CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer

BACKGROUND

MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming manual contouring step. However, the segmentation label is available only for CT data in many cases.

PURPOSE

We propose CycleSeg, a unified framework that generates sCT and corresponding segmentation from MR images without access to MR segmentation labels METHODS: CycleSeg utilizes the CycleGAN formulation to perform unpaired synthesis of sCT and image alignment. To enable MR (sCT) segmentation, CycleSeg incorporates unsupervised domain adaptation by using a pseudo-labeling approach with feature alignment in semantic segmentation space. In contrast to previous approaches that perform segmentation on MR data, CycleSeg could perform segmentation on both MR and sCT. Experiments were performed with data from prostate cancer patients, with 78/7/10 subjects in the training/validation/test sets, respectively.

RESULTS

CycleSeg showed the best sCT generation results, with the lowest mean absolute error of 102.2 and the lowest Fréchet inception distance of 13.0. CycleSeg also performed best on MR segmentation, with the highest average dice score of 81.0 and 81.1 for MR and sCT segmentation, respectively. Ablation experiments confirmed the contribution of the proposed components of CycleSeg.

CONCLUSION

CycleSeg effectively synthesized CT and performed segmentation on MR images of prostate cancer patients. Thus, CycleSeg has the potential to expedite MR-only radiotherapy treatment planning, reducing the prescribed scans and manual segmentation effort, and increasing throughput.

A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery

Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884-0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning

Purpose

Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance.

Materials and methods

One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation.

Results

The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model.

Conclusions

The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.

Evaluating the effects of variation in CT scanning parameters on the image quality and Hounsfield units for optimization of dose in radiotherapy treatment planning: A semi-anthropomorphic thorax phantom study

Aim

The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients' treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS).

Materials and Methods

A quality dose verification phantom was scanned several times by a 16-slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant.

Results

Reconstruction kernels and algorithms significantly affected noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back-projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS--calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib.

Conclusions

Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients.

Image quality comparisons of coil setups in 3T MRI for brain and head and neck radiotherapy simulations

PURPOSE

MRI is increasingly used for brain and head and neck radiotherapy treatment planning due to its superior soft tissue contrast. Flexible array coils can be arranged to encompass treatment immobilization devices, which do not fit in diagnostic head/neck coils. Selecting a flexible coil arrangement to replace a diagnostic coil should rely on image quality characteristics and patient comfort. We compared image quality obtained with a custom UltraFlexLarge18 (UFL18) coil setup against a commercial FlexLarge4 (FL4) coil arrangement, relative to a diagnostic Head/Neck20 (HN20) coil at 3T.

METHODS

The large American College of Radiology (ACR) MRI phantom was scanned monthly in the UFL18, FL4, and HN20 coil setup over 2 years, using the ACR series and three clinical sequences. High-contrast spatial resolution (HCSR), image intensity uniformity (IIU), percent-signal ghosting (PSG), low-contrast object detectability (LCOD), signal-to-noise ratio (SNR), and geometric accuracy were calculated according to ACR recommendations for each series and coil arrangement. Five healthy volunteers were scanned with the clinical sequences in all three coil setups. SNR, contrast-to-noise ratio (CNR) and artifact size were extracted from regions-of-interest along the head for each sequence and coil setup. For both experiments, ratios of image quality parameters obtained with UFL18 or FL4 over those from HN20 were formed for each coil setup, grouping the ACR and clinical sequences.

RESULTS

Wilcoxon rank-sum tests revealed significantly higher (p < 0.001) LCOD, IIU and SNR, and lower PSG ratios with UFL18 than FL4 on the phantom for the clinical sequences, with opposite PSG and SNR trends for the ACR series. Similar statistical tests on volunteer data corroborated that SNR ratios with UFL18 (0.58 ± 0.19) were significantly higher (p < 0.001) than with FL4 (0.51 ± 0.18) relative to HN20.

CONCLUSIONS

The custom UFL18 coil setup was selected for clinical application in MR simulations due to the superior image quality demonstrated on a phantom and volunteers for clinical sequences and increased volunteer comfort.

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy

Objective. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve the rigor of input feature design in a DL-FMP model by examining how different designs of input features influence model prediction performance.Approach. This study included 231 head-and-neck intensity-modulated radiation therapy patients. Three input feature designs were investigated. The first design (D1) assumed that information of all critical structures from all beam angles should be combined to predict fluence maps. The second design (D2) assumed that local anatomical information was sufficient for predicting radiation intensity of a beamlet at a respective beam angle. The third design (D3) assumed the need for both local anatomical information and inter-beam modulation to predict radiation intensity values of the beamlets that intersect at a voxel. For each input design, we tailored the DL model accordingly. All models were trained using the same set of ground truth plans (GT plans). The plans generated by DL models (DL plans) were analyzed using key dose-volume metrics. One-way ANOVA with multiple comparisons correction (Bonferroni method) was performed (significance level = 0.05).Main results. For PTV-related metrics, all DL plans had significantly higher maximum dose (p < 0.001), conformity index (p < 0.001), and heterogeneity index (p < 0.001) compared to GT plans, with D2 being the worst performer. Meanwhile, except for cord+5 mm (p < 0.001), DL plans of all designs resulted in OAR dose metrics that are comparable to those of GT plans.Significance. Local anatomical information contains most of the information that DL models need to predict fluence maps for clinically acceptable OAR sparing. Input features from beam angles are needed to achieve the best PTV coverage. These results provide valuable insights for further improvement of DL-FMP models and DL models in general.

Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer

PURPOSE

Deep learning-based knowledge-based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high-quality plans. This paper presents a novel KBP model using an attention-gating mechanism and a three-dimensional (3D) U-Net for intensity-modulated radiation therapy (IMRT) 3D dose distribution prediction in head-and-neck cancer.

METHODS

A total of 340 head-and-neck cancer plans, representing the OpenKBP-2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention-gated 3D U-Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean-squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U-Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning-based dose prediction models.

RESULTS

The proposed attention-gated 3D U-Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground-truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U-Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U-Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U-Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U-Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U-Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U-Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U-Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U-Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U-Net) in the body contour. The average difference in predicting the D₉₉ value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the D m a x ${D_{max}}$ (brainstem, spinal cord, and mandible) and D m e a n ${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground-truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground-truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real-time applications.

CONCLUSIONS

The attention-gated 3D U-Net model demonstrated a capability in predicting accurate 3D dose distributions for head-and-neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U-Net model, and it was also competitive to the performance of the best state-of-the-art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision-making before planning, quality assurance of planning, and guiding-automated planning for improved plan consistency, quality, and planning efficiency.

Assessment of the effects of different dental restorative materials on radiotherapy dose distribution: A phantom study

Background

One of the most specific effects of high-density dental restorative materials on head & neck cancer radiotherapy is generating variations on isodose distributions. These variations might have an impact on the accuracy and effectiveness of the radiation treatment. The aim of this study is investigating the possible dosimetric effect of six different restorative materials on isodose distributions in head & neck radiotherapy planning process.

Materials and Methods

A special phantom was developed and twenty-one caries-free human third molars (a control group + six different restorative materials) were used for the measurements. After acquiring the computed tomography (CT) images, seven treatment plans were created. Hounsfield Unit (HU) numbers, horizontal line dose profile (HLDP) and vertical line dose profiles (VLDPs) were compared with the control group.

Results

The amalgam sample deformed the HU numbers in CT images. The median HU value for the S4 material was considerably different than the other samples. The median values were quite close for the remaining samples. For the amalgam sample, the mean of the calculated median isodose values for HLDP and VLDP at 3.5 cm away from the isocenter line were lower than the mean of the control group 4.03% and 6.94%, respectively (for HLDP with tooth numbers of 36 and 38 P = 0.025 and P < 0.001, respectively; for VLDP P < 0.001). In C-S1 comparison results, the statistically significant differences were found for the measurement point at 1 cm away from the isocenter (P = 0.037, P = 0.002, and P = 0.018 for the tooth numbers 36, 37, and 38, respectively). In C-S2 and C-S6 comparisons, there was a statistically significant difference for tooth number 36 (P = 0.035 and P = 0.003, respectively).

Conclusions

The findings of the present study showed that amalgam should not be used in head & neck cancer patients who are planned to have radiation therapy. A high viscosity glass ionomer cement (GIC) and a ceramic reinforced GIC sample can be used instead of amalgam to minimize the distorting effect on isodose distributions.

Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning

Purpose.We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors.Methods.From the modeling perspective, the DL model's output is the fluence maps of IMRT plans. However, from the clinical planning perspective, the plan quality evaluation should be based on the clinical dosimetric criteria such as dose-volume histograms. To account for the complex and non-intuitive relationships between fluence map prediction errors and the corresponding dose distribution changes, we propose a novel error analysis approach that systematically examines plan dosimetric changes that are induced by varying amounts of fluence prediction errors. We investigated four decomposition modes of model prediction errors. The two spatial domain decompositions are based on fluence intensity and fluence gradient. The two frequency domain decompositions are based on Fourier-space banded frequency rings and Fourier-space truncated low-frequency disks. The decomposed error was analyzed for its impact on the resulting plans' dosimetric metrics. The analysis was conducted on 15 test cases spared from the 200 training and 16 validation cases used to train the model.Results.Most planning target volume metrics were significantly correlated with most error decompositions. The Fourier space disk radii had the largest Spearman's coefficients. The low-frequency region within a disk of ∼20% Fourier space contained most of errors that impact overall plan quality.Conclusions.This study demonstrates the feasibility of using fluence map prediction error analysis to understand the AI agent's performance. Such insights will help fine-tune the DL models in architecture design and loss function selection.

Variations in Head and Neck Treatment Plan Quality Assessment Among Radiation Oncologists and Medical Physicists in a Single Radiotherapy Department

Background

Agreement between planners and treating radiation oncologists (ROs) on plan quality criteria is essential for consistent planning. Differences between ROs and planning medical physicists (MPs) in perceived quality of head and neck cancer plans were assessed.

Materials and Methods

Five ROs and four MPs scored 65 plans for in total 15 patients. For each patient, the clinical (CLIN) plan and two or four alternative plans, generated with automated multi-criteria optimization (MCO), were included. There was always one MCO plan aiming at maximally adhering to clinical plan requirements, while the other MCO plans had a lower aimed quality. Scores were given as follows: 1-7 and 1-2, not acceptable; 3-5, acceptable if further planning would not resolve perceived weaknesses; and 6-7, straightway acceptable. One MP and one RO repeated plan scoring for intra-observer variation assessment.

