Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT

This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

Statistical breathing curve sampling to quantify interplay effects of moving lung tumors in a 4D Monte Carlo dose calculation framework

PURPOSE

The interplay between respiratory tumor motion and dose application by intensity modulated radiotherapy (IMRT) techniques can potentially lead to undesirable and non-intuitive deviations from the planned dose distribution. We developed a 4D Monte Carlo (MC) dose recalculation framework featuring statistical breathing curve sampling, to precisely simulate the dose distribution for moving target volumes aiming at a comprehensive assessment of interplay effects.

METHODS

We implemented a dose accumulation tool that enables dose recalculations of arbitrary breathing curves including the actual breathing curve of the patient. This MC dose recalculation framework is based on linac log-files, facilitating a high temporal resolution up to 0.1 s. By statistical analysis of 128 different breathing curves, interplay susceptibility of different treatment parameters was evaluated for an exemplary patient case. To facilitate prospective clinical application in the treatment planning stage, in which patient breathing curves or linac log-files are not available, we derived a log-file free version with breathing curves generated by a random walk approach. Interplay was quantified by standard deviations σ in D_5%, D_50% and D_95%.

RESULTS

Interplay induced dose deviations for single fractions were observed and evaluated for IMRT and volumetric arc therapy (σ_D95% up to 1.3 %) showing a decrease with higher fraction doses and an increase with higher MU rates. Interplay effects for conformal treatment techniques were negligible (σ<0.1%). The log-file free version and the random walk generated breathing curves yielded similar results (deviations in σ< 0.1 %) and can be used as substitutes for interplay assessment.

CONCLUSION

It is feasible to combine statistically sampled breathing curves with MC dose calculations. The universality of the presented framework allows comprehensive assessment of interplay effects in retrospective and prospective clinically relevant scenarios.

GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs

In the last two years learning-based methods have started to show encouraging results in different supervised and unsupervised medical image registration tasks. Deep neural networks enable (near) real time applications through fast inference times and have tremendous potential for increased registration accuracies by task-specific learning. However, estimation of large 3D deformations, for example present in inhale to exhale lung CT or interpatient abdominal MRI registration, is still a major challenge for the widely adopted U-Net-like network architectures. Even when using multi-level strategies, current state-of-the-art DL registration results do not yet reach the high accuracy of conventional frameworks. To overcome the problem of large deformations for deep learning approaches, in this work, we present GraphRegNet, a sparse keypoint-based geometric network for dense deformable medical image registration. Similar to the successful 2D optical flow estimation of FlowNet or PWC-Net we leverage discrete dense displacement maps to facilitate the registration process. In order to cope with enormously increasing memory requirements when working with displacement maps in 3D medical volumes and to obtain a well-regularised and accurate deformation field we 1) formulate the registration task as the prediction of displacement vectors on a sparse irregular grid of distinctive keypoints and 2) introduce our efficient GraphRegNet for displacement regularisation, a combination of convolutional and graph neural network layers in a unified architecture. In our experiments on exhale to inhale lung CT registration we demonstrate substantial improvements (TRE below 1.4 mm) over other deep learning methods. Our code is publicly available at https://github.com/multimodallearning/graphregnet.

A hierarchical model of abdominal configuration changes extracted from golden angle radial magnetic resonance imaging

Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min-1 for average breathing frequencies of 12.0 cycles min-1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min-1 for average frequencies of 3.2 cycles min-1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating.

A preliminary investigation of re-evaluating the irradiation dose in hepatocellular carcinoma radiotherapy applying 4D CT and deformable registration

Purpose

To investigate the effect of breathing motion on dose distribution for hepatocellular carcinoma (HCC) patients using four‐dimensional (4D) CT and deformable registration.

Methods

Fifty HCC patients who were going to receive radiotherapy were enrolled in this study. All patients had been treated with transarterial chemoembolization beforehand. Three‐dimensional (3D) and 4D CT scans in free breathing were acquired sequentially. Volumetric modulated arc therapy (VMAT) was planned on the 3D CT images and maximum intensity projection (MIP) images. Thus, the 3D dose (Dose‐_3D) and MIP dose (Dose‐_MIP) were obtained, respectively. Then, the Dose‐_3D and Dose‐_MIP were recalculated on 10 phases of 4D CT images, respectively, in which the end‐inhale and end‐exhale phase doses were defined as Dose‐_3D‐EI, Dose‐_3D‐EE, Dose‐_MIP‐EI, and Dose‐_MIP‐EE. The 4D dose (Dose‐_4D‐3D and Dose‐_4D‐MIP) were obtained by deforming 10 phase doses to the end‐exhale CT to accumulate. The dosimetric difference in Dose‐_3D, Dose‐_EI3D, Dose‐_EE3D, Dose‐_4D‐3D, Dose‐_MIP, Dose‐_EIMIP, Dose‐_EEMIP, and Dose‐_4D‐MIP were compared to evaluate the motion effect on dose delivery to the planning target volume (PTV) and normal liver.

Results

Compared with Dose‐_3D, PTV D99 in Dose‐_EI3D, Dose‐_EE3D and Dose‐_4D‐3D decreased by an average of 6.02%, 1.32%, 2.43%, respectively (P < 0.05); while PTV D95 decreased by an average of 3.34%, 1.51%, 1.93%, respectively (P < 0.05). However, CI and HI of the PTV in Dose‐_3D was superior to the other three distributions (P < 0.05). There was no significant differences for the PTV between Dose‐_EI and Dose‐_EE, and between the two extreme phase doses and Dose‐_4D (P> 0.05). Negligible difference was observed for normal liver in all dose distributions (P> 0.05).

Conclusions

Four‐dimensional dose calculations potentially ensure target volume coverage when breathing motion may affect the dose distribution. Dose escalation can be considered to improve the local control of HCC on the basis of accurately predicting the probability of radiation‐induced liver disease.

Accumulation of the delivered dose based on cone-beam CT and deformable image registration for non-small cell lung cancer treated with hypofractionated radiotherapy

Background

This study aimed to quantify the dosimetric differences between the planned and delivered dose to tumor and normal organs in locally advanced non-small cell lung cancer (LANSCLC) treated with hypofractionated radiotherapy (HRT), and to explore the necessity and identify optimal candidates for adaptive radiotherapy (ART).

Methods

Twenty-seven patients with stage III NSCLC were enrolled. Planned radiation dose was 51Gy in 17 fractions with cone-beam CT (CBCT) acquired at each fraction. Virtual CT was generated by deformable image registration (DIR) of the planning CT to CBCT for dose calculation and accumulation. Dosimetric parameters were compared between original and accumulated plans using Wilcoxon signed rank test. Correlations between dosimetric differences and clinical variables were analyzed using Mann-Whitney U test or Chi-square test.

