Radiotherapy Computed Tomography Research Articles

Advanced Computational Methods for Radiation Dose Optimization in CT.

In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Optimized protocol DRLs were proposed for comparison purposes.

VOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the samepatients. In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD ). The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3mm in terms of DC and HD , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5mm, which was considered as valid for the analysis of intermodalityvariability. The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guidedRT.

Survey on fan-beam computed tomography for radiotherapy: Current implementation and future perspectives of motion management and surface guidance devices

Background and purposeThis work reports on the results of a survey performed on the use of computed tomography (CT) imaging for motion management, surface guidance devices, and their quality assurance (QA). Additionally, it details the collected user insights regarding professional needs in CT for radiotherapy. The purpose of the survey is to understand current practice, professional needs and future directions in the field of fan-beam CT in radiation therapy (RT). Materials and methodsAn online institutional survey was conducted between 1-Sep-2022 and 10-Oct-2022 among medical physics experts at Belgian and Dutch radiotherapy institutions, to assess the current status, challenges, and future directions of motion management and surface image-guided radiotherapy. The survey consisted of a maximum of 143 questions, with the exact number depending on participants’ responses. ResultsThe response rate was 66 % (31/47). Respiratory management was reported as standard practice in all but one institution; surface imaging during CT-simulation was reported in ten institutions. QA procedures are applied with varying frequencies and methodologies, primarily with commercial anatomy-like phantoms. Surface guidance users report employing commercial static and dynamic phantoms. Four main subjects are considered clinically important by the respondents: surface guidance, CT protocol optimisation, implementing gated imaging (4DCT, breath-hold), and a tattoo-less workflow. ConclusionsThe survey highlights the scattered pattern of QA procedures for respiratory motion management, indicating the need for well-defined, unambiguous, and practicable guidelines. Surface guidance is considered one of the most important techniques that should be implemented in the clinical radiotherapy simulation workflow.

A deep learning segmentation method to assess dose to organs at risk during breast radiotherapy

Background and purposeContouring of organs at risk is important for studying health effects following breast radiotherapy. However, manual contouring is time-consuming and subject to variability. The purpose of this study was to develop a deep learning-based method to automatically segment multiple structures on breast radiotherapy planning computed tomography (CT) images. Materials and methodsWe used data from 118 patients, including 90 diagnostic CT scans with expert structure delineations for training and 28 breast radiotherapy planning CT images for testing. The radiotherapy CT images also had expert delineations for evaluating performance. We targeted a total of eleven organs at risk including five heart substructures. Segmentation performance was evaluated using the metrics of Dice similarity coefficient (DSC), overlap fraction, volume similarity, Hausdorff distance, mean surface distance, and dose. ResultsThe average DSC achieved on the radiotherapy planning images was 0.94 ± 0.02 for the whole heart, 0.96 ± 0.02 and 0.97 ± 0.01 for the left and right lung, 0.61 ± 0.10 for the esophagus, 0.81 ± 0.04 and 0.86 ± 0.04 for left and right atrium, 0.91 ± 0.02 and 0.84 ± 0.04 for left and right ventricle, and 0.21 ± 0.11 for the left anterior descending artery (LAD), respectively. Except for the LAD, the median difference in mean dose to these structures was small with absolute (relative) differences < 0.1 Gy (6 %). ConclusionsExcept for the LAD, our method demonstrated excellent performance and can be generalized to segment additional structures of interest.