Results

For the 36 unique observer pairs, the median percentage of plans for which the two observers agreed on a plan score (100% = 65 plans) was 27.7% [6.2, 40.0]. In the repeat scoring, agreements between first and second scoring were 52.3% and 40.0%, respectively. With a binary division between unacceptable (scores 1 and 2) and acceptable (3-7) plans, the median inter-observer agreement percentage was 78.5% [63.1, 86.2], while intra-observer agreements were 96.9% and 86.2%. There were no differences in observed agreements between RO-RO, MP-MP, and RO-MP pairs. Agreements for the highest-quality, automatically generated MCO plans were higher than for the CLIN plans.

Conclusions

Inter-observer differences in plan quality scores were substantial and could result in inconsistencies in generated treatment plans. Agreements among ROs were not better than between ROs and MPs, despite large differences in training and clinical role. High-quality automatically generated plans showed the best score agreements.

Potential Morbidity Reduction for Lung Stereotactic Body Radiation Therapy Using Respiratory Gating

Simple Summary

Lung stereotactic body radiotherapy (SBRT) is the standard of care for early-stage lung cancer and oligometastases. For SBRT, motion has to be considered to avoid misdosage. Respiratory phase gating, meaning to irradiate the target volume only in a predefined gating motion phase window, can be applied to mitigate motion-induced effects. The aim of this study was to exploit the clinical benefit of gating for lung SBRT. For the majority of 14 lung tumor patients and various gating windows, we could prove a reduced dose to normal tissue by gating simulation. A normal tissue complication probability (NTCP) model analysis revealed a major reduction of normal tissue toxicity for moderate gating window sizes. The most beneficial effect of gating was found for those patients with the highest prior toxicity risk. The presented results are useful for personalized risk assessment prior to treatment and may help to select patients and optimal gating windows.

Abstract

We investigated the potential of respiratory gating to mitigate the motion-caused misdosage in lung stereotactic body radiotherapy (SBRT). For fourteen patients with lung tumors, we investigated treatment plans for a gating window (GW) including three breathing phases around the maximum exhalation phase, GW40–60. For a subset of six patients, we also assessed a preceding three-phase GW20–40 and six-phase GW20–70. We analyzed the target volume, lung, esophagus, and heart doses. Using normal tissue complication probability (NTCP) models, we estimated radiation pneumonitis and esophagitis risks. Compared to plans without gating, GW40–60 significantly reduced doses to organs at risk without impairing the tumor doses. On average, the mean lung dose decreased by 0.6 Gy (p < 0.001), treated lung V20Gy by 2.4% (p = 0.003), esophageal dose to 5cc by 2.0 Gy (p = 0.003), and maximum heart dose by 3.2 Gy (p = 0.009). The model-estimated mean risks of 11% for pneumonitis and 12% for esophagitis without gating decreased upon GW40–60 to 7% and 9%, respectively. For the highest-risk patient, gating reduced the pneumonitis risk from 43% to 32%. Gating is most beneficial for patients with high-toxicity risks. Pre-treatment toxicity risk assessment may help optimize patient selection for gating, as well as GW selection for individual patients.

Knowledge-based radiation treatment planning: A data-driven method survey

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy

Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.

Quadratic relation for mass density calibration in human body using dual-energy CT data

PURPOSE

To derive the mass density (ρ) from dual-energy computed tomography (DECT) data by calibrating electron density (ρ_e ) and effective atomic numbers (Z_eff ) of human tissues.

METHODS

We propose the DEEDZ-MD method, in which a single polynomial parameterization covers the entire human-tissue range to establish an empirical quadratic relation between the atomic number-to-mass ratio and Z_eff . Then, we numerically evaluate the DEEDZ-MD method in reference human tissues listed in the ICRP Publication 110 and ICRU Report 46. The tissues are considered to have unknown ρ values. The attenuation coefficients of these tissues are calculated using the XCOM Photon Cross Sections Database. The DEEDZ-MD method is also applied to experimental DECT data acquired from a tissue characterization phantom and an anthropomorphic phantom at 90 kV and 150 kV/Sn.

RESULTS

The numerical analysis of the DEEDZ-MD method reveals a single quadratic relation between the atomic number-to-mass ratio and Z_eff in a wide range of human tissues. The simulated ρ values are in excellent agreement with the reference values over ρ values from 0.260 (lung) to 3.225 (hydroxyapatite). The relative deviations from the reference ρ remain within ±0.6% for all the reference human tissues, except for the eye lens (approximate deviation of -1.0%). The overall root-mean-square error is 0.24%. The application of the DEEDZ-MD method to experimental dual-energy CT data confirms this agreement within experimental accuracy, indicating the practical feasibility of the method. The DEEDZ-MD method enables the generation of ρ images with less image noise than the existing DECT-based conversion of ρ from ρ_e and with fewer beam-hardening artifacts than conventional single-energy CT images.

CONCLUSIONS

The DEEDZ-MD method can facilitate the generation of ρ images from dual-energy CT data without relying on the nontrivial segmentation of different tissues.