Results

Patients had varied gross tumor volume (GTV) reduction by HRT (median reduction rate 11.1%, range − 2.9-44.0%). The V₅₁ of planning target volume for GTV (PTV-GTV) was similar between original and accumulated plans (mean, 88.2% vs. 87.6%, p = 0.452). Only 11.1% of patients had above 5% relative decrease in V₅₁ of PTV-GTV in accumulated plans. Compared to the original plan, limited increase (median relative increase < 5%) was observed in doses of total lung (mean dose, V₂₀ and V₃₀), esophagus (mean dose, maximum dose) and heart (mean dose, V₃₀ and V₄₀) in accumulated plans. Less than 30% of patients had above 5% relative increase of lung or heart doses. Patients with quick tumor regression or baseline obstructive pneumonitis showed more notable increase in doses to normal structures. Patients with baseline obstructive atelectasis showed notable decrease (10.3%) in dose coverage of PTV-GTV.

Conclusions

LANSCLC patients treated with HRT had sufficient tumor dose coverage and acceptable normal tissue dose deviation. ART should be applied in patients with quick tumor regression and baseline obstructive pneumonitis/atelectasis to spare more normal structures.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12885-020-07617-3.

Impact of Intra-Fractional Motion on Dose Distributions in Lung IMRT

Aim

To investigate the impact of intra-fractional motion on dose distribution in patients treated with intensity-modulated radiation therapy (IMRT) for lung cancer.

Materials and methods

Twenty patients who had undergone IMRT for non-small cell lung cancer were selected for this retrospective study. For each patient, a four-dimensional computed tomography (CT) image set was acquired and clinical treatment plans were developed using the average CT. Dose distributions were then re-calculated for each of the 10 phases of respiratory cycle and combined using deformable image registration to produce cumulative dose distributions that were compared with the clinical treatment plans.

Results

Intra-fractional motion reduced planning target volume (PTV) coverage in all patients. The median reduction of PTV volume covered by the prescription isodose was 3.4%; D₉₈ was reduced by 3.1 Gy. Changes in the mean lung dose were within ±0.7 Gy. V₂₀ for the lung increased in most patients; the median increase was 1.6%. The dose to the spinal cord was unaffected by intra-fractional motion. The dose to the heart was slightly reduced in most patients. The median reduction in the mean heart dose was 0.22 Gy, and V₃₀ was reduced by 2.5%.The maximum dose to the esophagus was also reduced in most patients, by 0.74 Gy, whereas V₅₀ did not change significantly. The median number of points in which dose differences exceeded 3%/3 mm was 6.2%.

Findings

Intra-fractional anatomical changes reduce PTV coverage compared to the coverage predicted by clinical treatment planning systems that use the average CT for dose calculation. Doses to organs at risk were mostly over-predicted.

Dosimetric study of three-dimensional static and dynamic SBRT radiotherapy for hepatocellular carcinoma based on 4DCT image deformable registration

The purpose of this work was to determine the actual dose received by normal tissues during four‐dimensional radiation therapy (4DRT) composed of ten phases of four‐dimensional computer tomography (4DCT) images. The analysis was performed by tracking the hepatocellular carcinoma SBRT. Data were acquired from the tracking of each phase with the beam aperture for 28 hepatocellular carcinoma patients, and the data were used to generate a cumulative plan, which was compared to a three‐dimensional (3D) plan formed from a merged target volume based on 4DCT images in a radiation treatment planning system (TPS). The change in normal tissue dose was evaluated in the plan using the parameters V5, V10, V15, V20, V25, V30, V35, and V40 (volumes receiving 5, 10, 15, 20, 25, 30, 35, and 40 Gy, respectively) in the dose‐volume histogram for the liver; the mean dose was analyzed for the following tissues: liver, left kidney, and right kidney. The maximum dose was analyzed for the following tissues: bowel, duodenum, esophagus, stomach, and heart. There was a significant difference in the dose between the 4D planning target volume (PTV) (average 115.71 cm³) and ITV (169.86 cm³). The planning objective was for 95% of the volume of the PTV to be covered by the prescription dose, but the mean dose for the liver, left kidney and right kidney had an average decrease of 23.13%, 49.51%, and 54.38%, respectively. The maximum dose for the bowel, duodenum, esophagus, stomach, and heart had an average decrease of 16.77%, 28.07%, 24.28%, 4.89%, and 4.45%, respectively. Compared to 3D RT, the radiation volume for the liver V5, V10, V15, V20, V25, V30, V35, and V40 using the 4D plans had a significant decrease (P ﹤ 0.05). The 4D method creates plans that permit sparing of the normal tissues more than the commonly used ITV method, which delivers the same dosimetric effects to the target.

Comparison of planned dose on different CT image sets to four-dimensional Monte Carlo dose recalculation using the patient's actual breathing trace for lung stereotactic body radiation therapy

PURPOSE

The need for four-dimensional (4D) treatment planning becomes indispensable when it comes to radiation therapy for moving tumors in the thoracic and abdominal regions. The primary purpose of this study is to combine the actual breathing trace during each individual treatment fraction with the Linac's log file information and Monte Carlo 4D dose calculations. We investigated this workflow on multiple computed tomography (CT) datasets in a clinical environment for stereotactic body radiation therapy (SBRT) treatment planning.

METHODS

We have developed a workflow, which allows us to recalculate absorbed dose to a 4DCT dataset using Monte Carlo calculation methods and accumulate all 4D doses in order to compare them to the planned dose using the Linac's log file, a 4DCT dataset, and the patient's actual breathing curve for each individual fraction. For five lung patients, three-dimensional-conformal radiation therapy (3D-CRT) and volumetric modulated arc treatment (VMAT) treatment plans were generated on four different CT image datasets: a native free-breathing 3DCT, an average intensity projection (AIP) and a maximum intensity projection (MIP) CT both obtained from a 4DCT, and a 3DCT with density overrides based on the 3DCT (DO). The Monte Carlo 4D dose has been calculated on each 4DCT phase using the Linac's log file and the patient's breathing trace as a surrogate for tumor motion and dose was accumulated to the gross tumor volume (GTV) at the 50% breathing phase (end of exhale) using deformable image registration.

RESULTS

Δ D 98 % and Δ D 2 % between 4D dose and planned dose differed largely for 3DCT-based planning and also for DO in three patients. Least dose differences between planned and recalculated dose have been found for AIP and MIP treatment planning which both tend to be superior to DO, but the results indicate a dependency on the breathing variability, tumor motion, and size. An interplay effect has not been observed in the small patient cohort.

