Dosimetric Effect of Target Position Accuracy on Single-Isocenter Multiple Liver Metastases SBRT.

Purpose: To evaluate the dosimetric effects of intrafraction baseline shifts combined with rotational errors on Four-dimensional computed tomography-guided stereotactic body radiotherapy for multiple liver metastases (MLMs). Methods: A total of 10 patients with MLM (2 or 3 lesions) were selected for this retrospective study. Baseline shift errors of 0.5, 1.0, and 2.0 mm; and rotational errors of 0.5°, 1°, and 1.5°, were simulated about all axes. All of the baseline shifts and rotation errors were simulated around the planned isocenter using a matrix transformation of 6° of freedom. The coverage degradation of baseline shifts and rotational errors were analyzed according to the dose to 95% of the planning target volume (D95) and the volume covered by 95% of the prescribed dose (V95), and related changes in gross tumor volume were also analyzed. Results: At the rotation error of 0.5° and the baseline offset of less than 0.5 mm, the D95 and V95 values of all targets were >95%. For rotational errors of 1.0° (combined with all baseline shift errors), 36.3% of targets had D95 and V95 values of <95%. Coverage worsened substantially when the baseline shift errors were increased to 1.0 mm. D95 and V95 values were >95% for about 77.3% of the targets. Only 11.4% of the D95 and V95 values were >95% when the baseline shift errors were increased to 2.0 mm. When the rotational error was increased to 1.5° and baseline shift errors increased to 1.0 mm, the D95 and V95 values were >95% in only 3 cases. Conclusions: The multivariate regression model analysis in this study showed that the coverage of the target decreased further with reduced target volume, increasing the baseline drift, the rotation error, and the distance to the target.

Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images.

BACKGROUND: Magnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the absence of electron density information limits its further clinical application. Therefore, the aim of this study is to develop and evaluate a novel unsupervised network (cycleSimulationGAN) for unpaired MR-to-CT synthesis. METHODS: The proposed cycleSimulationGAN in this work integrates contour consistency loss function and channel-wise attention mechanism to synthesize high-quality CT-like images. Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature representation capability of deep network and extract more effective features. The mean absolute error (MAE) of Hounsfield Units (HU), peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and structural similarity index (SSIM) were calculated between synthetic CT (sCT) and ground truth (GT) CT images to quantify the overall sCT performance. RESULTS: One hundred and sixty nasopharyngeal carcinoma (NPC) patients who underwent volumetric-modulated arc radiotherapy (VMAT) were enrolled in this study. The generated sCT of our method were more consistent with the GT compared with other methods in terms of visual inspection. The average MAE, RMSE, PSNR, and SSIM calculated over twenty patients were 61.88 ± 1.42, 116.85 ± 3.42, 36.23 ± 0.52 and 0.985 ± 0.002 for the proposed method. The four image quality assessment metrics were significantly improved by our approach compared to conventional cycleGAN, the proposed cycleSimulationGAN produces significantly better synthetic results except for SSIM in bone. CONCLUSIONS: We developed a novel cycleSimulationGAN model that can effectively create sCT images, making them comparable to GT images, which could potentially benefit the MRI-based treatment planning.

Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.

Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance.

Objective. Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range dependency for volumetric dose prediction due to the loss of spatial dose features and the inherent locality of CPs. The purpose of this work is to construct a deep hybrid network by combining the self-attention mechanism-based Transformer with modified U-Net for predicting measurement-guided volumetric dose (MDose) of prePSQA.Approach. The enrolled 307 cancer patients underwent VMAT were randomly divided into 246 and 61 cases for training and testing the model. The input data included computed tomography images, radiotherapy dose images exported from the treatment planning system, as well as the MDose distribution from the verification system. The output was the predicted high-quality voxel-wise prePSQA dose distribution.Main results: qualitative and quantitative experimental results show that the proposed prediction method could achieve comparable or better performance on MDose prediction over other approaches in terms of spatial dose distribution, dose-volume histogram metrics, gamma passing rates, mean absolute error, root mean square error, and structural similarity.Significance. The preliminary results on multiple cancer sites show that our approach can be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Comparative dosimetric and radiobiological assessment of left-sided whole breast and regional nodes with advanced radiotherapy techniques.