Accurate segmentation of head and neck radiotherapy CT scans with 3D CNNs: consistency is key

Objective. Automatic segmentation of organs-at-risk in radiotherapy planning computed tomography (CT) scans using convolutional neural networks (CNNs) is an active research area. Very large datasets are usually required to train such CNN models. In radiotherapy, large, high-quality datasets are scarce and combining data from several sources can reduce the consistency of training segmentations. It is therefore important to understand the impact of training data quality on the performance of auto-segmentation models for radiotherapy. Approach. In this study, we took an existing 3D CNN architecture for head and neck CT auto-segmentation and compare the performance of models trained with a small, well-curated dataset (n = 34) and then a far larger dataset (n = 185) containing less consistent training segmentations. We performed 5-fold cross-validations in each dataset and tested segmentation performance using the 95th percentile Hausdorff distance and mean distance-to-agreement metrics. Finally, we validated the generalisability of our models with an external cohort of patient data (n = 12) with five expert annotators. Main results. The models trained with a large dataset were greatly outperformed by models (of identical architecture) trained with a smaller, but higher consistency set of training samples. Our models trained with a small dataset produce segmentations of similar accuracy as expert human observers and generalised well to new data, performing within inter-observer variation. Significance. We empirically demonstrate the importance of highly consistent training samples when training a 3D auto-segmentation model for use in radiotherapy. Crucially, it is the consistency of the training segmentations which had a greater impact on model performance rather than the size of the dataset used.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset.

For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods. The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for image-guided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set1 (42 cases or 75%) and test Set2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep nnU-Net architecture on Set1, and evaluating its performance on Set2. The data are publicly available through an open-access repository under the name HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset. Images and reference segmentations are stored in the NRRD file format, where the OAR filenames correspond to the nomenclature recommended by the American Association of Physicists in Medicine, and OAR and demographics information is stored in separate comma-separated value files. The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN. Other potential applications include out-of-challenge algorithm development and benchmarking, as well as external validation of the developedalgorithms.

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

Prediction of Patients at Risk of Pelvic Insufficiency Fractures Following Pelvic Radiotherapy

<h3>Purpose/Objective(s)</h3> Radiotherapy-induced pelvic insufficiency fractures (PIFs), historically under-reported, can lead to significant morbidity. Using deep-learning techniques, this study develops a fully automated predictive tool to identify patients who are at high-risk of developing PIFs following radiotherapy for gynecological cancer. <h3>Materials/Methods</h3> Retrospective clinical, dosimetry and imaging data from 330 patients receiving pelvic radiotherapy for any primary gynecological cancer between January 2012 and January 2021 was collected (study approved by local ethical committee). Patient electronic records were reviewed; PIFs were diagnosed radiologically using computed tomography (CT) and magnetic resonance imaging (MRI). An auto-segmentation model for the sacrum and whole pelvis on CT-imaging was developed using a UNETR architecture to aid evaluation of pelvic radiotherapy dose and CT density. The model was validated by comparing the derived dose-volume histograms (DVHs) with those derived from manual contours. Chi-squared and Mann-Whitney tests were performed to determine associations between derived potential risk factors and PIF. Using Elastic Net Logistic Regression modelling a predictive tool to identify patients at risk of developing PIF was developed and internally validated. <h3>Results</h3> Among the 330 patients, the median follow-up was 36.6 months, and median age was 65 years. The PIF incidence was 10.9% and median interval from radiotherapy to PIF was 16 months. 86% of fractures occurred in the sacrum, 56% of patients had multiple fractures and 64% of patients were symptomatic. The auto-contouring tool of the sacrum and whole pelvis was clinically validated and, against gold-standard contours, achieved a mean DICE coefficient of 0.94, Average Surface Distance (ASD) of 0.65, and maximum absolute error for DVH curves of 3.1%. Univariant analysis found that site of primary disease, body mass index, prior diagnosis of osteopenia or osteoporosis, use of concurrent chemotherapy, V_55Gy (volume of the sacrum and whole pelvis receiving 55 Gy), and lower CT density were statistically associated with the development of PIF (p<0.05). The fully automated predictive tool to identify patients at risk of PIF using solely imaging and dosimetry data had similar accuracy to models including additional clinical features; both had a receiver-operating-characteristic (ROC) area-under-the-curve (AUC) of 0.761. The prediction tool has a sensitivity of 66%, specificity of 72% and a negative predictive value of 95%. <h3>Conclusion</h3> PIF is not uncommon in this setting, often symptomatic and strongly associated with low bone density on CT. Prediction and prevention of PIF would have an immense impact on patients and healthcare systems. We propose an automated predictive tool to identify patients at high risk, for whom effective prophylactic treatments are needed.

