An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy

Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

PURPOSE

The purpose of this study was to investigate the relationship between inter-reader variability in manual prostate contour segmentation on magnetic resonance imaging (MRI) examinations and determine the optimal number of readers required to establish a reliable reference standard.

MATERIALS AND METHODS

Seven radiologists with various experiences independently performed manual segmentation of the prostate contour (whole-gland [WG] and transition zone [TZ]) on 40 prostate MRI examinations obtained in 40 patients. Inter-reader variability in prostate contour delineations was estimated using standard metrics (Dice similarity coefficient [DSC], Hausdorff distance and volume-based metrics). The impact of the number of readers (from two to seven) on segmentation variability was assessed using pairwise metrics (consistency) and metrics with respect to a reference segmentation (conformity), obtained either with majority voting or simultaneous truth and performance level estimation (STAPLE) algorithm.

RESULTS

The average segmentation DSC for two readers in pairwise comparison was 0.919 for WG and 0.876 for TZ. Variability decreased with the number of readers: the interquartile ranges of the DSC were 0.076 (WG) / 0.021 (TZ) for configurations with two readers, 0.005 (WG) / 0.012 (TZ) for configurations with three readers, and 0.002 (WG) / 0.0037 (TZ) for configurations with six readers. The interquartile range decreased slightly faster between two and three readers than between three and six readers. When using consensus methods, variability often reached its minimum with three readers (with STAPLE, DSC = 0.96 [range: 0.945-0.971] for WG and DSC = 0.94 [range: 0.912-0.957] for TZ, and interquartile range was minimal for configurations with three readers.

CONCLUSION

The number of readers affects the inter-reader variability, in terms of inter-reader consistency and conformity to a reference. Variability is minimal for three readers, or three readers represent a tipping point in the variability evolution, with both pairwise-based metrics or metrics with respect to a reference. Accordingly, three readers may represent an optimal number to determine references for artificial intelligence applications.

A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy

Purpose/objectives

Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI auto-segmentation contours produced by five commercial vendors against a common dataset.

Methods and materials

The organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients. Comparisons were made using geometric similarity metrics including volumetric and surface Dice similarity coefficient (vDSC and sDSC), Hausdorff distance (HD) and Added Path Length (APL). To assess the time saved, the time taken to manually draw the expert contours, as well as the time to correct the AI contours, were recorded.

Results

There are differences in the number of CT contours offered by each AI auto-segmentation solution at the time of the study (Mir 99; MV 143; Rad 83; Ray 67; Ther 86), with all offering contours of some lymph node levels as well as OARs. Averaged across all structures, the median vDSCs were good for all systems and compared favorably with existing literature: Mir 0.82; MV 0.88; Rad 0.86; Ray 0.87; Ther 0.88. All systems offer substantial time savings, ranging between: breast 14-20 mins; head and neck 74-93 mins; lung 20-26 mins; prostate 35-42 mins. The time saved, averaged across all structures, was similar for all systems: Mir 39.8 mins; MV 43.6 mins; Rad 36.6 min; Ray 43.2 mins; Ther 45.2 mins.

Conclusions

All five commercial AI auto-segmentation solutions evaluated in this work offer high quality contours in significantly reduced time compared to manual contouring, and could be used to render the radiotherapy workflow more efficient and standardized.

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.

Validation of accuracy deformable image registration contour propagation using a benchmark virtual HN phantom dataset

Virtual anatomic phantoms offer precise voxel mapping of the variation of anatomy with ground truth deformation vector fields (DVFs). Dice similarity coefficient (DSC) and mean distance to agreement (MDA) are the standard metrics for evaluating geometric contour congruence when testing deformable registration (DIR) algorithms. A HN virtual patient phantom data set was used for a kVCT‐kVCT automatic propagation contour validation study employing the Accuray DIR algorithm. Furthermore, since TomoTherapy uses MVCT images of the relevant anatomy for adaptive monitoring, the kVCT image data set quality was transformed to an MVCT image data set quality to study intermodal kVCT‐MVCT DIR accuracy. The results of the study indicate that the Accuray DIR algorithm can be expected to autopropagate HN contours adequately, on average, within tolerances recommended by TG‐132 (DSC 0.8‐0.9, MDA within voxel width). However, contours critical to dosimetric planning should always be visually proofed for accuracy. Using standard reconstruction MVCT image quality causes slightly less, but acceptable, agreement with ground truth contours.

