm can achieve high-quality hemodynamic parameter maps in low-dose CT-MPI.