The identification of abnormalities on radiotherapy CT planning scans: a retrospective audit

Abstract Introduction: Therapeutic radiographers are an essential part of a cancer patient’s journey and play a vital and changing role in the delivery of radiotherapy services. This retrospective audit highlights the number of incidental abnormalities found by a Breast Advanced Practitioner on radiotherapy computed tomography (CT) planning scans and their subsequent management. Methods: This retrospective audit investigated the incidental abnormalities found by the Breast Advanced Practitioner on routine CT planning scans for breast cancer patients 2016–2021. Any breast cancer patient found to have an abnormality had their planning scan uploaded to the national picture archiving and communication systems (PACS) system for radiology review. Further formal CT imaging was requested or direct referral to an appropriate multi-disciplinary team meeting. Results: Sixty-three significant abnormalities were found over the five-year period, of these thirty seven were malignant and the majority of these were lung lesions. Seven patients went on to have surgery alone, surgery plus chemoradiation or stereotactic ablative radiotherapy for their newly diagnosed lung primaries. Five patients were found to have liver metastases that unfortunately changed their treatment plan to palliative. Conclusion: This retrospective audit has demonstrated that CT planning for radiotherapy offers an opportunity to identify malignant abnormalities at a potentially early stage, thereby improving prognosis and survival. Radiographers have a duty of care to appraise these CT scans to ensure any abnormalities can be addressed in a timely manner.

Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs.

Accurate and robust auto-segmentation of highly deformable organs (HDOs), for example, stomach or bowel, remains an outstanding problem due to these organs' frequent and large anatomical variations. Yet, time-consuming manual segmentation of these organs presents a particular challenge to time-limited modern radiotherapy techniques such as on-line adaptive radiotherapy and high-dose-rate brachytherapy. We propose a machine-assisted interpolation (MAI) that uses prior information in the form of sparse manual delineations to facilitate rapid, accurate segmentation of the stomach from low field magnetic resonance images (MRI) and the bowel from computed tomography (CT) images. Stomach MR images from 116 patients undergoing 0.35T MRI-guided abdominal radiotherapy and bowel CT images from 120 patients undergoing high dose rate pelvic brachytherapy treatment were collected. For each patient volume, the manual delineation of the HDO was extracted from every 8th slice. These manually drawn contours were first interpolated to obtain an initial estimate of the HDO contour. A two-channel 64×64pixel patch-based convolutional neural network (CNN) was trained to localize the position of the organ's boundary on each slice within a five-pixel wide road using the image and interpolated contour estimate. This boundary prediction was then input, in conjunction with the image, to an organ closing CNN which output the final organ segmentation. A Dense-UNet architecture was used for both networks. The MAI algorithm was separately trained for the stomach segmentation and the bowel segmentation. Algorithm performance was compared against linear interpolation (LI) alone and against fully automated segmentation (FAS) using a Dense-UNet trained on the same datasets. The Dice Similarity Coefficient (DSC) and mean surface distance (MSD) metrics were used to compare the predictions from the three methods. Statistically significance was tested using Student's t test. For the stomach segmentation, the mean DSC from MAI (0.91 ± 0.02) was 5.0% and 10.0% higher as compared to LI and FAS, respectively. The average MSD from MAI (0.77 ± 0.25mm) was 0.54 and 3.19mm lower compared to the two other methods. Only 7% of MAI stomach predictions resulted in a DSC<0.8, as compared to 30% and 28% for LI and FAS, respectively. For the bowel segmentation, the mean DSC of MAI (0.90 ± 0.04) was 6% and 18% higher, and the average MSD of MAI (0.93 ± 0.48mm) was 0.42 and 4.9mm lower as compared to LI and FAS. Sixteen percent of the predicted contour from MAI resulted in a DSC<0.8, as compared to 46% and 60% for FAS and LI, respectively. All comparisons between MAI and the baseline methods were found to be statistically significant (p-value<0.001). The proposed MAI algorithm significantly outperformed LI in terms of accuracy and robustness for both stomach segmentation from low-field MRIs and bowel segmentation from CT images. At this time, FAS methods for HDOs still require significant manual editing. Therefore, we believe that the MAI algorithm has the potential to expedite the process of HDO delineation within the radiation therapy workflow.