Accuracy of surface-guided patient setup for conventional radiotherapy of brain and nasopharynx cancer

Purpose

To evaluate the accuracy of surface‐guided radiotherapy (SGRT) in cranial patient setup by direct comparison between optical surface imaging (OSI) and cone‐beam computed tomography (CBCT), before applying SGRT‐only setup for conventional radiotherapy of brain and nasopharynx cancer.

Methods and Materials

Using CBCT as reference, SGRT setup accuracy was examined based on 269 patients (415 treatments) treated with frameless cranial stereotactic radiosurgery (SRS) during 2018‐2019. Patients were immobilized in customized head molds and open‐face masks and monitored using OSI during treatment. The facial skin area in planning CT was used as OSI region of interest (ROI) for automatic surface alignment and the skull was used as the landmark for automatic CBCT/CT registration. A 6 degrees of freedom (6DOF) couch was used. Immediately after CBCT setup, an OSI verification image was captured, recording the SGRT setup differences. These differences were analyzed in 6DOFs and as a function of isocenter positions away from the anterior surface to assess OSI‐ROI bias. The SGRT in‐room setup time was estimated and compared with CBCT and orthogonal 2D kilovoltage (2DkV) setups.

Results

The SGRT setup difference (magnitude) is found to be 1.0 ± 2.5 mm and 0.1˚±1.4˚ on average among 415 treatments and within 5 mm/3˚ with greater than 95% confidence level (P < 0.001). Outliers were observed for very‐posterior isocenters: 15 differences (3.6%) are >5.0mm and 9 (2.2%) are >3.0˚. The setup differences show minor correlations (|r| < 0.45) between translational and rotational DOFs and a minor increasing trend (<1.0 mm) in the anterior‐to‐posterior direction. The SGRT setup time is 0.8 ± 0.3 min, much shorter than CBCT (5 ± 2 min) and 2DkV (2 ± 1 min) setups.

Conclusion

This study demonstrates that SGRT has sufficient accuracy for fast in‐room patient setup and allows real‐time motion monitoring for beam holding during treatment, potentially useful to guide radiotherapy of brain and nasopharynx cancer with standard fractionation.

IPEM topical report: guidance on the use of MRI for external beam radiotherapy treatment planning. Phys Med Biol 2021;66:055025. [PMID: 33450742 DOI: 10.1088/1361-6560/abdc30]

This document gives guidance for multidisciplinary teams within institutions setting up and using an MRI-guided radiotherapy (RT) treatment planning service. It has been written by a multidisciplinary working group from the Institute of Physics and Engineering in Medicine (IPEM). Guidance has come from the experience of the institutions represented in the IPEM working group, in consultation with other institutions, and where appropriate references are given for any relevant legislation, other guidance documentation and information in the literature. Guidance is only given for MRI acquired for external beam RT treatment planning in a CT-based workflow, i.e. when MRI is acquired and registered to CT with the purpose of aiding delineation of target or organ at risk volumes. MRI use for treatment response assessment, MRI-only RT and other RT treatment types such as brachytherapy and gamma radiosurgery are not considered within the scope of this document. The aim was to produce guidance that will be useful for institutions who are setting up and using a dedicated MR scanner for RT (referred to as an MR-sim) and those who will have limited time on an MR scanner potentially managed outside of the RT department, often by radiology. Although not specifically covered in this document, there is an increase in the use of hybrid MRI-linac systems worldwide and brief comments are included to highlight any crossover with the early implementation of this technology. In this document, advice is given on introducing a RT workload onto a non-RT-dedicated MR scanner, as well as planning for installation of an MR scanner dedicated for RT. Next, practical guidance is given on the following, in the context of RT planning: training and education for all staff working in and around an MR scanner; RT patient set-up on an MR scanner; MRI sequence optimisation for RT purposes; commissioning and quality assurance (QA) to be performed on an MR scanner; and MRI to CT registration, including commissioning and QA.

Synthetic 4D-CT of the thorax for treatment plan adaptation on MR-guided radiotherapy systems

MR-guided radiotherapy treatment planning utilises the high soft-tissue contrast of MRI to reduce uncertainty in delineation of the target and organs at risk. Replacing 4D-CT with MRI-derived synthetic 4D-CT would support treatment plan adaptation on hybrid MR-guided radiotherapy systems for inter- and intrafractional differences in anatomy and respiration, whilst mitigating the risk of CT to MRI registration errors. Three methods were devised to calculate synthetic 4D and midposition (time-weighted mean position of the respiratory cycle) CT from 4D-T1w and Dixon MRI. The first approach employed intensity-based segmentation of Dixon MRI for bulk-density assignment (sCTD). The second step added spine density information using an atlas of CT and Dixon MRI (sCTDS). The third iteration used a polynomial function relating Hounsfield units and normalised T1w image intensity to account for variable lung density (sCTDSL). Motion information in 4D-T1w MRI was applied to generate synthetic CT in midposition and in twenty respiratory phases. For six lung cancer patients, synthetic 4D-CT was validated against 4D-CT in midposition by comparison of Hounsfield units and dose-volume metrics. Dosimetric differences found by comparing sCTD,DS,DSL and CT were evaluated using a Wilcoxon signed-rank test (p   =  0.05). Compared to sCTD and sCTDS, planning on sCTDSL significantly reduced absolute dosimetric differences in the planning target volume metrics to less than 98 cGy (1.7% of the prescribed dose) on average. When comparing sCTDSL and CT, average radiodensity differences were within 97 Hounsfield units and dosimetric differences were significant only for the planning target volume D99% metric. All methods produced clinically acceptable results for the organs at risk in accordance with the UK SABR consensus guidelines and the LungTech EORTC phase II trial. The overall good agreement between sCTDSL and CT demonstrates the feasibility of employing synthetic 4D-CT for plan adaptation on hybrid MR-guided radiotherapy systems.