The aim of this study was to analyze the dosimetric and radiobiologic differences of the left-sided whole breast and regional nodes in intensity-modulated radiotherapy (IMRT), volume-modulated arc therapy (VMAT), and helical tomotherapy (HT).  The IMRT, VMAT, and HT plans in this study were generated for thirty-five left-sided breast cancer patients after breast-conserving surgery (BCS). The planning target volume (PTV) included the whole breast and supraclavicular nodes. PTV coverage, homogeneity index (HI), conformity index (CI), dose to organs at risk (OARs), secondary cancer complication probability (SCCP), and excess absolute risk (EAR) were used to evaluate the plans. Compared to IMRT, the VMAT and HT plans resulted in higher PTV coverage and homogeneity. The VMAT and HT plans also delivered a lower mean dose to the ipsilateral lung (9.19 ± 1.36 Gy, 9.48 ± 1.17 Gy vs. 11.31 ± 1.42 Gy) and heart (3.99 ± 0.86 Gy, 4.48 ± 0.62 Gy vs. 5.53 ± 1.02 Gy) and reduced the V5Gy, V10Gy, V20Gy, V30Gy, and V40Gy of the ipsilateral lung and heart. The SCCP and EAR for the ipsilateral lung were reduced by 3.67%, 3.09% in VMAT, and 22.18%, 19.21% in HT, respectively. While were increased for the contralateral lung and breast. This study showed that VMAT plans provide a more homogeneous dose distribution to the PTV, minimizing exposure to ipsilateral structures and significantly reducing SCCP and EAR, and slightly increasing dose to contralateral structures. Overall, the VMAT plan can be considered a beneficial technique for BCS patients whose PTV includes the whole breast and regional nodes.

Multi-isocenter VMAT craniospinal irradiation using feasibility dose-volume histogram-guided auto-planning technique.

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 < 0.0167. Overall, introducing the multi-isocenter AP technique into VMAT-CSI yielded positive outcomes and may play an important role in clinical CSI planning in the future.

Feasibility and dosimetric evaluation of single- and multi-isocentre stereotactic body radiation therapy for multiple liver metastases.

Objectives: Single-isocentre volumetric-modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) improves treatment efficiency and patient compliance for patients with multiple liver metastases (MLM). However, the potential increase in dose spillage to normal liver tissue using a single-isocentre technique has not yet been studied. We comprehensively evaluated the quality of single- and multi-isocentre VMAT-SBRT for MLM and propose a RapidPlan-based automatic planning (AP) approach for MLM SBRT. Methods: A total of 30 patients with MLM (two or three lesions) were selected for this retrospective study. We manually replanned all patients treated with MLM SBRT by using the single-isocentre (MUS) and multi-isocentre (MUM) techniques. Then, we randomly selected 20 MUS and MUM plans for training to generate the single-isocentre RapidPlan model (RPS) and the multi-isocentre RapidPlan model (RPM). Finally, we used data from the remaining 10 patients to validate RPS and RPM. Results: Compared with MUS, MUM reduced the mean dose delivered to the right kidney by 0.3 Gy. The mean liver dose (MLD) was 2.3 Gy higher for MUS compared with MUM. However, the monitor units, delivery time, and V20Gy of normal liver (liver-gross tumour volume) for MUM were significantly higher than for MUS. Based on validation, RPS and RPM slightly improved the MLD, V20Gy, normal tissue complications, and dose sparing to the right and left kidneys and spinal cord compared with manual plans (MUS vs RPS and MUM vs RPM), but RPS and RPM significantly increased monitor units and delivery time. Conclusions: The single-isocentre VMAT-SBRT approach could be used for MLM to reduce treatment time and patient comfort at the cost of a small increase in the MLD. Compared with the manual plans, RapidPlan-based plans, especially RPS, have slightly improved quality.

Efficient dose-volume histogram-based pretreatment patient-specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy.

BACKGROUND: 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. PURPOSE: 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. METHODS: 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 (H&N) 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. RESULTS: 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 Dmean of PTV6900 (p = 0.001), D50 of PTV6000 (p = 0.014), D2 of PTV5400 (p = 0.009), D50 of left parotid (p = 0.015), and Dmax of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases. CONCLUSIONS: The successful prediction of PSQA doses and classification of PoF for H&N 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.

