Imaging dose from cone beam computed tomography in radiation therapy

AbstractImaging dose in radiation therapy has traditionally been ignored due to its low magnitude and frequency in comparison to therapeutic dose used to treat patients. The advent of modern, volumetric, imaging modalities, often as an integral part of linear accelerators, has facilitated the implementation of image-guided radiation therapy (IGRT), which is often accomplished by daily imaging of patients. Daily imaging results in additional dose delivered to patient that warrants new attention be given to imaging dose. This review summarizes the imaging dose delivered to patients as the result of cone beam computed tomography (CBCT) imaging performed in radiation therapy using current methods and equipment. This review also summarizes methods to calculate the imaging dose, including the use of Monte Carlo (MC) and treatment planning systems (TPS). Peripheral dose from CBCT imaging, dose reduction methods, the use of effective dose in describing imaging dose, and the measurement of CT dose index (CTDI) in CBCT systems are also reviewed

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology

Sensitivity of standardised radiomics algorithms to mask generation across different software platforms

The field of radiomics continues to converge on a standardised approach to image processing and feature extraction. Conventional radiomics requires a segmentation. Certain features can be sensitive to small contour variations. The industry standard for medical image communication stores contours as coordinate points that must be converted to a binary mask before image processing can take place. This study investigates the impact that the process of converting contours to mask can have on radiomic features calculation. To this end we used a popular open dataset for radiomics standardisation and we compared the impact of masks generated by importing the dataset into 4 medical imaging software. We interfaced our previously standardised radiomics platform with these software using their published application programming interface to access image volume, masks and other data needed to calculate features. Additionally, we used super-sampling strategies to systematically evaluate the impact of contour data pre processing methods on radiomic features calculation. Finally, we evaluated the effect that using different mask generation approaches could have on patient clustering in a multi-center radiomics study. The study shows that even when working on the same dataset, mask and feature discrepancy occurs depending on the contour to mask conversion technique implemented in various medical imaging software. We show that this also affects patient clustering and potentially radiomic-based modelling in multi-centre studies where a mix of mask generation software is used. We provide recommendations to negate this issue and facilitate reproducible and reliable radiomics

Impact of 18F-Choline PET scan acquisition time on delineation of GTV in prostate cancer [Poster Abstract]

Background: Dose painting radiotherapy requires accurate outlining of primary tumour volumes in the prostate. T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) is the best imaging method for defining the gross tumour volume (GTV). Choline positron emission tomography (PET) is currently a controversial tracer. The image acquisition differs significantly in published studies. Many used early static imaging. One study found that 18F-choline PET/CT with late image acquisition has superior accuracy to T2W MR and functional MR alone1. We investigate whether increasing 18F-Choline PET scan acquisition time from 60 (PET-60) to 90 (PET-90) minutes improves GTV TVD. Methods. Analysis was performed on 9 18F-Choline PET scans. Patients were injected with 370MBq of activity. Three clinicians (C1, C2 and C3) independently and without reference to each other contoured GTVs on each of the T2W-MRI, PET-60 and PET-90 scans at differing times. Scans were registered by a clinician using rigid co-registration. The treating clinicians MRI contour was used as a reference contour. The resulting PET and MRI GTVs were transferred to the PET-60 and PET-90 scans after image registration. The Dice Similarity Coefficient (DSC), Specificity (Sp) and Sensitivity (S) were calculated from contour mask voxel analysis. Results. Table 1 shows the mean and range DSC, S and Sp scores on MRI, PET-60 and PET-90 for C1, C2 and C3 in comparison to the treating clinicians contour on MRI (C1). A 2 sampled T-test (P < 0.01) showed, no significant difference in the Sp, S and DSC between GTVs on PET-60 and PET-90 scans. Further to this, as shown in Figure 1, variability in GTV delineation is significant between observers in a singular case as well as across imaging modalities. Conclusion. Compared to MRI delineated GTVs, 18F-Choline PET GTVs are significantly different. This study found however that increasing the PET scan acquisition time from 60 to 90 minutes did not improve the performance of GTV TVD in comparison to MRI delineated GTV

A novel phantom technique for evaluating the performance of PET auto-segmentation methods in delineating heterogeneous and irregular lesions