CONCLUSIONS

We have developed a workflow which, to our best knowledge, is the first incorporation of the patient breathing trace over the course of all individual treatment fractions with the Linac's log file information and 4D Monte Carlo recalculations of the actual treated dose. Due to the small patient cohort, no clear recommendation on which CT can be used for SBRT treatment planning can be given, but the developed workflow, after adaption for clinical use, could be used to enhance a priori 4D Monte Carlo treatment planning in the future and help with the decision on which CT dataset treatment planning should be carried out.

Simulation of dosimetry impact of 4DCT uncertainty in 4D dose calculation for lung SBRT

Background

Due to the heterogeneity of patient’s individual respiratory motion pattern in lung stereotactic body radiotherapy (SBRT), treatment planning dose assessment using a traditional four-dimensional computed tomography (4DCT_traditional) images based on a uniform breathing curve may not represent the true treatment dose delivered to the patient. The purpose of this study was to evaluate the accumulated dose discrepancy between based on the 4DCT_traditional and true 4DCT (4DCT_true) that incorporated with the patient’s real entire breathing motion. The study also explored a novel 4D robust planning strategy to compensate for such heterogeneity respiratory motion uncertainties.

Methods

Simulated and measured patient specific breathing curves were used to generate 4D targets motion CT images. Volumetric-modulated arc therapy (VMAT) was planned using two arcs. Accumulated dose was obtained by recalculating the plan dose on each individual phase image and then deformed the dose from each phase image to the reference image. The “4 D dose” (D^4D) and “true dose” (D^true) were the accumulated dose based on the 4DCT_traditional and 4DCT_true respectively. The average worse case dose discrepancy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta D} $$\end{document}ΔD¯) between D^4D and D^true in all treatment fraction was calculated to evaluate dosimetric /planning parameters and correlate them with the heterogeneity of respiratory-induced motion patterns. A novel 4D robust optimization strategy for VMAT (4D Ro-VMAT) based on the probability density function(pdf) of breathing curve was proposed to improve the target coverage in the presence of heterogeneity respiratory motion. The data were assessed with a paired t-tests.

Results

With increasing breathing amplitude from 5 to 20 mm, target \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{99}} $$\end{document}ΔD99¯, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{95}} $$\end{document}ΔD95¯ increased from 1.59,1.39 to 10.15%,8.66% respectively. When the standard deviation of breathing amplitude increased from 15 to 35% of the mean amplitude, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{99}} $$\end{document}ΔD99¯, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{95}} $$\end{document}ΔD95¯ increased from 4.06,3.48 to 10.25%,6.63% respectively. The 4D Ro-VMAT plan significantly improve the target dose compared to VMAT plan.

Conclusion

When the breathing curve amplitude is more than 10 mm and standard deviation of amplitude is higher than 25% of mean amplitude, special care is needed to choose an appropriated dose accumulation approach to evaluate lung SBRT plan target coverage robustness. The proposed 4D Ro_VMAT strategy based on the pdf of patient specific breathing curve could effectively compensate such uncertainties.

Improved Woodcock tracking on Monte Carlo simulations for medical applications

This paper presents a new variance reduction technique called super voxel Woodcock (SVW), which combines Woodcock tracking technique with the super voxel concept, used in computer graphics. It consists in grouping the voxels of the volume in a super voxel grid (pre-processing step) by associating to each of the super voxels a local value of the most attenuate medium which will later serve to the interaction distances sampling. SVW allows reducing the sampling of the particle path while a high-density material is present within the simulated phantom. In order to evaluate the performance of the SVW method compare to both standard and Woodcock tracking methods, algorithms were implemented within the same GPU MCS framework GGEMS. This method improves the performance of the standard Woodcock method by a factor of 4.5 and 4.3 for x-ray imaging application and intraoperative radiotherapy respectively. The proposed SVW method did not introduce any bias on the simulations.

A Monte-Carlo-based and GPU-accelerated 4D-dose calculator for a pencil beam scanning proton therapy system

PURPOSE

The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose-accumulation methods. This work presents a clinically useful 4D-dose calculator that addresses each of these shortcomings.

METHODS

To quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in-house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose-grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose-accumulation technique.

RESULTS

Four-dimensional doses were successfully computed for the three test cases with computation times ranging from 4-6 min on a server with eight NVIDIA Titan X graphics cards; the most time-consuming component was the MC dose engine. The subvoxel-based dose-accumulation technique produced stable 4D-dose distributions at subvoxel scales of 0.5-1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose-volume histograms for these cases; the variations were reduced by implementing repainting or phase-gating motion mitigation techniques in the calculator.

CONCLUSIONS

A MC-based and GPU-accelerated 4D-dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D-robust optimization system.

Introduction of a pseudo demons force to enhance deformation range for robust reconstruction of super-resolution time-resolved 4DMRI

PURPOSE

The purpose of this study was to enhance the deformation range of demons-based deformable image registration (DIR) for large respiration-induced organ motion in the reconstruction of time-resolved four-dimensional magnetic resonance imaging (TR-4DMRI) for multi-breath motion simulation.

METHODS

A demons-based DIR algorithm was modified to enhance the deformation range for TR-4DMRI reconstruction using the super-resolution approach. A pseudo demons force was introduced to accelerate the coarse deformation in a multi-resolution (n = 3) DIR approach. The intensity gradient of a voxel was applied to its neighboring (5 × 5 × 5) voxels with a weight of Gaussian probability profile (σ = 1 voxel) to extend the demons force, especially on those voxels that have little intensity gradience but high-intensity difference. A digital 4DMRI phantom with 3-8 cm diaphragmatic motions was used for DIR comparison. Six volunteers were scanned with two high-resolution (highR: 2 × 2 × 2 mm³ ) breath-hold (BH) 3DMR images at full inhalation (BHI) and full exhalation (BHE) and low-resolution (lowR: 5 × 5 × 5 mm³ ) free-breathing (FB) 3DMR cine images (2 Hz) under an IRB-approved protocol. A cross-consistency check (CCC) (BHI→FB←BHE), with voxel intensity correlation (VIC) and inverse consistency error (ICE), was introduced for cross-verification of TR-4DMRI reconstruction.

RESULTS

Using the digital phantom, the maximum deformable magnitude is doubled using the modified DIR from 3 to 6 cm at the diaphragm. In six human subjects, the first 15-iteration DIR using the pseudo force deforms 200 ± 150% more than the original force, and succeeds in all 12 cases, whereas the original demons-based DIR failed in 67% of tested cases. Using the pseudo force, high VIC (>0.9) and small ICE (1.6 ± 0.6 mm) values are observed for DIR of BHI&BHE, BHI→FB, and BHE→FB. The CCC identifies four questionable cases, in which two cases need further DIR refinement, without missing true negative.