Estimation of lifetime attributable risks (LARs) of cancer associated with abdominopelvic radiotherapy treatment planning computed tomography (CT) simulations

PURPOSE

The present study attempts to calculate organ-absorbed and effective doses for cancer patients to estimate the possible cancer induction and cancer mortality risks resulting from 64-slice abdominopelvic computed tomography (CT) simulations for radiotherapy treatment planning (RTTP).

MATERIAL AND METHODS

A group of 70 patients, who underwent 64-slice abdominopelvic CT scan for RTTP, voluntarily participated in the present study. To calculate organ and effective doses in a standard phantom of 70 kg, the collected dosimetric parameters were used with the ImPACT CT Patient Dosimetry Calculator. Patient-specific organ dose and effective dose were calculated by applying related correction factors. For the estimation of lifetime attributable risks (LARs) of cancer incidence and cancer-related mortality, doses in radiosensitive organs were converted to risks based on the data published in Biological Effects of Ionizing Radiation VII (BEIR VII).

RESULTS

The mean ± standard deviation (SD) of the effective dose for males and females were 13.87 ± 2.37 mSv (range: 9.25-18.82 mSv) and 13.04 ± 3.42 mSv (range: 6.99-18.37 mSv), respectively. The mean ± SD of LAR of cancer incidence was 35.34 ± 13.82 cases in males and 34.49 ± 9.63 cases in females per 100,000 persons. The LAR of cancer mortality had the mean ± SD value of 15.38 ± 4.25 and 16.72 ± 3.87 cases per 100,000 persons in males and females respectively.

CONCLUSION

Increase in the LAR of cancer occurrence and mortality due to abdominopelvic treatment planning CT simulation is noticeable and should be considered.

The Radiobiology of Proton Therapy: Challenges and Opportunities Around Relative Biological Effectiveness

With the current UK expansion of proton therapy there is a great opportunity for clinical oncologists to develop a translational interest in the associated scientific base and clinical results. In particular, the underpinning controversy regarding the conversion of photon dose to proton dose by the relative biological effectiveness (RBE) must be understood, including its important implications. At the present time, the proton prescribed dose includes an RBE of 1.1 regardless of tissue, tumour and dose fractionation. A body of data has emerged against this pragmatic approach, including a critique of the existing evidence base, due to choice of dose, use of only acute-reacting in vivo assays, analysis methods and the reference radiations used to determine the RBE. Modelling systems, based on the best available scientific evidence, and which include the clinically useful biological effective dose (BED) concept, have also been developed to estimate proton RBEs for different dose and linear energy transfer (LET) values. The latter reflect ionisation density, which progressively increases along each proton track. Late-reacting tissues, such as the brain, where α/β = 2 Gy, show a higher RBE than 1.1 at a low dose per fraction (1.2-1.8 Gy) at LET values used to cover conventional target volumes and can be much higher. RBE changes with tissue depth seem to vary depending on the method of beam delivery used. To reduce unexpected toxicity, which does occasionally follow proton therapy, a more rational approach to RBE allocation, using a variable RBE that depends on dose per fraction and the tissue and tumour radiobiological characteristics such as α/β, is proposed.

T2-Weighted 4D Magnetic Resonance Imaging for Application in Magnetic Resonance-Guided Radiotherapy Treatment Planning

OBJECTIVES

The aim of this study was to develop and verify a method to obtain good temporal resolution T2-weighted 4-dimensional (4D-T2w) magnetic resonance imaging (MRI) by using motion information from T1-weighted 4D (4D-T1w) MRI, to support treatment planning in MR-guided radiotherapy.

MATERIALS AND METHODS

Ten patients with primary non-small cell lung cancer were scanned at 1.5 T axially with a volumetric T2-weighted turbo spin echo sequence gated to exhalation and a volumetric T1-weighted stack-of-stars spoiled gradient echo sequence with golden angle spacing acquired in free breathing. From the latter, 20 respiratory phases were reconstructed using the recently developed 4D joint MoCo-HDTV algorithm based on the self-gating signal obtained from the k-space center. Motion vector fields describing the respiratory cycle were obtained by deformable image registration between the respiratory phases and projected onto the T2-weighted image volume. The resulting 4D-T2w volumes were verified against the 4D-T1w volumes: an edge-detection method was used to measure the diaphragm positions; the locations of anatomical landmarks delineated by a radiation oncologist were compared and normalized mutual information was calculated to evaluate volumetric image similarity.