[Multi-scale 3D convolutional neural network-based segmentation of head and neck organs at risk]

OBJECTIVE

To establish an algorithm based on 3D convolution neural network to segment the organs at risk (OARs) in the head and neck on CT images.

METHODS

We propose an automatic segmentation algorithm of head and neck OARs based on V-Net. To enhance the feature expression ability of the 3D neural network, we combined the squeeze and exception (SE) module with the residual convolution module in V-Net to increase the weight of the features that has greater contributions to the segmentation task. Using a multi-scale strategy, we completed organ segmentation using two cascade models for location and fine segmentation, and the input image was resampled to different resolutions during preprocessing to allow the two models to focus on the extraction of global location information and local detail features respectively.

RESULTS

Our experiments on segmentation of 22 OARs in the head and neck indicated that compared with the existing methods, the proposed method achieved better segmentation accuracy and efficiency, and the average segmentation accuracy was improved by 9%. At the same time, the average test time was reduced from 33.82 s to 2.79 s.

CONCLUSIONS

The 3D convolution neural network based on multi-scale strategy can effectively and efficiently improve the accuracy of organ segmentation and can be potentially used in clinical setting for segmentation of other organs to improve the efficiency of clinical treatment.

Automatic large quantity landmark pairs detection in 4DCT lung images

PURPOSE

To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy.

METHODS

A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs.

RESULTS

Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm.

CONCLUSION

A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.

Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT

INTRODUCTION

The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images.

METHODS

The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses.

RESULTS

The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration.

CONCLUSIONS

The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning

OBJECTIVE

Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs.

METHODS

The ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samples t tests and analysis of variance were employed for statistical analysis.

RESULTS

ODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p = 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients.

CONCLUSION

The ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC.

KEY POINTS

• A fully automated deep-learning method (ODS net) is developed to detect and segment OARs in clinical CT images. • This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning. • This deep-learning-based framework delineating a single image requires approximately 30 s, which is suitable for clinical workflows.

Evaluation of automatic contour propagation in T2-weighted 4DMRI for normal-tissue motion assessment using internal organ-at-risk volume (IRV)

Purpose

The purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion.

Methods and Materials

T2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm.

Results

Visual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI.

Conclusion

Automatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy

PURPOSE

To determine whether serial cone beam computed tomography (CBCT) images taken during head and neck radiation therapy (HNR) can improve chronic xerostomia prediction.

METHODS AND MATERIALS

In a retrospective analysis, parotid glands (PGs) were delineated on daily kV CBCT images using deformable image registration for 119 HNR patients (60 or 70 Gy in 2 Gy fractions over 6 or 7 weeks). Deformable image registration accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient and mean distance to agreement in comparison with manually drawn contours. Average weekly changes in CBCT-measured mean Hounsfield unit intensity and volume were calculated for each PG relative to week 1. Dose-volume histogram statistics were extracted from each plan, and interactions among dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at 1 year after HNR. Models including CBCT delta imaging features were compared with clinical and dose-volume histogram-only models using area under the receiver operating characteristic curve (AUC) for grade ≥1 and grade ≥2 xerostomia prediction.

RESULTS

All patients experienced end-treatment PG volume reduction with mean (range) ipsilateral and contralateral PG shrinkage of 19.6% (0.9%-58.4%) and 17.7% (4.4%-56.3%), respectively. Midtreatment volume change was highly correlated with mean PG dose (r = -0.318, P < 1e^-6). Incidence of grade ≥1 and grade ≥2 xerostomia was 65% and 16%, respectively. For grade ≥1 xerostomia prediction, the delta-imaging model had an AUC of 0.719 (95% confidence interval [CI], 0.603-0.830), compared with 0.709 (95% CI, 0.603-0.815) for the dose/clinical model. For grade ≥2 xerostomia prediction, the dose/clinical model had an AUC of 0.692 (95% CI, 0.615-0.770), and the addition of contralateral PG changes modestly improved predictive performance, with an AUC of 0.776 (0.643-0.912).