Assessing the patient experience of anal and rectal cancer MR simulation for radiotherapy treatment planning

AbstractAim:The patient experience of radiotherapy magnetic resonance (MR) simulation is unknown. This study aims to evaluate the patient experience of MR simulation in comparison to computed tomography (CT) simulation, identifying the quality of patient experience and pathway changes which could improve patient experience outcomes.Materials and Methods:MR simulation was acquired for 46 anal and rectal cancer patients. Patient experience questionnaires were provided directly after MR simulation. Questionnaire responses were assessed after 33 patients (cohort one). Changes to the scanning pathway were identified and implemented. The impact of changes was assessed by cohort two (13 patients).Results:Response rates were 85% (cohort one) and 54% (cohort two). 75% of cohort one respondents found the magnetic resonance imaging (MRI) experience to be better or similar to their CT experience. Implemented changes included routine use of blankets, earplugs and headphones, music and feet-first positioning and further MRI protocol optimisation. All cohort two respondents found the MRI experience to be better or similar to the CT experience.Findings:MR simulation can be a comfortable and positive experience that is comparable to that of standard radiotherapy CT simulation. Special attention is required due to the fundamental differences between CT and MRI scanning.

Progressive muscle loss is an independent predictor for survival in locally advanced oral cavity cancer: A longitudinal study

Background and purposeTo investigate the association between progressive muscle loss and survival outcomes of patients with advanced-stage oral squamous cell carcinoma (OSCC) undergoing surgery and adjuvant (chemo)radiotherapy. MethodsWe analyzed the computed tomography (CT) scans of 155 patients with stage III–IVB OSCC at baseline, at simulation CT for radiotherapy, and at 3- and 9-months post-treatment. Skeletal muscle index (SMI) was measured using CT at the C3 vertebral level. The predictors of overall survival (OS) and recurrence-free survival (RFS) were evaluated using Cox regression models. ResultsThe median follow-up period was 75.0 months. Fifty-one patients (32.9%) developed recurrence, with the median time from the fourth CT to recurrence being 9.1 months. The SMI progressively decreased from baseline to simulation CT by 1.1% (p = 0.006), to 3 months post-treatment by 5.1% (p < 0.001), and to 9 months post-treatment by 15.6% (p < 0.001) in patients developing recurrence. Patients without recurrence lost SMI at the simulation CT by 0.7% (p = 0.001) and at 3 months post-treatment by 2.1% (p < 0.001); their SMI returned to the baseline level at 9 months post-treatment. SMI changes were weakly correlated with changes in body mass index (BMI) (Spearman ρ, 0.13; p = 0.11). In multivariate analysis, SMI changes (per 5% decrease) were independently associated with significantly worse OS (hazard ratio: 1.88, 95% confidence interval: 1.58–2.23; p < 0.001) and RFS (hazard ratio: 1.89, 95% confidence interval: 1.61–2.20; p < 0.001). ConclusionProgressive muscle loss was independently associated with worse survival outcomes in patients with stage III–IVB OSCC. Muscle loss might not be detected by changes in BMI.

Indigenous Groin Board Immobilization Reduces Planning Target Volume Margins in Groin Radiotherapy.