CONCLUSIONS

The introduction of a pseudo demons force enhances the largest deformation magnitude up to 6 cm. The cross-consistency check ensures the quality of TR-4DMRI reconstruction. Further investigation is ongoing to fully characterize TR-4DMRI for potential multi-breathing-cycle radiotherapy simulation.

Evaluation of normal lung tissue complication probability in gated and conventional radiotherapy using the 4D XCAT digital phantom

PURPOSE

The present study was conducted to investigate normal lung tissue complication probability in gated and conventional radiotherapy (RT) as a function of diaphragm motion, lesion size, and its location using 4D-XCAT digital phantom in a simulation study.

MATERIALS AND METHODS

Different time series of 3D-CT images were generated using the 4D-XCAT digital phantom. The binary data obtained from this phantom were then converted to the digital imaging and communication in medicine (DICOM) format using an in-house MATLAB-based program to be compatible with our treatment planning system (TPS). The 3D-TPS with superposition computational algorithm was used to generate conventional and gated plans. Treatment plans were generated for 36 different XCAT phantom configurations. These included four diaphragm motions of 20, 25, 30 and 35 mm, three lesion sizes of 3, 4, and 5 cm in diameter and each tumor was placed in four different lung locations (right lower lobe, right upper lobe, left lower lobe and left upper lobe). The complication of normal lung tissue was assessed in terms of mean lung dose (MLD), the lung volume receiving ≥20 Gy (V20), and normal tissue complication probability (NTCP).

RESULTS

The results showed that the gated RT yields superior outcomes in terms of normal tissue complication compared to the conventional RT. For all cases, the gated radiation therapy technique reduced the mean dose, V20, and NTCP of lung tissue by up to 5.53 Gy, 13.38%, and 23.89%, respectively.

CONCLUSIONS

The results of this study showed that the gated RT provides significant advantages in terms of the normal lung tissue complication, compared to the conventional RT, especially for the lesions near the diaphragm.

Influence of deformable image registration on 4D dose simulation for extracranial SBRT: A multi-registration framework study

BACKGROUND AND PURPOSE

To evaluate the influence of deformable image registration approaches on correspondence model-based 4D dose simulation in extracranial SBRT by means of open source deformable image registration (DIR) frameworks.

MATERIAL AND METHODS

Established DIR algorithms of six different open source DIR frameworks were considered and registration accuracy evaluated using freely available 4D image data. Furthermore, correspondence models (regression-based correlation of external breathing signal measurements and internal structure motion field) were built and model accuracy evaluated. Finally, the DIR algorithms were applied for motion field estimation in radiotherapy planning 4D CT data of five lung and five liver lesion patients, correspondence model formation, and model-based 4D dose simulation. Deviations between the original, statically planned and the 4D-simulated VMAT dose distributions were analyzed and correlated to DIR accuracy differences.

RESULTS

Registration errors varied among the DIR approaches, with lower DIR accuracy translating into lower correspondence modeling accuracy. Yet, for lung metastases, indices of 4D-simulated dose distributions widely agreed, irrespective of DIR accuracy differences. In contrast, liver metastases 4D dose simulation results strongly vary for the different DIR approaches.

CONCLUSIONS

Especially in treatment areas with low image contrast (e.g. the liver), DIR-based 4D dose simulation results strongly depend on the applied DIR algorithm, drawing resulting dose simulations and indices questionable.

The relative accuracy of 4D dose accumulation for lung radiotherapy using rigid dose projection versus dose recalculation on every breathing phase

PURPOSE

To investigate the accuracy of 4D dose accumulation using projection of dose calculated on the end-exhalation, mid-ventilation, or average intensity breathing phase CT scan, versus dose accumulation performed using full Monte Carlo dose recalculation on every breathing phase.

METHODS

Radiotherapy plans for 10 patients with stage I-II lung cancer were analyzed. All patients had respiratory-correlated computed tomography (4D-CT) performed as part of an IRB-approved research protocol. Stereotactic body radiotherapy (SBRT) plans were optimized using the dose calculated by a commercially available Monte Carlo algorithm on the end-exhalation 4D-CT phase. 4D dose accumulations using deformable registration were performed with a commercially available tool that projected the planned dose onto every breathing phase without recalculation, as well as with a Monte Carlo recalculation of the dose on all breathing phases. The 3D planned dose (3D-EX), the 3D dose calculated on the average intensity image (3D-AVE), and the 4D accumulations of the dose calculated on the end-exhalation phase CT (4D-PR-EX), the mid-ventilation phase CT (4D-PR-MID), and the average intensity image (4D-PR-AVE), respectively, were compared against the accumulation of the Monte Carlo dose recalculated on every phase. Plan evaluation metrics relating to target volumes and critical structures relevant for lung SBRT were analyzed.

RESULTS

Plan evaluation metrics tabulated using 4D-PR-EX, 4D-PR-MID, and 4D-PR-AVE differed from those tabulated using Monte Carlo recalculation on every phase by an average of 0.14 ± 0.70 Gy, -0.11 ± 0.51 Gy, and 0.00 ± 0.62 Gy, respectively. Plan evaluation metrics calculated from 3D-EX and 3D-AVE were acceptably accurate for target volumes and most critical structures, however, deviations of between 8 and 13 Gy were observed for the proximal bronchial trees of three patients.

CONCLUSIONS

The accuracy of 4D dose accumulated by projecting the dose calculated on the end-exhale, mid-ventilation, and average intensity images has been presented for 10 lung cancer SRBT plans. These methods involving projection without recalculation may be sufficiently accurate compared to 4D dose accumulated from Monte Carlo recalculation on every phase, depending on institutional protocols. Projection of the dose calculated on the mid-ventilation scan was found to be statistically significantly more accurate than projection of the dose calculated on end-exhalation when considering target volume dose metrics. Use of 4D dose accumulation should be considered when evaluating normal tissue complication probabilities as well as in clinical situations where target volumes are directly inferior to mobile critical structures.

Effect of deformable registration uncertainty on lung SBRT dose accumulation

PURPOSE

Deformable image registration (DIR) plays an important role in dose accumulation, such as incorporating breathing motion into the accumulation of the delivered dose based on daily 4DCBCT images. However, it is not yet well understood how the uncertainties associated with DIR methods affect the dose calculations and resulting clinical metrics. The purpose of this study is to evaluate the impact of DIR uncertainty on the clinical metrics derived from its use in dose accumulation.

METHODS

A biomechanical model based DIR method and a biomechanical-intensity-based hybrid method, which reduced the average registration error by 1.6 mm, were applied to ten lung cancer patients. A clinically relevant dose parameter [minimum dose to 0.5 cm(3) (Dmin)] was calculated for three dose scenarios using both algorithms. Dose scenarios included static (no breathing motion), predicted (breathing motion at the time of planning), and total accumulated (interfraction breathing motion). The relationship between the dose parameter and a combination of DIR uncertainty metrics, tumor volume, and dose heterogeneity of the plan was investigated.