Background Positron Emission Tomography (PET)-based automatic segmentation (PET-AS) methods can improve tumour delineation for radiotherapy treatment planning, particularly for Head and Neck (H&N) cancer. Thorough validation of PET-AS on relevant data is currently needed. Printed subresolution sandwich (SS) phantoms allow modelling heterogeneous and irregular tracer uptake, while providing reference uptake data. This work aimed to demonstrate the usefulness of the printed SS phantom technique in recreating complex realistic H&N radiotracer uptake for evaluating several PET-AS methods. Methods Ten SS phantoms were built from printouts representing 2mm-spaced slices of modelled H&N uptake, printed using black ink mixed with 18F-fluorodeoxyglucose, and stacked between 2mm thick plastic sheets. Spherical lesions were modelled for two contrasted uptake levels, and irregular and spheroidal tumours were modelled for homogeneous, and heterogeneous uptake including necrotic patterns. The PET scans acquired were segmented with ten custom PET-AS methods: adaptive iterative thresholding (AT), region growing, clustering applied to 2 to 8 clusters, and watershed transform-based segmentation. The difference between the resulting contours and the ground truth from the image template was evaluated using the Dice Similarity Coefficient (DSC), Sensitivity and Positive Predictive value. Results Realistic H&N images were obtained within 90 min of preparation. The sensitivity of binary PET-AS and clustering using small numbers of clusters dropped for highly heterogeneous spheres. The accuracy of PET-AS methods dropped between 4% and 68% for irregular lesions compared to spheres of the same volume. For each geometry and uptake modelled with the SS phantoms, we report the number of clusters resulting in optimal segmentation. Radioisotope distributions representing necrotic uptakes proved most challenging for most methods. Two PET-AS methods did not include the necrotic region in the segmented volume. Conclusions Printed SS phantoms allowed identifying advantages and drawbacks of the different methods, determining the most robust PET-AS for the segmentation of heterogeneities and complex geometries, and quantifying differences across methods in the delineation of necrotic lesions. The printed SS phantom technique provides key advantages in the development and evaluation of PET segmentation methods and has a future in the field of radioisotope imaging

The influence of dose distribution on treatment outcome in the SCOPE 1 oesophageal cancer trial

Purpose The first aim of this study was to assess plan quality using a conformity index (CI) and analyse its influence on patient outcome. The second aim was to identify whether clinical and technological factors including planning treatment volume (PTV) volume and treatment delivery method could be related to the CI value. Methods and materials By extending the original concept of the mean distance to conformity (MDC) index, the OverMDC and UnderMDC of the 95 % isodose line (50Gy prescribed dose) to the PTV was calculated for 97 patients from the UK SCOPE 1 trial (ISCRT47718479). Data preparation was carried out in CERR, with Kaplan-Meier and multivariate analysis undertaken in EUCLID and further tests in Microsoft Excel and IBM’s SPSS. Results A statistically significant breakpoint in the overall survival data, independent of cetuximab, was found with OverMDC (4.4 mm, p < 0.05). This was not the case with UnderMDC. There was a statistically significant difference in PTV volume either side of the OverMDC breakpoint (Mann Whitney p < 0.001) and in OverMDC value dependent on the treatment delivery method (mean IMRT = 2.1 mm, mean 3D-CRT = 4.1 mm Mann Whitney p < 0.001). Re-planning the worst performing patients according to OverMDC from 3D-CRT to VMAT resulted in a mean reduction in OverMDC of 2.8 mm (1.6–4.0 mm). OverMDC was not significant in multivariate analysis that included age, sex, staging, tumour type, and position. Conclusion Although not significant when included in multivariate analysis, we have shown in univariate analysis that a patient’s OverMDC is correlated with overall survival. OverMDC is strongly related to IMRT and to a lesser extent with PTV volume. We recommend that VMAT planning should be used for oesophageal planning when available and that attention should be paid to the conformity of the 95 % to the PTV

A heterogeneous phantom study for investigating the stability of PET images radiomic features with varying reconstruction settings

The purpose of this work was to assess the capability of radiomic features in distinguishing PET image regions with different uptake patterns. Furthermore, we assessed the stability of PET radiomic features with varying image reconstruction settings. An in-house phantom was designed and constructed, consisting of homogenous and heterogenous artificial phantom inserts. Four artificially constructed inserts were placed into a water filled phantom and filled with varying levels of radioactivity to simulate homogeneous and heterogeneous uptake patterns. The phantom was imaged for 80 min. PET images were reconstructed whilst varying reconstruction parameters. The parameters adjusted included, number of ordered subsets, number of iterations, use of time-of-flight and filter cut off. Regions of interest (ROI) were established by segmentation of the phantom inserts from the reconstructed images. In total seventy eight 3D radiomic features for each ROI with unique reconstructed parameters were extracted. The Friedman test was used to determine the statistical power of each radiomic feature in differentiating phantom inserts with different hetero/homogeneous configurations. The Coefficient of Variation (COV) of each feature, with respect to the reconstruction setting was used to determine feature stability. Forty three out of seventy eight radiomic features were found to be stable (COV ≤ 5%) against all reconstruction settings. To provide any utility, stable features are required to differentiate between regions with different hetro/homogeneity. Of the forty three stable features, fifteen (35%) features showed a statistically significant difference between the artificially constructed inserts. Such features included GLCM (Difference average, Difference entropy, Dissimilarity and Inverse difference), GLRL (Long run emphasis, Grey level non uniformity and Run percentage) and NGTDM (Complexity and Strength). The finding of this work suggests that radiomic features are capable of distinguishing between radioactive distribution patterns that demonstrate different levels of heterogeneity. Therefore, radiomic features could serve as an adjuvant diagnostic tool along with traditional imaging. However, the choice of the radiomic features needs to account for variability introduced when different reconstruction settings are used. Standardization of PET image reconstruction settings across sites performing radiomic analysis in multi-centre trials should be considered