CONCLUSIONS

The rate of CBCT-measured PG image feature changes improves prediction over dose alone for chronic xerostomia prediction. Analysis of CBCT images acquired for treatment positioning may provide an inexpensive monitoring system to support toxicity-reducing adaptive radiation therapy.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R²  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Segmentation of parotid glands from registered CT and MR images

PURPOSE

To develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.

METHODS

Magnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.

RESULTS

Using the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.

CONCLUSIONS

Automatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.

Contour propagation for lung tumor delineation in 4D-CT using tensor-product surface of uniform and non-uniform closed cubic B-splines

A robust contour propagation method is proposed to help physicians delineate lung tumors on all phase images of four-dimensional computed tomography (4D-CT) by only manually delineating the contours on a reference phase. The proposed method models the trajectory surface swept by a contour in a respiratory cycle as a tensor-product surface of two closed cubic B-spline curves: a non-uniform B-spline curve which models the contour and a uniform B-spline curve which models the trajectory of a point on the contour. The surface is treated as a deformable entity, and is optimized from an initial surface by moving its control vertices such that the sum of the intensity similarities between the sampling points on the manually delineated contour and their corresponding ones on different phases is maximized. The initial surface is constructed by fitting the manually delineated contour on the reference phase with a closed B-spline curve. In this way, the proposed method can focus the registration on the contour instead of the entire image to prevent the deformation of the contour from being smoothed by its surrounding tissues, and greatly reduce the time consumption while keeping the accuracy of the contour propagation as well as the temporal consistency of the estimated respiratory motions across all phases in 4D-CT. Eighteen 4D-CT cases with 235 gross tumor volume (GTV) contours on the maximal inhale phase and 209 GTV contours on the maximal exhale phase are manually delineated slice by slice. The maximal inhale phase is used as the reference phase, which provides the initial contours. On the maximal exhale phase, the Jaccard similarity coefficient between the propagated GTV and the manually delineated GTV is 0.881 [Formula: see text] 0.026, and the Hausdorff distance is 3.07 [Formula: see text] 1.08 mm. The time for propagating the GTV to all phases is 5.55 [Formula: see text] 6.21 min. The results are better than those of the fast adaptive stochastic gradient descent B-spline method, the 3D  +  t B-spline method and the diffeomorphic demons method. The proposed method is useful for helping physicians delineate target volumes efficiently and accurately.

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.

Automatic contour propagation using deformable image registration to determine delivered dose to spinal cord in head-and-neck cancer radiotherapy

To determine delivered dose to the spinal cord, a technique has been developed to propagate manual contours from kilovoltage computed-tomography (kVCT) scans for treatment planning to megavoltage computed-tomography (MVCT) guidance scans. The technique uses the Elastix software to perform intensity-based deformable image registration of each kVCT scan to the associated MVCT scans. The registration transform is then applied to contours of the spinal cord drawn manually on the kVCT scan, to obtain contour positions on the MVCT scans. Different registration strategies have been investigated, with performance evaluated by comparing the resulting auto-contours with manual contours, drawn by oncologists. The comparison metrics include the conformity index (CI), and the distance between centres (DBC). With optimised registration, auto-contours generally agree well with manual contours. Considering all 30 MVCT scans for each of three patients, the median CI is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.759 \pm 0.003$ \end{document}0.759±0.003, and the median DBC is (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.87 \pm 0.01$ \end{document}0.87±0.01) mm. An intra-observer comparison for the same scans gives a median CI of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.820 \pm 0.002$ \end{document}0.820±0.002 and a DBC of (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.64 \pm 0.01$ \end{document}0.64±0.01) mm. Good levels of conformity are also obtained when auto-contours are compared with manual contours from one observer for a single MVCT scan for each of 30 patients, and when they are compared with manual contours from six observers for two MVCT scans for each of three patients. Using the auto-contours to estimate organ position at treatment time, a preliminary study of 33 patients who underwent radiotherapy for head-and-neck cancers indicates good agreement between planned and delivered dose to the spinal cord.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks

PURPOSE

Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability.