RESULTS

Depending on the dose heterogeneity, tumor volume, and DIR uncertainty, in over 50% of the patients, differences greater than 1.0 Gy were observed in the Dmin of the tumor in the static dose calculation on exhale phase of the 4DCT. Such differences were due to the errors in propagating the tumor contours from the reference planning 4DCT phase onto a subsequent 4DCT phase using each DIR algorithm and calculating the dose on that phase. The differences in predicted dose were more subtle when breathing motion was modeled explicitly at the time of planning with only one patient exhibiting a greater than 1.0 Gy difference in Dmin. Dmin differences of up to 2.5 Gy were found in the total accumulated delivered dose due to difference in quantifying the interfraction variations. Such dose uncertainties could potentially be clinically significant.

CONCLUSIONS

Reductions in average uncertainty in DIR algorithms by 1.6 mm may have a clinically significant impact on the decision-making metrics used in dose planning and dose accumulation assessment.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI = 58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI = 69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC = 0.58, q-value = 0.33), while the differentiation was significant with other textures (AUC = 0.71-0.73, p < 0.009). Among the deformable algorithms, fast-demons (AUC = 0.68-0.70, q-value < 0.03) and fast-free-form (AUC = 0.69-0.74, q-value < 0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC = 0.72-0.78, q-value < 0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

Impact of temporal probability in 4D dose calculation for lung tumors

The purpose of this study was to evaluate the dosimetric uncertainty in 4D dose calculation using three temporal probability distributions: uniform distribution, sinusoidal distribution, and patient‐specific distribution derived from the patient respiratory trace. Temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. Four‐dimensional computed tomography (4D CT), along with deformable image registration, was used to compute 4D dose incorporating the patient's respiratory motion. First, the dose of each of 10 phase CTs was computed using the same planning parameters as those used in 3D treatment planning based on the breath‐hold CT. Next, deformable image registration was used to deform the dose of each phase CT to the breath‐hold CT using the deformation map between the phase CT and the breath‐hold CT. Finally, the 4D dose was computed by summing the deformed phase doses using their corresponding temporal probabilities. In this study, 4D dose calculated from the patient‐specific temporal probability distribution was used as the ground truth. The dosimetric evaluation matrix included: 1) 3D gamma analysis, 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20. For seven out of nine patients, both uniform and sinusoidal temporal probability dose distributions were found to have an average gamma passing rate >95% for both the lung and PTV regions. Compared with 4D dose calculated using the patient respiratory trace, doses using uniform and sinusoidal distribution showed a percentage difference on average of −0.1%±0.6% and −0.2%±0.4% in MTD, −0.2%±1.9% and −0.2%±1.3% in MLD, 0.09%±2.8% and −0.07%±1.8% in lung V20, −0.1%±2.0% and 0.08%±1.34% in lung V10, 0.47%±1.8% and 0.19%±1.3% in lung V5, respectively. We concluded that four‐dimensional dose computed using either a uniform or sinusoidal temporal probability distribution can approximate four‐dimensional dose computed using the patient‐specific respiratory trace.

PACS number: 87.55.D‐

The Importance of Quasi-4D Path-Integrated Dose Accumulation for More Accurate Risk Estimation in Stereotactic Liver Radiotherapy

Intrafraction organ deformation may be accounted for by inclusion of temporal information in dose calculation models. In this article, we demonstrate a quasi-4-dimensional method for improved risk estimation. Conventional 3-dimensional and quasi-4-dimensional calculations employing dose warping for dose accumulation were undertaken for patients with liver metastases planned for 42 Gy in 6 fractions of stereotactic body radiotherapy. Normal tissue complication probabilities and stochastic risks for radiation-induced carcinogenesis and cardiac complications were evaluated for healthy peripheral structures. Hypothetical assessments of other commonly employed dose/fractionation schedules on normal tissue complication probability estimates were explored. Conventional 3-dimensional dose computation may result in significant under- or overestimation of doses to organ at risk. For instance, doses differ (on average) by 17% (σ = 14%) for the left kidney, by 14% (σ = 7%) for the right kidney, by 7% (σ = 9%) for the large bowel, and by 10% (σ = 14%) for the duodenum. Discrepancies in the excess relative risk range up to about 30%. The 3-dimensional approach was shown to result in cardiac complication risks underestimated by >20%. For liver stereotactic body radiotherapy, we have shown that conventional 3-dimensional dose calculation may significantly over-/underestimate dose to organ at risk (90%-120% of the 4-dimensional estimate for the mean dose and 20%-150% for D2%). Providing dose estimates that most closely represent the actual dose delivered will provide valuable information to improve our understanding of the dose response for partial volume irradiation using hypofractionated schedules. Excess relative risks of radiocarcinogenesis were shown to range up to approximately excess relative risk = 4 and the prediction thereof depends greatly on the use of either 3-dimensional or 4-dimensional methods (with corresponding results differing by tens of percent).

Imaging and dosimetric errors in 4D PET/CT-guided radiotherapy from patient-specific respiratory patterns: a dynamic motion phantom end-to-end study

Effective positron emission tomography / computed tomography (PET/CT) guidance in radiotherapy of lung cancer requires estimation and mitigation of errors due to respiratory motion. An end-to-end workflow was developed to measure patient-specific motion-induced uncertainties in imaging, treatment planning, and radiation delivery with respiratory motion phantoms and dosimeters. A custom torso phantom with inserts mimicking normal lung tissue and lung lesion was filled with [(18)F]FDG. The lung lesion insert was driven by six different patient-specific respiratory patterns or kept stationary. PET/CT images were acquired under motionless ground truth, tidal breathing motion-averaged (3D), and respiratory phase-correlated (4D) conditions. Target volumes were estimated by standardized uptake value (SUV) thresholds that accurately defined the ground-truth lesion volume. Non-uniform dose-painting plans using volumetrically modulated arc therapy were optimized for fixed normal lung and spinal cord objectives and variable PET-based target objectives. Resulting plans were delivered to a cylindrical diode array at rest, in motion on a platform driven by the same respiratory patterns (3D), or motion-compensated by a robotic couch with an infrared camera tracking system (4D). Errors were estimated relative to the static ground truth condition for mean target-to-background (T/Bmean) ratios, target volumes, planned equivalent uniform target doses, and 2%-2 mm gamma delivery passing rates. Relative to motionless ground truth conditions, PET/CT imaging errors were on the order of 10-20%, treatment planning errors were 5-10%, and treatment delivery errors were 5-30% without motion compensation. Errors from residual motion following compensation methods were reduced to 5-10% in PET/CT imaging, <5% in treatment planning, and <2% in treatment delivery. We have demonstrated that estimation of respiratory motion uncertainty and its propagation from PET/CT imaging to RT planning, and RT delivery under a dose painting paradigm is feasible within an integrated respiratory motion phantom workflow. For a limited set of cases, the magnitude of errors was comparable during PET/CT imaging and treatment delivery without motion compensation. Errors were moderately mitigated during PET/CT imaging and significantly mitigated during RT delivery with motion compensation. This dynamic motion phantom end-to-end workflow provides a method for quality assurance of 4D PET/CT-guided radiotherapy, including evaluation of respiratory motion compensation methods during imaging and treatment delivery.