Purpose:To quantify the relative motion of pelvic and groin lymph nodes (PLN and GLN) and define indicative margins for image-guided radiotherapy based on bony anatomy for the frog-leg position (FLP) and groin immobilization board (GIB).Materials and Methods:Twenty patients with planning computed tomography (CT) scan and on treatment cone beam CTs (median = 8) for groin and pelvic radiotherapy were included in the study. Of these nine were treated with FLP and eleven with GIB. The PLN and GLN regions on the left and right were outlined in each scan. Systematic and random uncertainties were determined along with correlations between the motions of these regions. The clinical target volume to planning target volume (PTV) margins required to take motion into account was calculated for each immobilization.Results:The mean shifts for PLN and GLN were lesser but not statistically lower using GIB over FLP. There was significant concordance in the vertical, longitudinal and lateral motion of the pelvis and right groin (P = 0.015, 0.09 and 0.049, respectively), pelvis and left groin (P = 0.001, 0.048, and 0.006, respectively) and between left and right groin (P = 0.013, 0.01 and 0.07, respectively) for FLP and not GIB. The PTV margins required by Van Herk and Stroom's formula were reduced from 11 mm and 9 mm to 6 mm and 5 mm for pelvis; 12 mm and 11 mm to 7 mm and 6 mm for groin, respectively, using FLP over GIB.Conclusions:GIB brings concordance in shifts between pelvis and groin and between bilateral groins, thereby reducing the required PTV margins.

DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy.

Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross tumor, while CTV outlines the sub-clinical malignant disease. Automatic GTV and CTV segmentation are both challenging for distinct reasons: GTV segmentation relies on the radiotherapy computed tomography (RTCT) image appearance, which suffers from poor contrast with the surrounding tissues, while CTV delineation relies on a mixture of predefined and judgement-based margins. High intra- and inter-user variability makes this a particularly difficult task. We develop tailored methods solving each task in the esophageal cancer radiotherapy, together leading to a comprehensive solution for the target contouring task. Specifically, we integrate the RTCT and positron emission tomography (PET) modalities together into a two-stream chained deep fusion framework taking advantage of both modalities to facilitate more accurate GTV segmentation. For CTV segmentation, since it is highly context-dependent-it must encompass the GTV and involved lymph nodes while also avoiding excessive exposure to the organs at risk-we formulate it as a deep contextual appearance-based problem using encoded spatial distances of these anatomical structures. This better emulates the margin- and appearance-based CTV delineation performed by oncologists. Adding to our contributions, for the GTV segmentation we propose a simple yet effective progressive semantically-nested network (PSNN) backbone that outperforms more complicated models. Our work is the first to provide a comprehensive solution for the esophageal GTV and CTV segmentation in radiotherapy planning. Extensive 4-fold cross-validation on 148 esophageal cancer patients, the largest analysis to date, was carried out for both tasks. The results demonstrate that our GTV and CTV segmentation approaches significantly improve the performance over previous state-of-the-art works, e.g., by 8.7% increases in Dice score (DSC) and 32.9mm reduction in Hausdorff distance (HD) for GTV segmentation, and by 3.4% increases in DSC and 29.4mm reduction in HD for CTV segmentation.

Establishment of national diagnostic reference levels for radiotherapy computed tomography simulation procedures in Slovenia

PurposeTo propose national diagnostic reference levels (DRLs) for radiotherapy (RT) computed tomography (CT) localization purposes, compare both CT units used in the largest RT department in the country and to compare gathered results with other published DRLs in order to discover any need of optimization. MethodsIn total, 1631 patient data (time spend of 4 months) regarding sex, examination type, total dose-length product (DLP) and CTDIvol was collated on two CT units. Those simulation procedures account for more than 80 % of all simulation procedures performed nationwide. Then, total DLP and CTDIvol was calculated and mean, median and 3rd quartile for both units together were presented to determine national DRLs for simulation procedures. The same data was later compared between both units to discover any potential need for optimization. Results3rd quartile values of DLP for abdomen, breast, chest, head, head and neck, pelvis and spine were 1116.2, 606.6, 832.4, 1942.4, 969.2, 677.1 and 1042.4 mGy∙cm, respectively. 3rd quartile CTDIvol values for the same sequence of procedures were 18.7, 13.3, 19.2, 76.9, 22.6, 17.9 and 22.2 mGy, respectively. Among the two units, the mentioned dose values were on average significantly higher on one CT unit than on the other unit. ConclusionsWhen comparing collected dose values with other studies, RT CT DRLs showed that radiation doses from our institution were similar or even lower. Some variations were found between both CT units in certain protocols, so exposure parameters should be reviewed and optimized.