Comprehensive Comparison of Progressive Optimization Algorithm Based Automatic Plan and Manually Planned Treatment Technique for Cervical-Thoracic Esophageal Cancers.

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.

Safety and outcomes of volumetric modulated arc therapy in the treatment of patients with inoperable lung cancer.

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.

Association of lung and heart dose with survival in patients with non-small cell lung cancer underwent volumetric modulated arc therapy.

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.

Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. KEY POINTS: • 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.

Precise delineation and tumor localization based on novel image registration strategy between optical coherence tomography and computed tomography in the radiotherapy of intraocular cancer.

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.

Composite QA for intensity-modulated radiation therapy using individual volume-based 3D gamma indices.

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 <80% at 2%/2 mm; apart from these two individual volume indices, all other individual volume gamma indices and global indices had acceptable %GPs. For the criteria of 2%/2 mm, 3%/3 mm and 4%/4 mm, individual volume-based %GPs and global %GPs were correlated in 11, 1 and 12 out of 24 %DE metrics, and in 5, 4 and 5 out of 24 %DE metrics, respectively. Individual volume-based %GPs had a higher percentage of correlation with DVH metrics (%DEs) compared with global %GPs in composite IMRT QA. The areas under the curve (AUCs) of individual volume %GPs were higher than those of global %GPs. In conclusion, individual volume-based %GPs had a higher correlation with %DEs of metrics and a higher sensitivity presented by ROC analysis compared with global %GPs for composite IMRT QA. Thus, use of individual volume-based 3D gamma indices was found to be feasible and sensitive for composite IMRT QA.

Multidimensional correlation among plan complexity, quality and deliverability parameters for volumetric-modulated arc therapy using canonical correlation analysis.

A multidimensional exploratory statistical method, canonical correlation analysis (CCA), was applied to evaluate the impact of complexity parameters on the plan quality and deliverability of volumetric-modulated arc therapy (VMAT) and to determine parameters in the generation of an ideal VMAT plan. Canonical correlations among complexity, quality and deliverability parameters of VMAT, as well as the contribution weights of different parameters were investigated with 71 two-arc VMAT nasopharyngeal cancer (NPC) patients, and further verified with 28 one-arc VMAT prostate cancer patients. The average MU and MU per control point (MU/CP) for two-arc VMAT plans were 702.6 ± 55.7 and 3.9 ± 0.3 versus 504.6 ± 99.2 and 5.6 ± 1.1 for one-arc VMAT plans, respectively. The individual volume-based 3D gamma passing rates of clinical target volume (γCTV) and planning target volume (γPTV) for NPC and prostate cancer patients were 85.7% ± 9.0% vs 92.6% ± 7.8%, and 88.0% ± 7.6% vs 91.2% ± 7.7%, respectively. Plan complexity parameters of NPC patients were correlated with plan quality (P = 0.047) and individual volume-based 3D gamma indices γ(IV) (P = 0.01), in which, MU/CP and segment area (SA) per control point (SA/CP) were weighted highly in correlation with γ(IV) , and SA/CP, percentage of CPs with SA < 5 × 5 cm2 (%SA < 5 × 5 cm2) and PTV volume were weighted highly in correlation with plan quality with coefficients of 0.98, 0.68 and -0.99, respectively. Further verification with one-arc VMAT plans demonstrated similar results. In conclusion, MU, SA-related parameters and PTV volume were found to have strong effects on the plan quality and deliverability.

Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior.

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.

Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization.

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.

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study.

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 image 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-mA s data acquisitions. For simplicity, the presented method is termed 'MPD-AwTTV'. More specifically, the gains of the AwTTV regularization over the original tensor total variation regularization are from the anisotropic edge property of the sequential MPCT images. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate in the framework of an 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 artifact suppression, edge detail preservation, and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in digital phantom studies, and similar gains can be obtained in the porcine data experiment.

[Robust low-dose CT myocardial perfusion deconvolution via high-dimension total variation regularization].

OBJECTIVE: To develop a computed tomography myocardial perfusion (CT-MP) deconvolution algorithm by incorporating high-dimension total variation (HDTV) regularization. METHODS: 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. RESULTS: 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. CONCLUSION: The proposed algorithm can achieve high-quality hemodynamic parameter maps in low-dose CT-MPI.