Patient-specific quantification of respiratory motion-induced dose uncertainty for step-and-shoot IMRT of lung cancer

PURPOSE

The objective of this study was to quantify respiratory motion-induced dose uncertainty at the planning stage for step-and-shoot intensity-modulated radiation therapy (IMRT) using an analytical technique.

METHODS

Ten patients with stage II∕III lung cancer who had undergone a planning four-dimensional (4D) computed tomographic scan and step-and-shoot IMRT planning were selected with a mix of motion and tumor size for this retrospective study. A step-and-shoot IMRT plan was generated for each patient. The maximum and minimum doses with respiratory motion were calculated for each plan, and the mean deviation from the 4D dose was calculated, taking delivery time, fractionation, and patient breathing cycle into consideration.

RESULTS

For all patients evaluated in this study, the mean deviation from the 4D dose in the planning target volume (PTV) was <2.5%, with a standard deviation <1.2%, and maximum point dose variation from the 4D dose was <6.2% in the PTV assuming delivery dose rate of 200 MU∕min and patient breathing cycle of 8 s. The motion-induced dose uncertainty is a function of motion, fractionation, MU (plan modulation), dose rate, and patient breathing cycle.

CONCLUSIONS

Respiratory motion-induced dose uncertainty varies from patient to patient. Therefore, it is important to evaluate the dose uncertainty on a patient-specific basis, which could be useful for plan evaluation and treatment strategy determination for selected patients.

Improving radiotherapy planning, delivery accuracy, and normal tissue sparing using cutting edge technologies

In the United States, more than half of all new invasive cancers diagnosed are non-small cell lung cancer, with a significant number of these cases presenting at locally advanced stages, resulting in about one-third of all cancer deaths. While the advent of stereotactic ablative radiation therapy (SABR, also known as stereotactic body radiotherapy, or SBRT) for early-staged patients has improved local tumor control to >90%, survival results for locally advanced stage lung cancer remain grim. Significant challenges exist in lung cancer radiation therapy including tumor motion, accurate dose calculation in low density media, limiting dose to nearby organs at risk, and changing anatomy over the treatment course. However, many recent technological advancements have been introduced that can meet these challenges, including four-dimensional computed tomography (4DCT) and volumetric cone-beam computed tomography (CBCT) to enable more accurate target definition and precise tumor localization during radiation, respectively. In addition, advances in dose calculation algorithms have allowed for more accurate dosimetry in heterogeneous media, and intensity modulated and arc delivery techniques can help spare organs at risk. New delivery approaches, such as tumor tracking and gating, offer additional potential for further reducing target margins. Image-guided adaptive radiation therapy (IGART) introduces the potential for individualized plan adaptation based on imaging feedback, including bulky residual disease, tumor progression, and physiological changes that occur during the treatment course. This review provides an overview of the current state of the art technology for lung cancer volume definition, treatment planning, localization, and treatment plan adaptation.

A mass-conserving 4D XCAT phantom for dose calculation and accumulation

PURPOSE

The XCAT phantom is a realistic 4D digital torso phantom that is widely used in imaging and therapy research. However, lung mass is not conserved between respiratory phases of the phantom, making detailed dosimetric simulations and dose accumulation unphysical. A framework is developed to correct this issue by enforcing local mass conservation in the XCAT lung. Dose calculations are performed to assess the implications of neglecting mass conservation, and to demonstrate an application of the phantom to calculate the accumulated delivered dose in an irregularly breathing patient.

METHODS

A displacement vector field (DVF) between each respiratory state and a reference image is generated from the XCAT motion model and its divergence is calculated and used to correct the lung density. A series of phantoms with regular and irregular breathing (based on patient data) are generated and modified to conserve mass. Monte Carlo methods are used to simulate conventional and SBRT treatment delivery. The calculated dose is deformed and accumulated using the DVF. Results from the mass-conserving and original XCAT are compared. A 4DCT is simulated for the irregularly breathing patient, and a 4DCT-based dose estimate is compared with the accumulated delivered dose.

RESULTS

The presented framework successfully conserves mass in the XCAT lung. The spatial distribution of the lung dose was qualitatively changed by the use of a mass conservation in the XCAT; however, the corresponding DVH did not change significantly. The comparison of the delivered dose with the 4DCT-based prediction shows similar lung metric results, however dose differences of 10% can be seen in some spatial regions.

CONCLUSIONS

The XCAT phantom has been successfully modified so that it conserves lung mass during respiration, enabling it to be used as a tool to perform dose accumulation studies in the lung without relying on deformable image registration. Neglecting mass conservation can result in erroneous spatial distributions of the dose in the lung. Using this tool to simulate patient treatments reveals differences between the planned dose and the calculated delivered dose for the full treatment. The software is freely available from the authors.

A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs

We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.

Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data

Quantum noise is common in CT images and is a persistent problem in accurate ventilation imaging using 4D-CT and deformable image registration (DIR). This study focuses on the effects of noise in 4D-CT on DIR and thereby derived ventilation data. A total of six sets of 4D-CT data with landmarks delineated in different phases, called point-validated pixel-based breathing thorax models (POPI), were used in this study. The DIR algorithms, including diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B-spline, were used to register the inspiration phase to the expiration phase. The DIR deformation matrices (DIRDM) were used to map the landmarks. Target registration errors (TRE) were calculated as the distance errors between the delineated and the mapped landmarks. Noise of Gaussian distribution with different standard deviations (SD), from 0 to 200 Hounsfield Units (HU) in amplitude, was added to the POPI models to simulate different levels of quantum noise. Ventilation data were calculated using the ΔV algorithm which calculates the volume change geometrically based on the DIRDM. The ventilation images with different added noise levels were compared using Dice similarity coefficient (DSC). The root mean square (RMS) values of the landmark TRE over the six POPI models for the four DIR algorithms were stable when the noise level was low (SD <150 HU) and increased with added noise when the level is higher. The most accurate DIR was DD with a mean RMS of 1.5 ± 0.5 mm with no added noise and 1.8 ± 0.5 mm with noise (SD = 200 HU). The DSC values between the ventilation images with and without added noise decreased with the noise level, even when the noise level was relatively low. The DIR algorithm most robust with respect to noise was DM, with mean DSC = 0.89 ± 0.01 and 0.66 ± 0.02 for the top 50% ventilation volumes, as compared between 0 added noise and SD = 30 and 200 HU, respectively. Although the landmark TRE were stable with low noise, the differences between ventilation images increased with noise level, even when the noise was low, indicating ventilation imaging from 4D-CT was sensitive to image noise. Therefore, high quality 4D-CT is essential for accurate ventilation images.