METHODS

Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image. For CNN training, we extracted a representative number of positive intensity patches around voxels that belong to the OAR of interest in training CT images, and negative intensity patches around voxels that belong to the surrounding structures. These patches then passed through a sequence of CNN layers that captured local image features such as corners, end-points, and edges, and combined them into more complex high-order features that can efficiently describe the OAR. The trained network was applied to classify voxels in a region of interest in the test image where the corresponding OAR is expected to be located. We then smoothed the obtained classification results by using Markov random fields algorithm. We finally extracted the largest connected component of the smoothed voxels classified as the OAR by CNN, performed dilate-erode operations to remove cavities of the component, which resulted in segmentation of the OAR in the test image.

RESULTS

The performance of CNNs was validated on segmentation of spinal cord, mandible, parotid glands, submandibular glands, larynx, pharynx, eye globes, optic nerves, and optic chiasm using 50 CT images. The obtained segmentation results varied from 37.4% Dice coefficient (DSC) for chiasm to 89.5% DSC for mandible. We also analyzed the performance of state-of-the-art algorithms and commercial software reported in the literature, and observed that CNNs demonstrate similar or superior performance on segmentation of spinal cord, mandible, parotid glands, larynx, pharynx, eye globes, and optic nerves, but inferior performance on segmentation of submandibular glands and optic chiasm.

CONCLUSION

We concluded that convolution neural networks can accurately segment most of OARs using a representative database of 50 HaN CT images. At the same time, inclusion of additional information, for example, MR images, may be beneficial to some OARs with poorly visible boundaries.

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification

MOTIVATION

Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed.

OBJECTIVE

This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes.

METHODS

Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined.

RESULTS

Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems.

CONCLUSION

Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy

Purpose:

To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it.

Materials and Methods:

Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.

Results:

A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.

Conclusion:

The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Texture analysis to assess structural modifications induced by radiotherapy

Texture analysis is an emerging tool employed in Radiotherapy (RT) to improve tumor characterization for planning and to evaluate treatment effects. In the treatment of Head and Neck Cancer, parotid glands can receive high dose that may compromise gland functionality and structure. Texture analysis was here applied on CT images of Head and Neck to evaluate changes in parotid gland structure during RT. CT images at the beginning, at the intermediate stage and at the end of RT were considered and in each time point different features (i.e. mean intensity, variance, entropy, homogeneity, local entropy, fractal dimension and volume) were extracted within parotid volume. A general decrease in tissue complexity and heterogeneity was found, with different time trend for textural features. This is explainable by different biological mechanisms associated to the variation of each index. Volume and mean intensity variation are also correlated with some pre-treatment dosimetric parameters, indicating a relationship between the dose plan and the structural variation estimated after RT.

Roles of Deformable Image Registration in adaptive RT: From contour propagation to dose monitoring

Adaptive radiation therapy (ART) is based on the optimization of the treatment plan during the treatment delivery to compensate for anatomical deformations. Deformable Image Registration (DIR) then constitutes a key step in order to analyze the huge amount of daily or weekly images to provide clinically usefull information. Two main applications of DIR have been developped in ART: delineation propagation and dose accumulation. If delineation propagation is well validated and transfered in the clinic, some challenges remain to address for dose accumulation. In this paper, we review the recent developments of DIR in ART, particularly in prostate and head-and-neck (H&N), with a focus on their evaluation.

Analysis of serial CT images for studying the RT effects in head-neck cancer patients

Images taken during and after RT for head and neck cancer have the potential to quantitatively assess xerostomia. Image information may be used as biomarkers of RT effects on parotid glands with significant potential to support adaptive treatment strategies. We investigated the possibility to extract information based on in-room CT images (kVCT, MVCT), acquired for daily image-guided radiotherapy treatment of head-and-neck cancer patients, in order to predict individual response in terms of toxicity. Follow-up MRI images were also used in order to investigate long term parotid gland deformation.

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

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

High quality surface reconstruction in radiotherapy: Cross-sectional contours to 3D mesh using wavelets

A novel approach for three-dimensional (3D) surface reconstruction of anatomical structures in radiotherapy (RT) is presented. This is obtained from manual cross-sectional contours by combining both image voxel segmentation processing and implicit surface streaming methods using wavelets. 3D meshes reconstructed with the proposed approach are compared to those obtained from traditional triangulation algorithm. Qualitative and quantitative evaluations are performed in terms of mesh quality metrics. Differences in smoothness, detail and accuracy are observed in the comparison, considering three different anatomical districts and several organs at risk in radiotherapy. Overall best performances were recorded for the proposed approach, regardless the complexity of the anatomical structure. This demonstrates the efficacy of the proposed approach for the 3D surface reconstruction in radiotherapy and allows for further specific image analyses using real biomedical data.