RESULTS

High-resolution 4D-T2w MRI was obtained. Respiratory motion was preserved on calculated 4D-T2w MRI, with median diaphragm positions being consistent with less than 6.6 mm (2 voxels) for all patients and less than 3.3 mm (1 voxel) for 9 of 10 patients. Geometrical positions were coherent between 4D-T1w and 4D-T2w MRI as Euclidean distances between all corresponding anatomical landmarks agreed to within 7.6 mm (Euclidean distance of 2 voxels) and were below 3.8 mm (Euclidean distance of 1 voxel) for 355 of 470 pairs of anatomical landmarks. Volumetric image similarity was commensurate between 4D-T1w and 4D-T2w MRI, as mean percentage differences in normalized mutual information (calculated over all respiratory phases and patients), between corresponding respiratory phases of 4D-T1w and 4D-T2w MRI and the tie-phase of 4D-T1w and 3-dimensional T2w MRI, were consistent to 0.41% ± 0.37%. Four-dimensional T2w MRI displayed tumor extent, structure, and position more clearly than corresponding 4D-T1w MRI, especially when mobile tumor sites were adjacent to organs at risk.

CONCLUSIONS

A methodology to obtain 4D-T2w MRI that retrospectively applies the motion information from 4D-T1w MRI to 3-dimensional T2w MRI was developed and verified. Four-dimensional T2w MRI can assist clinicians in delineating mobile lesions that are difficult to define on 4D-T1w MRI, because of poor tumor-tissue contrast.

A Feasibility Study of Personalized Prescription Schemes for Glioblastoma Patients Using a Proliferation and Invasion Glioma Model

Purpose: This study investigates the feasibility of personalizing radiotherapy prescription schemes (treatment margins and fractional doses) for glioblastoma (GBM) patients and their potential benefits using a proliferation and invasion (PI) glioma model on phantoms. Methods and Materials: We propose a strategy to personalize radiotherapy prescription schemes by simulating the proliferation and invasion of the tumor in 2D according to the PI glioma model. We demonstrate the strategy and its potential benefits by presenting virtual cases, where the standard and personalized prescriptions were applied to the tumor. Standard prescription was assumed to deliver 46 Gy in 23 fractions to the initial, gross tumor volume (GTV₁) plus a 2 cm margin and an additional 14 Gy in 7 fractions to the boost GTV₂ plus a 2 cm margin. The virtual cases include the tumors with a moving velocity of 0.029 (slow-move), 0.079 (average-move), and 0.13 (fast-move) mm/day for the gross tumor volume (GTV) with a radius of 1 (small) and 2 (large) cm. For each tumor size and velocity, the margin around GTV₁ and GTV₂ was varied between 0–6 cm and 1–3 cm, respectively. Equivalent uniform dose (EUD) to normal brain was constrained to the EUD value obtained by using the standard prescription. Various linear dose policies, where the fractional dose is linearly decreasing, constant, or increasing, were investigated to estimate the temporal effect of the radiation dose on tumor cell-kills. The goal was to find the combination of margins for GTV₁ and GTV₂ and a linear dose policy, which minimize the tumor cell-surviving fraction (SF) under a normal tissue constraint. The efficacy of a personalized prescription was evaluated by tumor EUD and the estimated survival time. Results: The personalized prescription for the slow-move tumors was to use 3.0–3.5 cm margins for GTV₁, and a 1.5 cm margin for GTV₂. For the average- and fast-move tumors, it was optimal to use a 6.0 cm margin for GTV₁ and then 1.5–3.0 cm margins for GTV₂, suggesting a course of whole brain therapy followed by a boost to a smaller volume. It was more effective to deliver the boost sequentially using a linearly decreasing fractional dose for all tumors. Personalized prescriptions led to surviving fractions of 0.001–0.465% compared to the standard prescription, and increased the tumor EUDs by 25.3–49.3% and estimated survival times by 7.6–22.2 months. Conclusions: Personalizing treatment margins based on the measured proliferative capacity of GBM tumor cells can potentially lead to significant improvements in tumor cell kill and related clinical outcomes.

An Approach in Radiation Therapy Treatment Planning: A Fast, GPU-Based Monte Carlo Method

An accurate and fast radiation dose calculation is essential for successful radiation radiotherapy. The aim of this study was to implement a new graphic processing unit (GPU) based radiation therapy treatment planning for accurate and fast dose calculation in radiotherapy centers. A program was written for parallel running based on GPU. The code validation was performed by EGSnrc/DOSXYZnrc. Moreover, a semi-automatic, rotary, asymmetric phantom was designed and produced using a bone, the lung, and the soft tissue equivalent materials. All measurements were performed using a Mapcheck dosimeter. The accuracy of the code was validated using the experimental data, which was obtained from the anthropomorphic phantom as the gold standard. The findings showed that, compared with those of DOSXYZnrc in the virtual phantom and for most of the voxels (>95%), <3% dose-difference or 3 mm distance-to-agreement (DTA) was found. Moreover, considering the anthropomorphic phantom, compared to the Mapcheck dose measurements, <5% dose-difference or 5 mm DTA was observed. Fast calculation speed and high accuracy of GPU-based Monte Carlo method in dose calculation may be useful in routine radiation therapy centers as the core and main component of a treatment planning verification system.

Validation of a Magnetic Resonance Imaging-based Auto-contouring Software Tool for Gross Tumour Delineation in Head and Neck Cancer Radiotherapy Planning

AIMS

To carry out statistical validation of a newly developed magnetic resonance imaging (MRI) auto-contouring software tool for gross tumour volume (GTV) delineation in head and neck tumours to assist in radiotherapy planning.