Four-dimensional dose evaluation using deformable image registration in radiotherapy for liver cancer

PURPOSE

In order to evaluate the dosimetric impact of respiratory motion on the dose delivered to the target volume and critical organs during free-breathing radiotherapy, a four-dimensional dose was evaluated using deformable image registration (DIR).

METHODS

Four-dimensional computed tomography (4DCT) images were acquired for 11 patients who were treated for liver cancer. Internal target volume-based treatment planning and dose calculation (3D dose) were performed using the end-exhalation phase images. The four-dimensional dose (4D dose) was calculated based on DIR of all phase images from 4DCT to the planned image. Dosimetric parameters from the 4D dose, were calculated and compared with those from the 3D dose.

RESULTS

There was no significant change of the dosimetric parameters for gross tumor volume (p > 0.05). The increase D(mean) and generalized equivalent uniform dose (gEUD) for liver were by 3.1% ± 3.3% (p = 0.003) and 2.8% ± 3.3% (p = 0.008), respectively, and for duodenum, they were decreased by 15.7% ± 11.2% (p = 0.003) and 15.1% ± 11.0% (p = 0.003), respectively. The D(max) and gEUD for stomach was decreased by 5.3% ± 5.8% (p = 0.003) and 9.7% ± 8.7% (p = 0.003), respectively. The D(max) and gEUD for right kidney was decreased by 11.2% ± 16.2% (p = 0.003) and 14.9% ± 16.8% (p = 0.005), respectively. For left kidney, D(max) and gEUD were decreased by 11.4% ± 11.0% (p = 0.003) and 12.8% ± 12.1% (p = 0.005), respectively. The NTCP values for duodenum and stomach were decreased by 8.4% ± 5.8% (p = 0.003) and 17.2% ± 13.7% (p = 0.003), respectively.

CONCLUSIONS

The four-dimensional dose with a more realistic dose calculation accounting for respiratory motion revealed no significant difference in target coverage and potentially significant change in the physical and biological dosimetric parameters in normal organs during free-breathing treatment.

Visualisation of respiratory tumour motion and co-moving isodose lines in the context of respiratory gating, IMRT and flattening-filter-free beams

Respiratory motion during percutaneous radiotherapy can be considered based on respiration-correlated computed tomography (4DCT). However, most treatment planning systems perform the dose calculation based on a single primary CT data set, even though cine mode displays may allow for a visualisation of the complete breathing cycle. This might create the mistaken impression that the dose distribution were independent of tumour motion. We present a movie visualisation technique with the aim to direct attention to the fact that the dose distribution migrates to some degree with the tumour and discuss consequences for gated treatment, IMRT plans and flattening-filter-free beams. This is a feasibility test for a visualisation of tumour and isodose motion. Ten respiratory phases are distinguished on the CT, and the dose distribution from a stationary IMRT plan is calculated on each phase, to be integrated into a movie of tumour and dose motion during breathing. For one example patient out of the sample of five lesions, the plan is compared with a gated treatment plan with respect to tumour coverage and lung sparing. The interplay-effect for small segments in the IMRT plan is estimated. While the high dose rate, together with the cone-shaped beam profile, makes the use of flattening-filter-free beams more problematic for conformal and IMRT treatment, it can be the option of choice if gated treatment is preferred. The different effects of respiratory motion, dose build-up and beam properties (segments and flatness) for gated vs. un-gated treatment can best be considered if planning is performed on the full 4DCT data set, which may be an incentive for future developments of treatment planning systems.

Four-dimensional tissue deformation reconstruction (4D TDR) validation using a real tissue phantom

Calculation of four‐dimensional (4D) dose distributions requires the remapping of dose calculated on each available binned phase of the 4D CT onto a reference phase for summation. Deformable image registration (DIR) is usually used for this task, but unfortunately almost always considers only endpoints rather than the whole motion path. A new algorithm, 4D tissue deformation reconstruction (4D TDR), that uses either CT projection data or all available 4D CT images to reconstruct 4D motion data, was developed. The purpose of this work is to verify the accuracy of the fit of this new algorithm using a realistic tissue phantom. A previously described fresh tissue phantom with implanted electromagnetic tracking (EMT) fiducials was used for this experiment. The phantom was animated using a sinusoidal and a real patient‐breathing signal. Four‐dimensional computer tomography (4D CT) and EMT tracking were performed. Deformation reconstruction was conducted using the 4D TDR and a modified 4D TDR which takes real tissue hysteresis (4D TDRHysteresis) into account. Deformation estimation results were compared to the EMT and 4D CT coordinate measurements. To eliminate the possibility of the high contrast markers driving the 4D TDR, a comparison was made using the original 4D CT data and data in which the fiducials were electronically masked. For the sinusoidal animation, the average deviation of the 4D TDR compared to the manually determined coordinates from 4D CT data was 1.9 mm, albeit with as large as 4.5 mm deviation. The 4D TDR calculation traces matched 95% of the EMT trace within 2.8 mm. The motion hysteresis generated by real tissue is not properly projected other than at endpoints of motion. Sinusoidal animation resulted in 95% of EMT measured locations to be within less than 1.2 mm of the measured 4D CT motion path, enabling accurate motion characterization of the tissue hysteresis. The 4D TDRHysteresis calculation traces accounted well for the hysteresis and matched 95% of the EMT trace within 1.6 mm. An irregular (in amplitude and frequency) recorded patient trace applied to the same tissue resulted in 95% of the EMT trace points within less than 4.5 mm when compared to both the 4D CT and 4D TDRHysteresis motion paths. The average deviation of 4D TDRHysteresis compared to 4D CT datasets was 0.9 mm under regular sinusoidal and 1.0 mm under irregular patient trace animation. The EMT trace data fit to the 4D TDRHysteresis was within 1.6 mm for sinusoidal and 4.5 mm for patient trace animation. While various algorithms have been validated for end‐to‐end accuracy, one can only be fully confident in the performance of a predictive algorithm if one looks at data along the full motion path. The 4D TDR, calculating the whole motion path rather than only phase‐ or endpoints, allows us to fully characterize the accuracy of a predictive algorithm, minimizing assumptions. This algorithm went one step further by allowing for the inclusion of tissue hysteresis effects, a real‐world effect that is neglected when endpoint‐only validation is performed. Our results show that the 4D TDRHysteresis correctly models the deformation at the endpoints and any intermediate points along the motion path.