Vision 20/20: perspectives on automated image segmentation for radiotherapy

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

Early changes of parotid density and volume predict modifications at the end of therapy and intensity of acute xerostomia

PURPOSE

To quantitatively assess the predictive power of early variations of parotid gland volume and density on final changes at the end of therapy and, possibly, on acute xerostomia during IMRT for head-neck cancer.

MATERIALS AND METHODS

Data of 92 parotids (46 patients) were available. Kinetics of the changes during treatment were described by the daily rate of density (rΔρ) and volume (rΔvol) variation based on weekly diagnostic kVCT images. Correlation between early and final changes was investigated as well as the correlation with prospective toxicity data (CTCAEv3.0) collected weekly during treatment for 24/46 patients.

RESULTS

A higher rΔρ was observed during the first compared to last week of treatment (-0,50 vs -0,05HU, p-value = 0.0001). Based on early variations, a good estimation of the final changes may be obtained (Δρ: AUC = 0.82, p = 0.0001; Δvol: AUC = 0.77, p = 0.0001). Both early rΔρ and rΔvol predict a higher "mean" acute xerostomia score (≥ median value, 1.57; p-value = 0.01). Median early density rate changes for patients with mean xerostomia score ≥ / < 1.57 were -0.98 vs -0.22 HU/day respectively (p = 0.05).

CONCLUSIONS

Early density and volume variations accurately predict final changes of parotid glands. A higher longitudinally assessed score of acute xerostomia is well predicted by higher rΔρ and rΔvol in the first two weeks of treatment: best cut-off values were -0.50 HU/day and -380 mm(3)/day for rΔρ and rΔvol respectively. Further studies are necessary to definitively assess the potential of early density/volume changes in identifying more sensitive patients at higher risk of experiencing xerostomia.

Use of articulated registration for response assessment of individual metastatic bone lesions

Accurate skeleton registration is necessary to match corresponding metastatic bone lesions for response assessment over multiple scans. In articulated registration (ART), whole-body skeletons are registered by auto-segmenting individual bones, then rigidly aligning them. Performance and robustness of the ART in lesion matching were evaluated and compared to other commonly used registration techniques. Sixteen prostate cancer patients were treated either with molecular targeted therapy or chemotherapy. Ten out of the 16 patients underwent the double baseline whole-body [F-18]NaF PET/CT scans for test-retest (TRT) evaluation. Twelve of the 16 patients underwent pre- and mid-treatment [F-18]NaF PET/CT scans. Skeletons at different time points were registered using ART, rigid, and deformable (DR) registration algorithms. The corresponding lesions were contoured and identified on successive PET images based on including the voxels with the standardized uptake value over 15. Each algorithm was evaluated for its ability to accurately align corresponding lesions via skeleton registration. A lesion matching score (MS) was measured for each lesion, which quantified the per cent overlap between the lesion's two corresponding contours. Three separate sensitivity studies were conducted to investigate the robustness of ART in matching: sensitivity of lesion matching to various contouring threshold levels, effects of imperfections in the bone auto-segmentation and sensitivity of mis-registration. The performance of ART (MS = 82% for both datasets, p ≪ 0.001) in lesion matching was significantly better than rigid (MS(TRT)=53%, MS(Response)= 46%) and DR (MS(TRT)=46%, MS(Response)=45%) algorithms. Neither varying threshold levels for lesion contouring nor imperfect bone segmentation had significant (p~0.10) impact on the ART matching performance as the MS remained unchanged. Despite the mis-registration reduced MS for ART, as low as 67% (p ≪ 0.001), the performance remained to be superior to the rigid and DR algorithms. ART is not only robust to contouring threshold levels for bone lesions, but also outperforms rigid and DR algorithms in lesion matching. ART therefore enables the study of TRT variability and treatment assessment of individual bone lesions.