A Monte Carlo investigation of dose length product of cone beam computed tomography scans

The dose length product (DLP) provides a measurement related to energy imparted from a computed tomography (CT) scan. The DLP is based on the volume-averaged CT dose index (CTDIvol), which is designed for fan beams. The aims of this study were to investigate the use of DLP for scans with wide beams used in cone beam CT (DLPCBCT) in radiotherapy that would be analogous to the DLP of fan beam scans (DLPCT), and to compare the efficiencies of DLPCT and DLPCBCT in reporting the total energy imparted in patients. A validated Monte Carlo model of a kV imaging system integrated into a Varian TrueBeam linac was employed. The DLPCT was assessed by multiplying the CTDIvol for a 20 mm fan beam by scan length, and the DLPCBCT determined through multiplying the CTDIvol, estimated for wide beams using a correction factor based on free-in-air measurements, by the beam width. Two scan protocols for head and body were investigated for tube potentials between 80 and 140 kV and a range of scan lengths/widths. Efficiency values were estimated by normalising the DLPCT and DLPCBCT with respect to the corresponding dose profile integrals (DPIs), which were evaluated within 900 mm long phantoms. The results show that the DLPCBCT values were within 1% of those for DLPCT of similar length performed on the same system, and the efficiencies decrease with tube potential. However, whereas DLP values for fan beams are approximately proportional to scan length, those for wide beams decrease by ∼2% between beam widths of 20 and 320 mm. As a result, while the DLPCT efficiency is similar over all scan lengths, that for DLPCBCT increases slightly with beam width. The DLPCT and DLPCBCT underestimated the total energy imparted by comparable amounts with efficiencies within the range of 80–81% and 80–83% for the head scans, and 71–76% and 70–77% for the body scans, respectively. The results indicate that the DLPCBCT can be considered as an analogous dose index to the DLPCT. It could, therefore, be used for quantification of doses from imaging in radiotherapy and provide a valuable tool to aid optimisation.

Can the rectal size of prostate patients at MRI be used as a predictor of rectal size at CT for patients referred for radiotherapy planning?

Keywords: Prostate cancer, computed tomography, magnetic resonance Introduction: To audit the dynamic interrelationship between rectal distension at diagnosis magnetic resonance (MRI) scan with the computed tomography (CT) radiotherapy planning scan; to determine the effect of rectal movement on the position of the prostatic gland. This review will examine the efficacy of rectal preparation. At present rectal distension at RT CT planning scan is assessed on control slices using GSTT protocol prior to having a full CT scan. If this is outside tolerance a repeat CT scan appointment whereby the patient is given an enema. If rectal size can be predicted prior to the CT planning scan, bowl preparation can be pre-empted to ensure efficiencies in the patient pathway. Method: A retrospective audit looking at the MRI diagnostic scans and radiotherapy CT planning scans of 141 patients with adenocarcinoma of the prostate between May to August 2019. To find out if rectal size at MRI serves as an indicator of rectal size at planning RT CT. Dependent on the outcome of the retrospective study, a prospective study will then be undertaken to assess if an intervention prior to RT CT scan such as a prescription of Docusate Sodium enema help resolve the issue of rectal size at planning. Results: Results pending. Conclusions & Discussion: The outcome should inform clinical practice, benefit patients and clinicians and provision of service. The audit may result in reduction of rebooking patients for repeat CT scan and cease the delay of treatment processes. Numerical

In silico comparison of the dosimetric impacts of a greater omentum spacer for abdominal and pelvic tumors in carbon-ion, proton and photon radiotherapy