MATERIALS AND METHODS

Axial MRI baseline scans were obtained for 10 oropharyngeal and laryngeal cancer patients. GTV was present on 102 axial slices and auto-contoured using the modified fuzzy c-means clustering integrated with the level set method (FCLSM). Peer-reviewed (C-gold) manual contours were used as the reference standard to validate auto-contoured GTVs (C-auto) and mean manual contours (C-manual) from two expert clinicians (C1 and C2). Multiple geometric metrics, including the Dice similarity coefficient (DSC), were used for quantitative validation. A DSC≥0.7 was deemed acceptable. Inter- and intra-variabilities among the manual contours were also validated. The two-dimensional contours were then reconstructed in three dimensions for GTV volume calculation, comparison and three-dimensional visualisation.

RESULTS

The mean DSC between C-gold and C-auto was 0.79. The mean DSC between C-gold and C-manual was 0.79 and that between C1 and C2 was 0.80. The average time for GTV auto-contouring per patient was 8 min (range 6-13 min; mean 45 s per axial slice) compared with 15 min (range 6-23 min; mean 88 s per axial slice) for C1. The average volume concordance between C-gold and C-auto volumes was 86.51% compared with 74.16% between C-gold and C-manual. The average volume concordance between C1 and C2 volumes was 86.82%.

CONCLUSIONS

This newly designed MRI-based auto-contouring software tool shows initial acceptable results in GTV delineation of oropharyngeal and laryngeal tumours using FCLSM. This auto-contouring software tool may help reduce inter- and intra-variability and can assist clinical oncologists with time-consuming, complex radiotherapy planning.

Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models

PURPOSE

Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.

METHODS

The authors' segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.

RESULTS

In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors' segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.

CONCLUSIONS

The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

The application of functional imaging techniques to personalise chemoradiotherapy in upper gastrointestinal malignancies

Functional imaging gives information about physiological heterogeneity in tumours. The utility of functional imaging tests in providing predictive and prognostic information after chemoradiotherapy for both oesophageal cancer and pancreatic cancer will be reviewed. The benefit of incorporating functional imaging into radiotherapy planning is also evaluated. In cancers of the upper gastrointestinal tract, the vast majority of functional imaging studies have used (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET). Few studies in locally advanced pancreatic cancer have investigated the utility of functional imaging in risk-stratifying patients or aiding target volume definition. Certain themes from the oesophageal data emerge, including the need for a multiparametric assessment of functional images and the added value of response assessment rather than relying on single time point measures. The sensitivity and specificity of FDG-PET to predict treatment response and survival are not currently high enough to inform treatment decisions. This suggests that a multimodal, multiparametric approach may be required. FDG-PET improves target volume definition in oesophageal cancer by improving the accuracy of tumour length definition and by improving the nodal staging of patients. The ideal functional imaging test would accurately identify patients who are unlikely to achieve a pathological complete response after chemoradiotherapy and would aid the delineation of a biological target volume that could be used for treatment intensification. The current limitations of published studies prevent integrating imaging-derived parameters into decision making on an individual patient basis. These limitations should inform future trial design in oesophageal and pancreatic cancers.

Computer-aided beam arrangement based on similar cases in radiation treatment-planning databases for stereotactic lung radiation therapy

The purpose of this study was to develop a computer-aided method for determination of beam arrangements based on similar cases in a radiotherapy treatment-planning database for stereotactic lung radiation therapy. Similar-case-based beam arrangements were automatically determined based on the following two steps. First, the five most similar cases were searched, based on geometrical features related to the location, size and shape of the planning target volume, lung and spinal cord. Second, five beam arrangements of an objective case were automatically determined by registering five similar cases with the objective case, with respect to lung regions, by means of a linear registration technique. For evaluation of the beam arrangements five treatment plans were manually created by applying the beam arrangements determined in the second step to the objective case. The most usable beam arrangement was selected by sorting the five treatment plans based on eight plan evaluation indices, including the D95, mean lung dose and spinal cord maximum dose. We applied the proposed method to 10 test cases, by using an RTP database of 81 cases with lung cancer, and compared the eight plan evaluation indices between the original treatment plan and the corresponding most usable similar-case-based treatment plan. As a result, the proposed method may provide usable beam arrangements, which have no statistically significant differences from the original beam arrangements (P > 0.05) in terms of the eight plan evaluation indices. Therefore, the proposed method could be employed as an educational tool for less experienced treatment planners.

Functional image-based radiotherapy planning for non-small cell lung cancer: A simulation study

BACKGROUND AND PURPOSE

To investigate the incorporation of data from single-photon emission computed tomography (SPECT) or hyperpolarized helium-3 magnetic resonance imaging ((3)He-MRI) into intensity-modulated radiotherapy (IMRT) planning for non-small cell lung cancer (NSCLC).

MATERIAL AND METHODS

Seven scenarios were simulated that represent cases of NSCLC with significant functional lung defects. Two independent IMRT plans were produced for each scenario; one to minimise total lung volume receiving >or=20Gy (V(20)), and the other to minimise only the functional lung volume receiving >or=20Gy (FV(20)). Dose-volume characteristics and a plan quality index related to planning target volume coverage by the 95% isodose (V(PTV95)/FV(20)) were compared between anatomical and functional plans using the Wilcoxon signed ranks test.

RESULTS

Compared to anatomical IMRT plans, functional planning reduced FV(20) (median 2.7%, range 0.6-3.5%, p=0.02), and total lung V(20) (median 1.5%, 0.5-2.7%, p=0.02), with a small reduction in mean functional lung dose (median 0.4Gy, 0-0.7Gy, p=0.03). There were no significant differences in target volume coverage or organ-at-risk doses. Plan quality index was improved for functional plans (median increase 1.4, range 0-11.8, p=0.02).

CONCLUSIONS

Statistically significant reductions in FV(20), V(20) and mean functional lung dose are possible when IMRT planning is supplemented by functional information derived from SPECT or (3)He-MRI.

Improvements in radiotherapy practice: the impact of new imaging technologies

Improvements in imaging technology are impacting on every stage of the radiotherapy treatment process. Fundamental to this is the move towards computed tomography (CT) simulation as the basis of all radiotherapy planning. Whilst for many treatments, the definition of three-dimensional (3D) tumour volumes is necessary, for geometrically simple treatments virtual simulation may be more speedily performed by utilising the reconstruction of data in multiple imaging planes. These multi-planar reconstructions may be used to define both the treatment volumes (e.g. for palliative lung treatments) and the organs at risk to be avoided (e.g. for para-aortic strip irradiation). For complex treatments such as conformal radiotherapy (CFRT) and intensity-modulated radiotherapy (IMRT) where 3D volumes are defined, improvements in imaging technologies have specific roles to play in defining the gross tumour volume (GTV) and the planning target volume (PTV). Image registration technologies allow the incorporation of functional imaging, such as positron emission tomography and functional magnetic resonance imaging, into the definition of the GTV to result in a biological target volume. Crucial to the successful irradiation of these volumes is the definition of appropriate PTV margins. Again improvements in imaging are revolutionising this process by reducing the necessary margin (active breathing control, treatment gating) and by incorporating patient motion into the planning process (slow CT scans, CT/fluoroscopy units). CFRT and IMRT are leading to far closer conformance of the treated volume to the defined tumour volume. To ensure that this is reliably achieved on a daily basis, new imaging technologies are being incorporated into the verification process. Portal imaging has been transformed by the introduction of electronic portal imaging devices and a move is underway from two-dimensional (2D) to 3D treatment verification (cone beam CT, optical video systems). A parallel development is underway from off-line analysis of portal images to the incorporation of imaging at the time of treatment using image-guided radiotherapy. By impacting on the whole process of radiotherapy (tumour definition, simulation, treatment verification), these new imaging technologies offer improvements in radiotherapy delivery with the potential for greater cure rates and a minimum level of treatment side effects.

Comparison of two planning systems for HDR brachytherapy gynecological application

PURPOSE

This report compares the Nucletron NPS and PLATO planning system for patients treated for cervix cancer.

MATERIALS AND METHODS

This study compares calculations generated using the older NPS (version 11.43) planning system and the more recent PLATO (version 14.1) system for two cases: 1) a single dwell position and 2) an actual patient application using a tandem and ovoid.

RESULTS

For one dwell position: for NPS planning the dose for points along the source axis forward of the cable was 9.85% more than for symmetrically placed points in the cable direction. For PLATO, the same test gave rise to a difference of 10.2%. Comparing the two systems, NPS calculated doses for points in the forward direction 14% greater than those calculated by PLATO. The entry of points using the digitizer accounted for less than 1% of any difference. For the patient case: the dose difference between NPS and PLATO planning for all patient reference points entered from films ranged from 1 to 4%. The difference in dose between optimized and nonoptimized planning was approximately 0.5% for prescription points (points A), while for the bladder and rectum the differences were 6% and 20%, respectively with NPS, and with PLATO, 8% and 22%, respectively.

CONCLUSION

This study highlighted the effects of the differences in the calculational algorithm between the older and newer planning systems from Nucletron. While the differences were minimal on the perpendicular bisector of the source, along the axis they become considerable. In a practical gynecological case, these differences mostly affect the dose to the rectum, since that organ receives the greatest proportion of its dose from rays near the same axis. Overall, the PLATO system plan required about 2.5% less integrated reference air kerma than the NPS plan for the same dose to point A. For either planning system, optimization is crucial in decreasing dose to bladder and rectal points.

Reproducibility of interactive registration of 3D CT and MR pediatric treatment planning head images

The reproducibility of an interactive image registration technique used as part of the radiotherapy treatment planning process was investigated for 3D CT and MR pediatric head images. Over a nine month period, 85 CT/MR image registrations, required for treatment planning, were repeated, 52 by the same operator and 33 by a different operator. All were performing image registrations for normal clinical care and the first registration was used clinically. Inter- and intra-operator reproducibility of the translation and rotation were calculated separately. The standard deviation of the average total translation and rotation was 0.39 mm and 1.7 degrees, and 0.58 mm and 2.8 degrees, respectively. The maximum difference between registrations was 1.1 mm and 4.1 degrees when repeated by the same operator, and 1.4 mm and 5.8 degrees when repeated by another operator. The variation for the lowest resolution parameters, out of plane translation and rotations, was 2 to 3 times larger than for in-plane movements. A registration took between 5 minutes and over half an hour for difficult cases, with a mean of 14.3 minutes. One to two millimeter reproducibility was not achieved and interactive registration was relatively time consuming. There is a clear image resolution effect on registration reproducibility, suggesting that reducing slice thickness could considerably improve registration reproducibility.