Development and evaluation of an articulated registration algorithm for human skeleton registration

Accurate registration over multiple scans is necessary to assess treatment response of bone diseases (e.g. metastatic bone lesions). This study aimed to develop and evaluate an articulated registration algorithm for the whole-body skeleton registration in human patients. In articulated registration, whole-body skeletons are registered by auto-segmenting into individual bones using atlas-based segmentation, and then rigidly aligning them. Sixteen patients (weight = 80-117 kg, height = 168-191 cm) with advanced prostate cancer underwent the pre- and mid-treatment PET/CT scans over a course of cancer therapy. Skeletons were extracted from the CT images by thresholding (HU>150). Skeletons were registered using the articulated, rigid, and deformable registration algorithms to account for position and postural variability between scans. The inter-observers agreement in the atlas creation, the agreement between the manually and atlas-based segmented bones, and the registration performances of all three registration algorithms were all assessed using the Dice similarity index-DSIobserved, DSIatlas, and DSIregister. Hausdorff distance (dHausdorff) of the registered skeletons was also used for registration evaluation. Nearly negligible inter-observers variability was found in the bone atlases creation as the DSIobserver was 96 ± 2%. Atlas-based and manual segmented bones were in excellent agreement with DSIatlas of 90 ± 3%. Articulated (DSIregsiter = 75 ± 2%, dHausdorff = 0.37 ± 0.08 cm) and deformable registration algorithms (DSIregister = 77 ± 3%, dHausdorff = 0.34 ± 0.08 cm) considerably outperformed the rigid registration algorithm (DSIregsiter = 59 ± 9%, dHausdorff = 0.69 ± 0.20 cm) in the skeleton registration as the rigid registration algorithm failed to capture the skeleton flexibility in the joints. Despite superior skeleton registration performance, deformable registration algorithm failed to preserve the local rigidity of bones as over 60% of the skeletons were deformed. Articulated registration is superior to rigid and deformable registrations by capturing global flexibility while preserving local rigidity inherent in skeleton registration. Therefore, articulated registration can be employed to accurately register the whole-body human skeletons, and it enables the treatment response assessment of various bone diseases.

Upgrade and benchmarking of a 4D treatment planning system for scanned ion beam therapy

PURPOSE

Upgrade and benchmarking of a research 4D treatment planning system (4DTPS) suitable for realistic patient treatment planning and treatment simulations taking into account specific requirements for scanned ion beam therapy, i.e., modeling of dose heterogeneities due to interplay effects and range changes caused by patient motion and dynamic beam delivery.

METHODS

The 4DTPS integrates data interfaces to 4D computed tomography (4DCT), deformable image registration and clinically used motion monitoring devices. The authors implemented a novel data model for 4D image segmentation using Boolean mask volume datasets and developed an algorithm propagating a manually contoured reference contour dataset to all 4DCT phases. They further included detailed treatment simulation and dose reconstruction functionality, based on the irregular patient motion and the temporal structure of the beam delivery. The treatment simulation functionality was validated against experimental data from irradiation of moving radiographic films in air, 3D moving ionization chambers in a water phantom, and moving cells in a biological phantom with a scanned carbon ion beam. The performance of the program was compared to results obtained with predecessor programs.

RESULTS

The measured optical density distributions of the radiographic films were reproduced by the simulations to (-2 ± 12)%. Compared to earlier versions of the 4DTPS, the mean agreement improved by 2%, standard deviations were reduced by 7%. The simulated dose to the moving ionization chambers in water showed an agreement with the measured dose of (-1 ± 4)% for the typical beam configuration. The mean deviation of the simulated from the measured biologically effective dose determined via cell survival was (617 ± 538) mGy relative biological effectiveness corresponding to (10 ± 9)%.

CONCLUSIONS

The authors developed a research 4DTPS suitable for realistic treatment planning on patient data and capable of simulating dose delivery to a moving patient geometry for scanned ion beams. The accuracy and reliability of treatment simulations improved considerably with respect to earlier versions of the 4DTPS.