PurposeThe purpose of this study was to compare carbon-ion (C-ion), proton and photon radiotherapy (RT) plans with regard to dose reduction of the gastrointestinal (GI) tract by using a greater omentum spacer (GO spacer).MethodsWe retrospectively retrieved data for ten patients who received the GO spacer as surgical spacer placement for abdominal and pelvic tumors. Simulation plans were created on pre-spacer Computed Tomography (CT) and post-spacer CT for C-ion RT, proton RT and photon RT to compare the dose of the GI tract. The plans were normalized so that at least 95% of the planning target volume (PTV) received 70 Gy (relative biological effectiveness equivalent) delivered in 35 fractions. All plans were created with the lowest possible dose to the GI tract under conditions that meet the dose constraints for the PTV and spinal cord (maximum dose < 45 Gy). The part of the GI tract to be evaluated was defined as that most adjacent to the PTV. C-ion RT plans and proton RT plans were calculated by a spot scanning technique, and photon RT plans were calculated employing by fixed-field intensity-modulated radiation therapy.ResultsD2 cc and V10–70 of the GI tract were significantly lower on post-spacer plans than on pre-spacer plans for all three RT modalities. Regarding post-spacer plans, D2 cc of the GI tract was significantly lower on C-ion RT plans and proton RT plans than on photon RT plans (C-ion vs photon p = 0.001, proton vs photon p = 0.002). However, there was no significant difference between C-ion RT plans and proton RT plans for D2 cc of the GI tract (C-ion vs proton p = 0.992). In the photon RT plan for one patient, D2 cc of the GI tract did not meet < 50 Gy.ConclusionsThe GO spacer shows a significant dose reduction effect on the GI tract.

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks

Background and ObjectiveThe spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images. MethodsThe techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification. ResultsThe methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord. ConclusionsIt is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions.

P261] Image quality assessment of filtered dual energy cone beam CT for radiotherapy

Purpose Dual energy (DE) computed tomography (CT), combines two scans obtained with different photon energy spectra. It is known from diagnostic CT to reduce metal artefacts. This study investigates the feasibility of developing a DE protocol for cone-beam CT (CBCT) in radiotherapy. Methods The DE efficacy depends on the ability to separate the energy spectra of the two scans. Therefore, different thickness of copper (Cu) filtration was added to the high energy scan. The DE CBCT scanning protocol consists of two scans at 80 (low) and 140 (high) kV, respectively. Two different DE protocols with 1 and 2 mm added Cu filtration were created. The filtration was done by physically adding Cu plates onto the kV source of the CBCT system. The mAs was adjusted to keep the doses of both DE protocols on the same level as the standard CBCT. The dose was measured using a CTDI phantom. The DE images, I DE , were created in image space from the high, I high , and low, I low , energy images using I DE ( E ) = w ( E ) ∗ I low + ( 1 - w ( E ) ) ∗ I high where w(E) is a weighting factor dependent on the (pseudo) monochromatic energy, E, of the I DE images. Scans of a Catphan phantom were acquired with the DE CBCT protocols as well as a standard CBCT scan at 100 kV. The Cu filtered DE CBCT scans were investigated with respect to soft tissue image quality as the potential benefit of image artefact reduction should not compromise overall image quality or increase radiation dose compared to the standard CBCT. To quantify the image quality, contrast-to-noise (CNR) measurements were done in regions resembling muscle and fat in the phantom. Results The radiation dose was 100.1% and 101.4% of the standard CBCT dose for the 1 and 2 mm Cu protocols, respectively. The CNR was found to be 1.3 (standard), 1.4 (DE, 1 mm Cu) and 1.3 (DE, 2 mm Cu). Conclusions The filtered DE and standard CBCT protocols showed similar performance in radiation dose and soft tissue image quality. Hence it is feasible to explore the potential benefits of increased soft tissue contrast and artefact reduction for DE CBCT protocols.