Validation of Varian's SmartAdapt® deformable image registration algorithm for clinical application

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study

BACKGROUND

The planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast.

METHODS

PANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases.

RESULTS

The median JCI for the arterial and venous phases were 0.50 (range, 0.17-0.64) and 0.41 (range, 0.23-0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38-0.57) on the arterial phase, and 0.52 (range 0.36-0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators.

CONCLUSIONS

Our results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

The effects of mega-voltage CT scan parameters on offline adaptive radiation therapy

TomoTherapy involves image-guided radiation therapy (IGRT) using Mega-voltage CT (MVCT) for each treatment session. The acquired MVCT images can be utilized for the retrospective assessment of dose distribution. The TomoTherapy provides 18 distinct imaging conditions that can be selected based on a combination of algorithms, acquisition pitch, and slice interval. We investigated the accuracy of dose calculation and deformable image registration (DIR) depending on MVCT scan parameters and their effects on adaptive radiation therapy (ART). We acquired image values for density calibration tables (IVDTs) under 18 different MVCT conditions and compared them. The planning CT (pCT) was performed using a thoracic phantom, and an esophageal intensity-modulated radiation therapy (IMRT) plan was created. MVCT images of the thoracic phantom were acquired under each of the 18 conditions, and dose recalculation was performed. DIR was performed on the MVCT images acquired under each condition. The accuracy of DIR, depending on the MVCT scan parameters, was compared using the mean distance to agreement (MDA) and Dice similarity coefficient (DSC). The dose distribution calculated on the MVCT images was deformed using deformed vector fields (DVF). No significant differences were observed in the results of the 18 IVDTs. The esophageal IMRT plan also showed a small dose difference. Regarding verifying the DIR accuracy, the MDA increased, and the DSC decreased as the acquisition pitch and slice interval increased. The difference between the dose distributions after dose mapping was comparable to that before DIR. The MVCT scan parameters had little effect on ART.

Differences in geometric patterns of failure in human papillomavirus (HPV)-associated and HPV-non-associated oropharyngeal cancer after definitive radiotherapy

INTRODUCTION

The aim of this study was to evaluate and compare the spatial pattern of locoregional recurrences in patients diagnosed with HPV-associated and HPV-non-associated oropharyngeal SCC (OPSCC) treated with definitive radiotherapy.

METHODS AND MATERIALS

Patients who had locoregional recurrence following definitive intensity-modulated radiation therapy were identified at a single tertiary institution. Target volumes were delineated according to the latest consensus international guidelines. Recurrences were classified into five categories based on radiotherapy dose distribution and target volume, using a previously validated methodology; type A (central high dose), type B (peripheral high dose), type C (central elective dose), type D (peripheral elective dose), and type E (extraneous dose). The types of failure were compared between p16-positive and p16-negative tumors using the Pearson chi-square test.

RESULTS

Fifty-eight locoregional recurrences were observed in 36 patients. The majority of recurrences were in nodal locations (66%, 38/58). Among these, 34 (59%) were classified as type A, 6 (10%) as type B, 9 (15%) as type C, 5 (9%) as type D, and 4 (7%) as type E failure. A significant difference was found in the types of failure between p16-positive and p16-negative tumors (X2 9.52, p = 0.044). p16-negative tumors were more likely to have recurrences in a peripheral location compared to p16-positive tumors (32% vs. 7%). p16-positive tumor were more likely to have extraneous recurrences (17% vs. 0%).

CONCLUSION

Our study results identified a significant difference in patterns of locoregional failure among patients diagnosed with oropharyngeal cancer following consensus-based tumor delineation and modern radiotherapy. Further confirmatory pattern of failure studies are required to enable greater individualization of radiotherapy for patients diagnosed with oropharyngeal malignancy in the future.

The Investigating Image Registration Accuracy and Contour Propagation for Adaptive Radiotherapy Purposes in Line with the Task Group No. 132 Recommendation

Purpose

Image registration is a crucial component of the adaptive radiotherapy workflow. This study investigates the accuracy of the deformable image registration (DIR) and contour propagation features of SmartAdapt, an application in the Eclipse treatment planning system (TPS) version 16.1.

Materials and Methods

The registration accuracy was validated using the Task Group No. 132 (TG-132) virtual phantom, which features contour evaluation and landmark analysis based on the quantitative criteria recommended in the American Association of Physicists in Medicine TG-132 report. The target registration error, Dice similarity coefficient (DSC), and center of mass displacement were used as quantitative validation metrics. The performance of the contour propagation feature was evaluated using clinical datasets (head and neck, pelvis, and chest) and an additional four-dimensional computed tomography (CT) dataset from TG-132. The primary planning and the second CT images were appropriately registered and deformed. The DSC was used to find the volume overlapping between the deformed contours and the radiation oncologist (RO)-drawn contour. The clinical value of the DIR-generated structure was reviewed and scored by an experienced RO to make a qualitative assessment.

Results

The registration accuracy fell within the specified tolerances. SmartAdapt exhibited a reasonably propagated contour for the chest and head-and-neck regions, with DSC values of 0.80 for organs at risk. Misregistration is frequently observed in the pelvic region, which is specified as a low-contrast region. However, 78% of structures required no modification or minor modification, demonstrating good agreement between contour comparison and the qualitative analysis.

Conclusions

SmartAdapt has adequate efficiency for image registration and contour propagation for adaptive purposes in various anatomical sites. However, there should be concern about its performance in regions with low contrast and small volumes.

CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that incorporates progressive changes in patient anatomy into active plan/dose adaption during the fractionated treatment. However, the clinical application relies on the accurate segmentation of cancer tumors on low-quality on-board images, which has posed challenges for both manual delineation and deep learning-based models. In this paper, we propose a novel sequence transduction deep neural network with an attention mechanism to learn the shrinkage of the cancer tumor based on patients' weekly cone-beam computed tomography (CBCT). We design a self-supervised domain adaption (SDA) method to learn and adapt the rich textural and spatial features from pre-treatment high-quality computed tomography (CT) to CBCT modality in order to address the poor image quality and lack of labels. We also provide uncertainty estimation for sequential segmentation, which aids not only in the risk management of treatment planning but also in the calibration and reliability of the model. Our experimental results based on a clinical non-small cell lung cancer (NSCLC) dataset with sixteen patients and ninety-six longitudinal CBCTs show that our model correctly learns weekly deformation of the tumor over time with an average dice score of 0.92 on the immediate next step, and is able to predict multiple steps (up to 5 weeks) for future patient treatments with an average dice score reduction of 0.05. By incorporating the tumor shrinkage predictions into a weekly re-planning strategy, our proposed method demonstrates a significant decrease in the risk of radiation-induced pneumonitis up to 35% while maintaining the high tumor control probability.

A novel tool to evaluate and quantify radiation pneumonitis: A retrospective analysis of correlation of dosimetric parameters with volume of pneumonia patch

Introduction

In lung cancer, radiation-induced lung injury (RILI) or radiation pneumonitis (RP) are major concerns after radiotherapy. We investigated the correlation between volumes of RP lesions and their RP grades after radiotherapy.

Methods and materials

We retrospectively collected data from patients with non-small lung cancer that received curative doses to the thorax without undergoing chest radiotherapy before this treatment course. The post-treatment computed tomography (CT) image was used to register to the planning CT to evaluate the correlation between dosimetric parameters and volume of pneumonia patch by using deformable image registration.

Results

From January 1, 2019, to December 30, 2020, 71 patients with non-small cell lung cancer with 169 sets of CT images met our criteria for evaluation. In all patient groups, we found the RPv max and RP grade max to be significant (p&lt;0.001). Some parameters that were related to the dose-volume histogram (DVH) and RP were lung Vx (x=1-66 Gy, percentage of lung volume received ≥x Gy), and mean lung dose. Comparing these parameters of the DVH with RP grade max showed that the mean lung dose and lung V1–V31 were significantly correlated. The cut-off point for the occurrence of symptoms in all patient groups, the RPv max value, was 4.79%, while the area under the curve was 0.779. In the groups with grades 1 and 2 RP, the dose curve of 26 Gy covered ≥80% of RP lesions in &gt;80% of patients. Patients who had radiotherapy in combination with chemotherapy had significantly shorter locoregional progression-free survival (p=0.049) than patients who received radiation therapy in combination with target therapy. Patients with RPv max &gt;4.79% demonstrated better OS (p=0.082).

Conclusion

The percentage of RP lesion volume to total lung volume is a good indicator for quantifying RP. RP lesions can be projected onto the original radiation therapy plan using coverage of the 26 Gy isodose line to determine whether the lesion is RILI.

Machine-learning-based prediction of the effectiveness of the delivered dose by exhale-gated radiotherapy for locally advanced lung cancer: The additional value of geometric over dosimetric parameters alone

Purpose

This study aimed to assess interfraction stability of the delivered dose distribution by exhale-gated volumetric modulated arc therapy (VMAT) or intensity-modulated arc therapy (IMAT) for lung cancer and to determine dominant prognostic dosimetric and geometric factors.

Methods

Clinical target volume (CTV_Plan) from the planning CT was deformed to the exhale-gated daily CBCT scans to determine CTV_i, treated by the respective dose fraction. The equivalent uniform dose of the CTV_i was determined by the power law (gEUD_i) and cell survival model (EUD_iSF) as effectiveness measure for the delivered dose distribution. The following prognostic factors were analyzed: (I) minimum dose within the CTV_i (D_{min_i}), (II) Hausdorff distance (HDD_i) between CTV_i and CTV_Plan, (III) doses and deformations at the point in CTV_Plan at which the global minimum dose over all fractions per patient occurs (PD_{min_global_i}), and (IV) deformations at the point over all CTV_i margins per patient with the largest Hausdorff distance (HDPw_orst). Prognostic value and generalizability of the prognostic factors were examined using cross-validated random forest or multilayer perceptron neural network (MLP) classifiers. Dose accumulation was performed using back deformation of the dose distribution from CTV_i to CTV_Plan.

Results

Altogether, 218 dose fractions (10 patients) were evaluated. There was a significant interpatient heterogeneity between the distributions of the normalized gEUD_i values (p<0.0001, Kruskal-Wallis tests). Accumulated gEUD over all fractions per patient was 1.004-1.023 times of the prescribed dose. Accumulation led to tolerance of ~20% of fractions with gEUD_i <93% of the prescribed dose. Normalized D_min >60% was associated with predicted gEUD values above 95%. D_min had the highest importance for predicting the gEUD over all analyzed prognostic parameters by out-of-bag loss reduction using the random forest procedure. Cross-validated random forest classifier based on D_min as the sole input had the largest Pearson correlation coefficient (R=0.897) in comparison to classifiers using additional input variables. The neural network performed better than the random forest classifier, and the gEUD values predicted by the MLP classifier with D_min as the sole input were correlated with the gEUD values characterized by R=0.933 (95% CI, 0.913-0.948). The performance of the full MLP model with all geometric input parameters was slightly better (R=0.952) than that based on D_min (p=0.0034, Z-test).

Conclusion

Accumulated dose distributions over the treatment series were robust against interfraction CTV deformations using exhale gating and online image guidance. D_min was the most important parameter for gEUD prediction for a single fraction. All other parameters did not lead to a markedly improved generalizable prediction. Dosimetric information, especially location and value of D_min within the CTV _i , are vital information for image-guided radiation treatment.

Comparison of Three Commercial Methods of Cone-Beam Computed Tomography-Based Dosimetric Analysis of Head-and-Neck Patients with Weight Loss

Purpose

This investigation compares three commercial methods of cone-beam computed tomography (CBCT)-based dosimetric analysis to a method based on repeat computed tomography (CT).

Materials and Methods

Seventeen head-and-neck patients treated in 2020, and with a repeat CT, were included in the analyses. The planning CT was deformed to anatomy in repeat CT to generate a reference plan. Two of the CBCT-based methods generated test plans by deforming the planning CT to CBCT of fraction N using VelocityAI™ and SmartAdapt®. The third method compared directly calculated doses on the CBCT for fraction 1 and fraction N, using PerFraction™. Maximum dose to spinal cord (Cord_dmax) and dose to 95% volume (D95) of planning target volumes (PTVs) were used to assess "need to replan" criteria.

Results

The VelocityAI™ method provided results that most accurately matched the reference plan in "need to replan" criteria using either Cord_dmax or PTV D95. SmartAdapt® method overestimated the change in Cord_dmax (6.77% vs. 3.85%, P < 0.01) and change in cord volume (9.56% vs. 0.67%, P < 0.01) resulting in increased false positives in "need to replan" criteria, and performed similarly to VelocityAI™ for D95, but yielded more false negatives. PerFraction™ method underestimated Cord_dmax, did not perform any volume deformation, and missed all "need to replan" cases based on cord dose. It also yielded high false negatives using the D95 PTV criteria.

Conclusions

The VelocityAI™-based method using fraction N CBCT is most similar to the reference plan using repeat CT; the other two methods had significant differences.

Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

PURPOSE

The performance of deformable medical image registration (DIR) algorithms has become a major concern.

METHODS

We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines.

RESULTS

One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed.

CONCLUSIONS

This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.

MIRSIG position paper: the use of image registration and fusion algorithms in radiotherapy

The report of the American Association of Physicists in Medicine (AAPM) Task Group No. 132 published in 2017 reviewed rigid image registration and deformable image registration (DIR) approaches and solutions to provide recommendations for quality assurance and quality control of clinical image registration and fusion techniques in radiotherapy. However, that report did not include the use of DIR for advanced applications such as dose warping or warping of other matrices of interest. Considering that DIR warping tools are now readily available, discussions were hosted by the Medical Image Registration Special Interest Group (MIRSIG) of the Australasian College of Physical Scientists & Engineers in Medicine in 2018 to form a consensus on best practice guidelines. This position statement authored by MIRSIG endorses the recommendations of the report of AAPM task group 132 and expands on the best practice advice from the 'Deforming to Best Practice' MIRSIG publication to provide guidelines on the use of DIR for advanced applications.

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.

Automatic contour segmentation of cervical cancer using artificial intelligence

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the tumor contours would prove useful. To the best of our knowledge, automatic tumor contour segmentation has rarely been applied to cervical cancer treatment. In this study, diffusion-weighted images (DWI) of 98 patients with cervical cancer were acquired. We trained an automatic tumor contour segmentation model using 2D U-Net and 3D U-Net to investigate the possibility of applying such a model to clinical practice. A total of 98 cases were employed for the training, and they were then predicted by swapping the training and test images. To predict tumor contours, six prediction images were obtained after six training sessions for one case. The six images were then summed and binarized to output a final image through automatic contour segmentation. For the evaluation, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) was applied to analyze the difference between tumor contour delineation by radiation oncologists and the output image. The DSC ranged from 0.13 to 0.93 (median 0.83, mean 0.77). The cases with DSC <0.65 included tumors with a maximum diameter < 40 mm and heterogeneous intracavitary concentration due to necrosis. The HD ranged from 2.7 to 9.6 mm (median 4.7 mm). Thus, the study confirmed that the tumor contours of cervical cancer can be automatically segmented with high accuracy.

Evaluation of Varian's SmartAdapt for clinical use in radiation therapy for patients with thoracic lesions

INTRODUCTION

Deformable image registration (DIR) is a required tool in any adaptive radiotherapy program to help account for anatomical changes that occur during a multifraction treatment. SmartAdapt is a DIR tool from Varian incorporated within the eclipse treatment planning system, that can be used for contour propagation and transfer of PET, MRI, or computed tomography (CT) data. The purpose of this work is to evaluate the registration and contour propagation accuracy of SmartAdapt for thoracic CT studies using the guidelines from AAPM TG 132.

METHODS

To evaluate the registration accuracy of SmartAdapt the mean target registration error (TRE) was measured for ten landmarked 4DCT images from the https://www.dir-labs.com/ which included 300 landmarks matching the inspiration and expiration phase images. To further characterize the registration accuracy, the magnitude of deformation for each 4DCT was measured and compared against the mean TRE for each study. Contour propagation accuracy was evaluated using 22 randomly selected lung cancer cases from our center where there was either a replan, or the patient was treated for a new lesion within the lung. Contours evaluated included the right and left lung, esophagus, spinal canal, heart and the GTV and the results were quantified using the DICE similarity coefficient.

RESULTS

The mean TRE from all ten cases was 1.89 mm, the maximum mean TRE per case was 3.8 mm from case #8, which also had the most landmark pairs with displacements >2 cm. For contour propagation accuracy, the DICE coefficient results for left lung, right lung, heart, esophagus, and spinal canal were 0.93, 0.94, 0.90, 0.61, and 0.82 respectively.

CONCLUSION

The results from our study demonstrate that for thoracic images SmartAdapt in most cases will be accurate to below 2 mm in registration error unless there is deformation greater than 2 cm.

Spatiotemporal Free-Form Registration Method Assisted by a Minimum Spanning Tree During Discontinuous Transformations

The sliding motion along the boundaries of discontinuous regions has been actively studied in B-spline free-form deformation framework. This study focusses on the sliding motion for a velocity field-based 3D+t registration. The discontinuity of the tangent direction guides the deformation of the object region, and a separate control of two regions provides a better registration accuracy. The sliding motion under the velocity field-based transformation is conducted under the [Formula: see text]-Rényi entropy estimator using a minimum spanning tree (MST) topology. Moreover, a new topology changing method of the MST is proposed. The topology change is performed as follows: inserting random noise, constructing the MST, and removing random noise while preserving a local connection consistency of the MST. This random noise process (RNP) prevents the [Formula: see text]-Rényi entropy-based registration from degrading in sliding motion, because the RNP creates a small disturbance around special locations. Experiments were performed using two publicly available datasets: the DIR-Lab dataset, which consists of 4D pulmonary computed tomography (CT) images, and a benchmarking framework dataset for cardiac 3D ultrasound. For the 4D pulmonary CT images, RNP produced a significantly improved result for the original MST with sliding motion (p<0.05). For the cardiac 3D ultrasound dataset, only a discontinuity-based registration indicated activity of the RNP. In contrast, the single MST without sliding motion did not show any improvement. These experiments proved the effectiveness of the RNP for sliding motion.

Lasso logistic regression to derive workflow-specific algorithm performance requirements as demonstrated for head and neck cancer deformable image registration in adaptive radiation therapy

As automation in radiation oncology becomes more common, it is important to determine which algorithms are equivalent for a given workflow. Often, algorithm comparisons are performed in isolation; however, clinical context can provide valuable insight into the importance of algorithm features and error magnification in subsequent workflow steps. We propose a strategy for deriving workflow-specific algorithm performance requirements. We considered two independent workflows indicating the need for radiotherapy treatment replanning for 15 head and neck cancer patients (15 planning CTs, 105 on-unit CBCTs). Each workflow was based on a different deformable image registration (DIR) algorithm. Differences in DIR output were assessed using three sets of QA metrics: (1) conventional, (2) workflow-specific, (3) a combination of (1) and (2). For a given set of algorithm metrics, lasso logistic regression modeled the probability of discrepant replan indications. Varying the minimum probability needed to predict a workflow discrepancy produced receiver operating characteristic (ROC) curves. ROC curves were compared using sensitivity, specificity, and the area under the curve (AUC). A heuristic then derived simple algorithm performance requirements. Including workflow-specific QA metrics improved AUC from 0.70 to 0.85, compared to the use of conventional metrics alone. Algorithm performance requirements had high sensitivity of 0.80, beneficial for replan assessments, with specificity of 0.57. This was an improvement over a naïve application of conventional QA criteria, which had sensitivity of 0.57 and specificity of 0.68. In addition, the algorithm performance requirements indicated practical refinements of conventional QA tolerances, indicated where auxiliary workflow processes should be standardized, and may be used to prioritize structures for manual review. Our algorithm performance requirements outperformed current comparison recommendations and provided practical means for ensuring workflow equivalence. This strategy may aid in trial credentialing, algorithm development, and streamlining expert adjustment of workflow output.

Three-dimensional versus four-dimensional dose calculation for breast intensity-modulated radiation therapy

OBJECTIVE

To analyze the effect of intra- and interfractional motion during breast intensity modulated radiation therapy (IMRT) by calculating dose distribution based on four-dimensional computed tomography (4DCT).

METHODS

20 patients diagnosed with left breast cancer were enrolled. Three-dimensional CT (3DCT) along with 10 phases of 4DCT were collected for each patient, with target volumes independently delineated on both 3DCT and all phases of 4DCT. IMRT plans were generated based on 3DCT (43.2 Gy in 16 fractions). The plan parameters for each segment were split into phases based on time duration estimates for each respiratory phase, with phase-specific dose distributions calculated and summated (4D-calculated dose). The procedure is repeated for 16 fractionations by randomly allocating starting phase using random-number generation to simulate interfractional discrepancy caused by different starting phase. Comparisons of plan quality between the original and 4D-calculated doses were analyzed.

RESULTS

There was a significant distortion in 4D-calculated dose induced by respiratory motion in terms of conformity and homogeneity index compared to those of the original 3D plan. Mean doses of the heart and the ipsilateral lung were significantly higher in the 4D-calculated doses compared to those of the original 3D plan (0.34 Gy, p = 0.010 and 0.59 Gy, p < 0.001), respectively). The mean internal mammary lymph node (IMN) dose was significantly greater in the 4D-calculated plan, compared to the original 3D plan (1.42 Gy, p < 0.001).

CONCLUSIONS

IMN doses should be optimized during the dose-calculation for the free-breathing left breast IMRT.

ADVANCES IN KNOWLEDGE

The interplay effect between respiratory motion and multileaf collimator modulation caused discrepancies in dose distribution, particularly in IMN.

Data clustering to select clinically-relevant test cases for algorithm benchmarking and characterization

Algorithm benchmarking and characterization are an important part of algorithm development and validation prior to clinical implementation. However, benchmarking may be limited to a small collection of test cases due to the resource-intensive nature of establishing 'ground-truth' references. This study proposes a framework for selecting test cases to assess algorithm and workflow equivalence. Effective test case selection may minimize the number of ground-truth comparisons required to establish robust and clinically relevant benchmarking and characterization results. To demonstrate the proposed framework, we clustered differences between two independent workflows estimating during-treatment dose objective violations for 15 head and neck cancer patients (15 planning CTs, 105 on-unit CBCTs). Each workflow used a different deformable image registration algorithm to estimate inter-fractional anatomy and contour changes. The Hopkins statistic tested whether workflow output was inherently clustered and k-medoid clustering formalized cluster assignment. Further statistical analyses verified the relevance of clusters to algorithm output. Data at cluster centers ('medoids') were considered as candidate test cases representative of workflow-relevant algorithm differences. The framework indicated that differences in estimated dose objective violations were naturally grouped (Hopkins  =  0.75, providing 90% confidence). K-medoid clustering identified five clusters which stratified workflow differences (MANOVA: p   <  0.001) in estimated parotid gland D50%, spinal cord/brainstem Dmax, and high dose CTV coverage dose violations (Kendall's tau: p   <  0.05). Systematic algorithm differences resulting in workflow discrepancies were: parotid gland volumes (ANOVA: p   <  0.001), external contour deformations (t-test: p   =  0.022), and CTV-to-PTV margins (t-test: 0.009), respectively. Five candidate test cases were verified as representative of the five clusters. The framework successfully clustered workflow outputs and identified five test cases representative of clinically relevant algorithm discrepancies. This approach may improve the allocation of resources during the benchmarking and characterization process and the applicability of results to clinical data.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

A multiparametric method to assess the MIM deformable image registration algorithm

A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM-Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.

An evaluation of techniques for dose calculation on cone beam computed tomography

OBJECTIVE:

To assess the accuracy and efficiency of four different techniques, thus determining the optimum method for recalculating dose on cone beam CT (CBCT) images acquired during radiotherapy treatments.

METHODS:

Four established techniques were investigated and their accuracy assessed via dose calculations: (1) applying a standard planning CT (pCT) calibration curve, (2) applying a CBCT site-specific calibration curve, (3) performing a density override and (4) using deformable registration. Each technique was applied to 15 patients receiving volumetric modulated arc therapy to one of three treatment sites, head and neck, lung and prostate. Differences between pCT and CBCT recalculations were determined with dose volume histogram metrics and 2.0%/0.1 mm gamma analysis using the pCT dose distribution as a reference.

RESULTS:

Dose volume histogram analysis indicated that all techniques yielded differences from expected results between 0.0 and 2.3% for both target volumes and organs at risk. With volumetric gamma analysis, the dose recalculation on deformed images yielded the highest pass-rates. The median pass-rate ranges at 50% threshold were 99.6-99.9%, 94.6-96.0%, and 94.8.0-96.0% for prostate, head and neck and lung patients, respectively.

CONCLUSION:

Deformable registration, HU override and site-specific calibration curves were all identified as dosimetrically accurate and efficient methods for dose calculation on CBCT images.

ADVANCES IN KNOWLEDGE:

With the increasing adoption of CBCT, this study provides clinical radiotherapy departments with invaluable information regarding the comparison of dose reconstruction methods, enabling a more accurate representation of a patient's treatment. It can also integrate studies in which CBCT is used in image-guided radiation therapy and for adaptive radiotherapy planning processes.

Application of aSi-kVCBCT for Volume Assessment and Dose Estimation: An Offline Adaptive Study for Prostate Radiotherapy

Objective:

The purpose of this study is to develop a method to estimate the dose using amorphous silicon detector panel cone beam computed tomography (aSi-kVCBCT) for the OARs and targets in prostate radiotherapy and to compare with the actual planned dose.

Methods:

The aSi-kVCBCT is used widely in radiotherapy to verify the patient position before treatment. The advancement in aSi-kVCBCT combined with adaptive software allows us to verify the dose distribution in daily acquired CBCT images. CBCT images from 10 patients undergoing radical prostate radiotherapy were included in this study. Patients received total dose of 65Gy in 25 fractions using volumetric modulated arc therapy (VMAT). aSi-kVCBCT scans were acquired before daily treatment and exported to smart adapt software for image adaptation. The planning CT is adapted to daily aSi-kVCBCT images in terms of HU mapping. The primary VMAT plans were copied on to the adapted planning CT images and dose was calculated using Anisotropic Analytic Algorithm (AAA). The DVH is then used to evaluate the volume changes of organs at risk (OAR), the actual dose received by OARs, CTV and PTV during a single fraction.

Results:

The normalized volume of the bladder and rectum ranged from 0.70–1.66 and 0.70–1.16 respectively. The cumulative mean Sorensen–Dice coefficient values of bladder and rectum were 0.89±0.04 and 0.79±0.06 respectively. The maximum dose differences for CTV and PTV were 2.5% and -4.7% and minimum were 0.1% and 0.1% respectively.

Conclusion:

The adapted planning CT obtained from daily imaging using aSi-kVCBCT and SmartAdapt^® can be used as an effective tool to estimate the volume changes and dose difference in prostate radiotherapy.

Quantitative analysis of respiration-induced motion of each liver segment with helical computed tomography and 4-dimensional computed tomography

Background

To analyze the respiratory-induced motion of each liver segment using helical computed tomography (helical CT) and 4-dimensional computed tomography (4DCT), and to establish the individual segment expansion margin of internal target volume (ITV) to facilitate target delineation of tumors in different liver segments.

Methods

Twenty patients who received radiotherapy with CT-simulation scanning of the whole liver in both helical CT and 10-phase-gated 4DCT were investigated, including 2 patients with esophagus cancer, 4 with lung cancer, 10 with breast cancer, 2 with liver cancer, 1 with thymoma, and 1 with gastric diffuse large B-cell lymphoma (DLBCL). For each patient, 9 representative points were drawn on the helical CT images of liver segments 1, 2, 3, 4a, 4b, 5, 6, 7, and 8, respectively, and adaptively deformed to 2 phases of the 4DCT images at the end of inspiration (phase 0 CT) and expiration (phase 50 CT) in the treatment planning system. Using the amplitude of each point between phase 0 CT and phase 50 CT, we established quantitative data for the respiration-induced motion of each liver segment in 3-dimensional directions. Moreover, using the amplitude between the original helical CT and both 4DCT images, we rendered the individual segment expansion margin of ITV for hepatic target delineation to cover more than 95% of each tumor.

Results

The average amplitude (mean ± standard deviation) was 0.6 ± 3.0 mm in the left-right (LR) direction, 2.3 ± 2.4 mm in the anterior-posterior (AP) direction, and 5.7 ± 3.4 mm in the superior-inferior (SI) direction, respectively. All of the segments moved posteriorly and superiorly during expiration. Segment 7 had the largest amplitude in the SI direction, at 8.6 ± 3.4 mm. Otherwise, the segments over the lateral side, including segments 2, 3, 6, and 7, had greater excursion in the SI direction compared to the medial segments. To cover more than 95% of each tumor, the required expansion margin of ITV in the LR, AP, and SI directions were at least 2.5 mm, 2.5 mm, and 5.0 mm on average, respectively, with variations between different segments.

Conclusions

The greatest excursion occurred in liver segment 7, followed by the segments over the lateral side in the SI direction. The individual segment expansion margin of ITV is required to delineate targets for each segment and direction.

Variation assessment of deformable registration in stereotactic radiosurgery

INTRODUCTION

The regular functions of CT-MRI registration include delineation of targets and organs-at-risk (OARs) in radiosurgery planning. The question of whether deformable image registration (DIR) could be applied to stereotactic radiosurgery (SRS) in its place remains a subject of debate.

METHODS

This study collected data regarding 16 patients who had undergone single-fraction SRS treatment. All lesions were located close to the brainstem. CT and MRI two image sets were registered by both rigid image registration (RIR) and DIR algorithms. The contours of the OARs were drawn individually on the rigid and deformable CT-MRI image sets by qualified radiation oncologists and dosimetrists. The evaluation metrics included volume overlapping (VO), Dice similarity coefficient (DSC), and dose. The modified demons deformable algorithm (VARIAN SmartAdapt) was used for evaluation in this study.

RESULTS

The mean range of VO for OARs was 0.84 ± 0.08, and DSC was 0.82 ± 0.07. The maximum average volume difference was at normal brain (17.18 ± 14.48 cm³) and the second highest was at brainstem (2.26 cm³ ± 1.18). Pearson correlation testing showed that all DIRs' OAR volumes were linearly and significantly correlated with RIRs' volume (0.679-0.992, two tailed, P << 0.001). The 100% dose was prescribed at gross tumor volume (GTV). The average maximum percent dose difference was observed in brainstem (26.54% ± 27.027), and the average mean dose difference has found at same organ (1.6% ± 1.66).

CONCLUSION

The change in image-registration method definitely produces dose variance, and is significantly more what depending on the target location. The volume size of OARs, however, was not statistical significantly correlated with dose variance.

A method to detect landmark pairs accurately between intra-patient volumetric medical images

PURPOSES

An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively.

METHODS

Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency.

RESULTS

The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm.

CONCLUSION

A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.

RapidPlan head and neck model: the objectives and possible clinical benefit

Background

To evaluate a knowledge based planning model for RapidPlan (RP) generated for advanced head and neck cancer (HNC) patient treatments, as well its ability to possibly improve the clinical plan quality. The stability of the model was assessed also for a different beam geometry, different dose fractionation and different management of bilateral structures (parotids).

Methods

Dosimetric and geometric data from plans of 83 patients presenting HNC were selected for the model training. All the plans used volumetric modulated arc therapy (VMAT, RapidArc) to treat two targets at dose levels of 69.96 and 54.45 Gy in 33 fractions with simultaneous integrated boost. Two models were generated, the first separating the ipsi- and contra-lateral parotids, while the second associating the two parotids to a single structure for training. The optimization objectives were adjusted to the final model to better translate the institutional planning and dosimetric strategies and trade-offs. The models were validated on 20 HNC patients, comparing the RP generated plans and the clinical plans. RP generated plans were also compared between the clinical beam arrangement and a simpler geometry, as well as for a different fractionation scheme.

Results

RP improved significantly the clinical plan quality, with a reduction of 2 Gy, 5 Gy, and 10 Gy of the mean parotid, oral cavity and laryngeal doses, respectively. A simpler beam geometry was deteriorating the plan quality, but in a small amount, keeping a significant improvement relative to the clinical plan. The two models, with one or two parotid structures, showed very similar results. NTCP evaluations indicated the possibility of improving (NTCP decreasing of about 7%) the toxicity profile when using the RP solution.

Conclusions

The HNC RP model showed improved plan quality and planning stability for beam geometry and fractionation. An adequate choice of the objectives in the model is necessary for the trade-offs strategies.

Evaluation of Deformable Image Registration-Based Contour Propagation From Planning CT to Cone-Beam CT

Purpose:

We evaluated the performance of organ contour propagation from a planning computed tomography to cone-beam computed tomography with deformable image registration by comparing contours to manual contouring.

Materials and Methods:

Sixteen patients were retrospectively identified based on showing considerable physical change throughout the course of treatment. Multiple organs in the 3 regions (head and neck, prostate, and pancreas) were evaluated. A cone-beam computed tomography from the end of treatment was registered to the planning computed tomography using rigid registration, followed by deformable image registration. The contours were copied on cone-beam computed tomography image sets using rigid registration and modified by 2 radiation oncologists. Contours were compared using Dice similarity coefficient, mean surface distance, and Hausdorff distance.

Results:

The mean physician-to-physician Dice similarity coefficient for all organs was 0.90. When compared to each physician’s contours, the overall mean for rigid was 0.76 (P < .001), and it was improved to 0.79 (P < .001) for deformable image registration. Comparing deformable image registration to physicians resulted in a mean Dice similarity coefficient of 0.77, 0.74, and 0.84 for head and neck, prostate, and pancreas groups, respectively; whereas, the physician-to-physician mean agreement for these sites was 0.87, 0.90, and 0.93 (P < .001, for all sites). The mean surface distance for physician-to-physician contours was 1.01 mm, compared to 2.58 mm for rigid-to-physician contours and 2.24 mm for deformable image registration-to-physician contours. The mean physician-to-physician Hausdorff distance was 11.32 mm, and when compared to any physician’s contours, the mean for rigid and deformable image registration was 12.1 mm and 12.0 mm (P < .001), respectively.

Conclusion:

The physicians had a high level of agreement via the 3 metrics; however, deformable image registration fell short of this level of agreement. The automatic workflows using deformable image registration to deform contours to cone-beam computed tomography to evaluate the changes during treatment should be used with caution.

Online dosimetric evaluation of larynx SBRT: A pilot study to assess the necessity of adaptive replanning

Purpose

We have initiated a multi‐institutional phase I trial of 5‐fraction stereotactic body radiotherapy (SBRT) for Stage III–IVa laryngeal cancer. We conducted this pilot dosimetric study to confirm potential utility of online adaptive replanning to preserve treatment quality.

Methods

We evaluated ten cases: five patients enrolled onto the current trial and five patients enrolled onto a separate phase I SBRT trial for early‐stage glottic larynx cancer. Baseline SBRT treatment plans were generated per protocol. Daily cone‐beam CT (CBCT) or diagnostic CT images were acquired prior to each treatment fraction. Simulation CT images and target volumes were deformably registered to daily volumetric images, the original SBRT plan was copied to the deformed images and contours, delivered dose distributions were re‐calculated on the deformed CT images. All of these were performed on a commercial treatment planning system. In‐house software was developed to propagate the delivered dose distribution back to reference CT images using the deformation information exported from the treatment planning system. Dosimetric differences were evaluated via dose‐volume histograms.

Results

We could evaluate dose within 10 minutes in all cases. Prescribed coverage to gross tumor volume (GTV) and clinical target volume (CTV) was uniformly preserved; however, intended prescription dose coverage of planning treatment volume (PTV) was lost in 53% of daily treatments (mean: 93.9%, range: 83.9–97.9%). Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases.

Conclusions

Although GTV and CTV SBRT dose coverage is preserved with in‐room three‐dimensional image guidance, PTV coverage can vary significantly from intended plans and dose to critical structures may exceed tolerances. Online adaptive treatment re‐planning is potentially necessary and clinically applicable to fully preserve treatment quality. Confirmatory trial accrual and analysis remains ongoing.

Feasibility of offline head & neck adaptive radiotherapy using deformed planning CT electron density mapping on weekly cone beam computed tomography

OBJECTIVE

The purpose of the study was to use deformable mapping of planning CT (pCT) electron density values on weekly cone-beam CT (CBCT) to quantify the anatomical changes and determine the dose-volume relationship in offline adaptive volumetric-modulated arc therapy.

METHODS

10 patients treated with RapidArc plans who had weekly CBCTs were selected retrospectively. The pCT was deformed to weekly CBCTs and the deformed contours were checked for any discrepancies. Clinical target volume 66 Gy and 60 Gy (CTV₆₆ and CTV₆₀), parotids and spinal cord were the structures selected for analysis. Volume reduction and dice similarity index (DSI) were determined. Hybrid RapidArc plans were created and the cumulative dose-volume histograms for selected structures were analyzed.

RESULTS

Results showed a mean volume reduction of 18.82 ± 6.08% and 18.22 ± 6.1% for Clinical target volume 66 Gy and 60 Gy (CTV₆₆ and CTV₆₀), respectively, and their corresponding DSI values were 0.94 ± 0.03 and 0.95 ± 0.01. Mean volume reductions of left and right parotids were 32.79 ± 10.28% and 29.46 ± 8.78%, respectively, and their corresponding mean DSI values were 0.90 ± 0.05 and 0.89 ± 0.05. The cumulative mean dose difference for Planning target volume 66 Gy (PTV₆₆) was -1.35 ± 1.71% and for Planning target volume 60 Gy (PTV₆₀), it was -0.69 ± 1.37%. Spinal cord doses varied for all patients over the course.

CONCLUSION

The results from the study showed that it is clinically feasible to estimate the dose-volume relationship using deformed pCT. Monitoring of patient anatomic changes and incorporating patient-specific replanning strategy are necessary to avoid critical structure complications. Advances in knowledge: Deformable mapping of pCT electron density values on weekly CBCTs has been performed to establish the volumetric and dosimetric changes. The anatomical changes differ among the patients and hence, the choice for adaptive radiotherapy should be strictly patient specific rather than time specific.

Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints

Background

Deformable image registration (DIR) is a key component in many radiotherapy applications. However, often resulting deformations are not satisfying, since varying deformation properties of different anatomical regions are not considered. To improve the plausibility of DIR in adaptive radiotherapy in the male pelvic area, this work integrates a local rigidity deformation model into a DIR algorithm.

Methods

A DIR framework is extended by constraints, enforcing locally rigid deformation behavior for arbitrary delineated structures. The approach restricts those structures to rigid deformations, while surrounding tissue is still allowed to deform elastically. The algorithm is tested on ten CT/CBCT male pelvis datasets with active rigidity constraints on bones and prostate and compared to the Varian SmartAdapt deformable registration (VSA) on delineations of bladder, prostate and bones.

Results

The approach with no rigid structures (REG0) obtains an average dice similarity coefficient (DSC) of 0.87 ± 0.06 and a Hausdorff-Distance (HD) of 8.74 ± 5.95 mm. The new approach with rigid bones (REG1) yields a DSC of 0.87 ± 0.07, HD 8.91 ± 5.89 mm. Rigid deformation of bones and prostate (REG2) obtains 0.87 ± 0.06, HD 8.73 ± 6.01 mm, while VSA yields a DSC of 0.86 ± 0.07, HD 10.22 ± 6.62 mm. No deformation grid foldings are observed for REG0 and REG1 in 7 of 10 cases; for REG2 in 8 of 10 cases, with no grid foldings in prostate, an average of 0.08 % in bladder (REG2: no foldings) and 0.01 % inside the body contour. VSA exhibits grid foldings in each case, with an average percentage of 1.81 % for prostate, 1.74 % for bladder and 0.12 % for the body contour. While REG1 and REG2 keep bones rigid, elastic bone deformations are observed with REG0 and VSA. An average runtime of 26.2 s was achieved with REG1; 31.1 s with REG2, compared to 10.5 s with REG0 and 10.7 s with VMS.

Conclusions

With accuracy in the range of VSA, the new approach with constraints delivers physically more plausible deformations in the pelvic area with guaranteed rigidity of arbitrary structures. Although the algorithm uses an advanced deformation model, clinically feasible runtimes are achieved.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-016-0697-4) contains supplementary material, which is available to authorized users.

Registering prostate external beam radiotherapy with a boost from high-dose-rate brachytherapy: a comparative evaluation of deformable registration algorithms

BACKGROUND

Registering CTs for patients receiving external beam radiotherapy (EBRT) with a boost dose from high-dose-rate brachytherapy (HDR) can be challenging due to considerable image discrepancies (e.g. rectal fillings, HDR needles, HDR artefacts and HDR rectal packing materials). This study is the first to comparatively evaluate image processing and registration methods used to register the rectums in EBRT and HDR CTs of prostate cancer patients. The focus is on the rectum due to planned future analysis of rectal dose-volume response.

METHODS

For 64 patients, the EBRT CT was retrospectively registered to the HDR CT with rigid registration and non-rigid registration methods in VelocityAI. Image processing was undertaken on the HDR CT and the rigidly-registered EBRT CT to reduce the impact of discriminating features on alternative non-rigid registration methods applied in the software suite for Deformable Image Registration and Adaptive Radiotherapy Research (DIRART) using the Horn-Schunck optical flow and Demons algorithms. The propagated EBRT-rectum structures were compared with the HDR structure using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and average surface distance (ASD). The image similarity was compared using mutual information (MI) and root mean squared error (MSE). The displacement vector field was assessed via the Jacobian determinant (JAC). The post-registration alignments of rectums for 21 patients were visually assessed.

RESULTS

The greatest improvement in the median DSC relative to the rigid registration result was 35 % for the Horn-Schunck algorithm with image processing. This algorithm also provided the best ASD results. The VelocityAI algorithms provided superior HD, MI, MSE and JAC results. The visual assessment indicated that the rigid plus deformable multi-pass method within VelocityAI resulted in the best rectum alignment.

CONCLUSIONS

The DSC, ASD and HD improved significantly relative to the rigid registration result if image processing was applied prior to DIRART non-rigid registrations, whereas VelocityAI without image processing provided significant improvements. Reliance on a single rectum structure-correspondence metric would have been misleading as the metrics were inconsistent with one another and visual assessments. It was important to calculate metrics for a restricted region covering the organ of interest. Overall, VelocityAI generated the best registrations for the rectum according to the visual assessment, HD, MI, MSE and JAC results.