Mesh-based approach for the 3D analysis of anatomical structures of interest in radiotherapy

In this paper a method based on mesh surfaces approximations for the 3D analysis of anatomical structures in Radiotherapy (RT) is presented. Parotid glands meshes constructed from Megavoltage CT (MVCT) images were studied in terms of volume, distance between center of mass (distCOM) of the right and left parotids, dice similarity coefficient (DICE), maximum distance between meshes (DMax) and the average symmetric distance (ASD). A comparison with the standard binary images approach was performed. While absence of significant differences in terms of volume, DistCOM and DICE indices suggests that both approaches are comparable, the fact that the ASD showed significant difference (p=0.002) and the DMax was almost significant (p=0.053) suggests that the mesh approach should be adopted to provide accurate comparison between 3D anatomical structures of interest in RT.

Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy

PURPOSE

The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment.

METHOD

We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT.

RESULTS

Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found.

CONCLUSION

The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

Normal tissue anatomy for oropharyngeal cancer: contouring variability and its impact on optimization

PURPOSE

To evaluate the variability of organ at risk (OAR) delineation and the resulting impact on intensity modulated radiation therapy (IMRT) treatment plan optimization in head-and-neck cancer.

METHODS AND MATERIALS

An expert panel of 3 radiation oncologists jointly delineated OARs, including the parotid and submandibular glands (SM), pharyngeal constrictors (PC), larynx, and glottis (GL), in 10 patients with advanced oropharynx cancer in 3 contouring sessions, spaced at least 1 week apart. Contour variability and uncertainty, as well as their dosimetric impact on IMRT planning for each case, were assessed.

RESULTS

The mean difference in total volume for each OAR was 1 cm(3) (σ 0.5 cm(3)). Mean fractional overlap was 0.7 (σ 0.1) and was highest (0.8) for the larynx and bilateral SMs and parotids and lowest (0.5) for PC. There were considerable spatial differences in contours, with the ipsilateral parotid and PC displaying the most variability (0.9 cm), which was most prominent in cases in which tumors obliterated fat planes. Both SMs and GL had the smallest differences (0.5 cm). The mean difference in OAR dose was 0.9 Gy (range 0.6-1.1 Gy, σ 0.1 Gy), with the smallest difference for GL and largest for both SMs and the larynx.

CONCLUSIONS

Despite substantial difference in OAR contours, optimization was barely affected, with a 0.9-Gy mean difference between optimizations, suggesting relative insensitivity of dose distributions for IMRT of oropharynx cancer to the extent of OARs.

Introducing the Jacobian-volume-histogram of deforming organs: application to parotid shrinkage evaluation

The Jacobian of the deformation field of elastic registration between images taken during radiotherapy is a measure of inter-fraction local deformation. The histogram of the Jacobian values (Jac) within an organ was introduced (JVH-Jacobian-volume-histogram) and first applied in quantifying parotid shrinkage. MVCTs of 32 patients previously treated with helical tomotherapy for head-neck cancers were collected. Parotid deformation was evaluated through elastic registration between MVCTs taken at the first and last fractions. Jac was calculated for each voxel of all parotids, and integral JVHs were calculated for each parotid; the correlation between the JVH and the planning dose-volume histogram (DVH) was investigated. On average, 82% (±17%) of the voxels shrinks (Jac < 1) and 14% (±17%) shows a local compression >50% (Jac < 0.5). The best correlation between the DVH and the JVH was found between V10 and V15, and Jac < 0.4-0.6 (p < 0.01). The best constraint predicting a higher number of largely compressing voxels (Jac0.5<7.5%, median value) was V15 ≥ 75% (OR: 7.6, p = 0.002). Jac and the JVH are promising tools for scoring/modelling toxicity and for evaluating organ/contour variations with potential applications in adaptive radiotherapy.

Elastic registration based on particle filter in radiotherapy images with brain deformations

This paper presents the evaluation of the accuracy of an elastic registration algorithm, based on the particle filter and an optical flow process. The algorithm is applied in brain CT and MRI simulated image datasets, and MRI images from a real clinical radiotherapy case. To validate registration accuracy, standard indices for registration accuracy assessment were calculated: the dice similarity coefficient (DICE), the average symmetric distance (ASD) and the maximal distance between pixels (Dmax). The results showed that this registration process has good accuracy, both qualitatively and quantitatively, suggesting that this method may be considered as a good new option for radiotherapy applications like patient's follow up treatment.