PACS numbers: 87.55.km, 87.55.Qr, 87.57.nf, 87.85.Tu

Performance of independent dose calculation in helical tomotherapy: implementation of the MCSIM code

Currently, a software-based second check dose calculation for helical tomotherapy (HT) is not available. The goal of this study is to evaluate the dose calculation accuracy of the in-house software using EGS4/MCSIM Monte Carlo environment against the treatment planning system calculations. In-house software was used to convert HT treatment plan information into a non-helical format. The MCSIM dose calculation code was evaluated by comparing point dose calculations and dose profiles against those from the HT treatment plan. Fifteen patients, representing five treatment sites, were used in this comparison. Point dose calculations between the HT treatment planning system and the EGS4/MCSIM Monte Carlo environment had percent difference values below 5 % for the majority of this study. Vertical and horizontal planar profiles also had percent difference values below 5 % for the majority of this study. Down sampling was seen to improve speed without much loss of accuracy. EGS4/MCSIM Monte Carlo environment showed good agreement with point dose measurements, compared to the HT treatment plans. Vertical and horizontal profiles also showed good agreement. Significant time saving may be obtained by down-sampling beam projections. The dose calculation accuracy of the in-house software using the MCSIM code against the treatment planning system calculations was evaluated. By comparing point doses and dose profiles, the EGS4/MCSIM Monte Carlo environment was seen to provide an accurate independent dose calculation.

A novel technique to enable experimental validation of deformable dose accumulation

PURPOSE

To propose a novel technique to experimentally validate deformable dose algorithms by measuring 3D dose distributions under the condition of deformation using deformable gel dosimeters produced by a novel gel fabrication method.

METHOD

Five gel dosimeters, two rigid control gels and three deformable gels, were manufactured and treated with the same conformal plan that prescribed 400 cGy to the isocenter. The control gels were treated statically; the deformable gels were treated while being compressed by an actuation device to simulate breathing motion (amplitude of compression  = 1, 1.5, and 2 cm, respectively; frequency  =  16 rpm). Comparison between the dose measured by the control gels and the corresponding static dose distribution calculated in the treatment planning system (TPS) has determined the intrinsic dose measurement uncertainty of the gel dosimeters. Doses accumulated using MORFEUS, a biomechanical model-based deformable registration and dose accumulation algorithm, were compared with the doses measured by the deformable gel dosimeters to verify the accuracy of MORFEUS using dose differences at each voxel as well as the gamma index test. Flexible plastic wraps were used to contain and protect the deformable gels from oxygen infiltration, which inhibits the gels' dose sensitizing ability. Since the wraps were imperfect oxygen barrier, dose comparison between MORFEUS and the deformable gels was performed only in the central region with a received dose of 200 cGy or above to exclude the peripheral region where oxygen penetration had likely affected dose measurements.

RESULTS

Dose measured with the control gels showed that the intrinsic dose measurement uncertainty of the gel dosimeters was 11.8 cGy or 4.7% compared to the TPS. The absolute mean voxel-by-voxel dose difference between the accumulated dose and the dose measured with the deformable gels was 4.7 cGy (SD = 36.0 cGy) or 1.5% (SD = 13.4%) for the three deformable gels. The absolute mean vector distance between the 250, 300, 350, and 400 cGy isodose surfaces on the accumulated and measured distributions was 1.2 mm (SD < 1.5 mm). The gamma index test that used the dose measurement precision of the control gels as the dose difference criterion and 2 mm as the distance criterion was performed, and the average pass rate of the accumulated dose distributions for all three deformable gels was 92.7%. When the distance criterion was relaxed to 3 mm, the average pass rate increased to 96.9%.

CONCLUSION

This study has proposed a novel technique to manufacture deformable volumetric gel dosimeters. By comparing the doses accumulated in MORFEUS and the doses measured with the dosimeters under the condition of deformation, the study has also demonstrated the potential of using deformable gel dosimetry to experimentally validate algorithms that include deformations into dose computation. Since dose less than 200 cGy was not evaluated in this study, future investigations will focus more on low dose regions by either using bigger gel dosimeters or prescribing a lower dose to provide a more complete experimental validation of MORFEUS across a wider dose range.

A method to evaluate dose errors introduced by dose mapping processes for mass conserving deformations

PURPOSE

To present a method to evaluate the dose mapping error introduced by the dose mapping process. In addition, apply the method to evaluate the dose mapping error introduced by the 4D dose calculation process implemented in a research version of commercial treatment planning system for a patient case.

METHODS

The average dose accumulated in a finite volume should be unchanged when the dose delivered to one anatomic instance of that volume is mapped to a different anatomic instance-provided that the tissue deformation between the anatomic instances is mass conserving. The average dose to a finite volume on image S is defined as d(S)=e(s)/m(S), where e(S) is the energy deposited in the mass m(S) contained in the volume. Since mass and energy should be conserved, when d(S) is mapped to an image R(d(S→R)=d(R)), the mean dose mapping error is defined as Δd(m)=|d(R)-d(S)|=|e(R)/m(R)-e(S)/m(S)|, where the e(R) and e(S) are integral doses (energy deposited), and m(R) and m(S) are the masses within the region of interest (ROI) on image R and the corresponding ROI on image S, where R and S are the two anatomic instances from the same patient. Alternatively, application of simple differential propagation yields the differential dose mapping error, Δd(d)=|∂d∂e*Δe+∂d∂m*Δm|=|(e(S)-e(R))m(R)-(m(S)-m(R))m(R) (2)*e(R)|=α|d(R)-d(S)| with α=m(S)/m(R). A 4D treatment plan on a ten-phase 4D-CT lung patient is used to demonstrate the dose mapping error evaluations for a patient case, in which the accumulated dose, D(R)=∑(S=0) (9)d(S→R), and associated error values (ΔD(m) and ΔD(d)) are calculated for a uniformly spaced set of ROIs.

RESULTS

For the single sample patient dose distribution, the average accumulated differential dose mapping error is 4.3%, the average absolute differential dose mapping error is 10.8%, and the average accumulated mean dose mapping error is 5.0%. Accumulated differential dose mapping errors within the gross tumor volume (GTV) and planning target volume (PTV) are lower, 0.73% and 2.33%, respectively.

CONCLUSIONS

A method has been presented to evaluate the dose mapping error introduced by the dose mapping process. This method has been applied to evaluate the 4D dose calculation process implemented in a commercial treatment planning system. The method could potentially be developed as a fully-automatic QA method in image guided adaptive radiation therapy (IGART).

Manifold learning for image-based breathing gating in ultrasound and MRI

Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. We achieve the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. Since Laplacian eigenmaps assign to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking.