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BackgroundThis study utilizes interviews of clinical medical physicists to investigate self-reported shortcomings of the current weekly chart check workflow and opportunities for improvement.MethodsNineteen medical physicists were recruited for a 30-minute semi-structured interview, with a particular focus placed on image review and the use of automated tools for image review in weekly checks. Survey-type questions were used to gather quantitative information about chart check practices and importance placed on reducing chart check workloads versus increasing chart check effectiveness. Open-ended questions were used to probe respondents about their current weekly chart check workflow, opinions of the value of weekly chart checks and perceived shortcomings, and barriers and facilitators to the implementation of automated chart check tools. Thematic analysis was used to develop common themes across the interviews.ResultsPhysicists ranked highly the value of reducing the time spent on weekly chart checks (average 6.3 on a scale from 1 to 10), but placed more value on increasing the effectiveness of checks with an average of 9.2 on a 1-10 scale. Four major themes were identified: (1) weekly chart checks need to adapt to an electronic record-and-verify chart environment, (2) physicists could add value to patient care by analyzing images without duplicating the work done by physicians, (3) greater support for trending analysis is needed in weekly checks, and (4) automation has the potential to increase the value of physics checks.ConclusionThis study identified several key shortcomings of the current weekly chart check process from the perspective of the clinical medical physicist. Our results show strong support for automating components of the weekly check workflow in order to allow for more effective checks that emphasize follow-up, trending, failure modes and effects analysis, and allow time to be spent on other higher value tasks that improve patient safety.

Objective. In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, a novel detection method for such misalignments in IGRT was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing.Approach. A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011-2021.Main results. The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error.Significance. Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph IGRT is unique in being determined independently of an error reporting system.

PurposePresent knowledge of patient setup and alignment errors in image guided radiation therapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible owing to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate.Methods and materialsThe EDA was developed to analyze the registration between planning scans and pretreatment cone beam computed tomography scans, outputting a misalignment score ranging from 0 (most unlikely) to 1 (most likely). The algorithm was trained using simulated translational errors on a data set obtained from 680 patients treated at 2 radiation therapy clinics between 2017 and 2022. A receiver operating characteristic analysis was performed to obtain target thresholds. DICOM Query and Retrieval software was integrated with the EDA to interact with the clinical database and fully automate data retrieval and analysis during a retrospective error search from 2016 to 2017 and from 2021 to 2022 for the 2 institutions, respectively. Registrations were flagged for human review using both a hard-thresholding method and a prediction trending analysis over each individual patient's treatment course. Flagged registrations were manually reviewed and categorized as errors (>1 cm misalignment at the target) or nonerrors.ResultsA total of 17,612 registrations were analyzed by the EDA, resulting in 7.7% flagged events. Three previously reported errors were successfully flagged by the EDA, and 4 previously unreported vertebral body misalignment errors were discovered during case reviews. False positive cases often displayed substantial image artifacts, patient rotation, and soft tissue anatomy changes.ConclusionsOur results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of IGRT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction.

PURPOSE: Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup. METHODS: An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous days registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance. RESULTS: Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions. CONCLUSION: In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.

Summary: Background: Optimal blood glucose management is essential for optimal managing type 1 diabetes (T1D). Exercise is an efficacious, non-pharmacological, and cost-efficient adjunctive treatment that provides additional health benefits. Nevertheless, many adolescents with T1D are not meeting recommended physical activity levels and struggle with implementing appropriate management strategies around exercise. Aims: To investigate in adolescents with T1D: 1) physical activity levels; 2) management strategies utilised around physical activity; and 3) barriers and facilitators to physical activity engagement. Methods: Physical activity levels were assessed using the PAQ-C and PAQ-A questionnaires. A cut-off score of 2.75 was adopted to classify participants as meeting the recommended ≥60 minutes of moderate-to-vigorous daily physical activity. Semi-structured interviews with 16 adolescents were conducted, followed by NVivo-assisted thematic analysis. Results: The mean PAQ-score was 2.7(SD±0.77), with 25% adhering to the recommended physical activity guidelines. Themes related to management strategies were explored, including blood glucose monitoring, insulin-related, exercise-related, and nutrition-related strategies. Identified barriers included limited T1D management knowledge, negative social impact, difficulty with technology, poor planning and organisation, individual physiological responses, and the burden of T1D management. Facilitators included the ability to optimally manage the condition, having supportive people who understand and service supports. Conclusions: This study identified barriers and facilitators to managing blood glucose levels around and during physical activity in adolescents with T1D. This provides clinical insights into the management strategies utilised around physical activity and factors affecting participation that can inform the future promotion of physical activity in this population. In this cohort, two-thirds did not meet the recommended physical activity levels. Physical activity participation was primarily influenced by adolescents' confidence and ability to manage their condition around exercise. Exercise promotion strategies should focus on supporting optimal exercise management patterns for adolescents with T1D.

Background and purposeCurrently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity.Materials and methodsAlgorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting.ResultsARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions.ConclusionThe high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.

This is a general guide to field identification, species description, and biology of the five bumble bee species Bombus spp. found in Florida. This guide to bumble bee identification in Florida would also be usable for coastal South Carolina, Georgia, Alabama, Louisiana, Mississippi, and Arkansas, but it is not comprehensive for the northern/mountainous parts of those states, where Bombus auricomus, B. citrinus, B. fervidus, B. flavidus, B. perplexus, B sandersoni, B. terricola, and B. vagans are possible.

BackgroundIn cone-beam computed tomography (CBCT)-guided radiotherapy, off-by-one vertebral-body misalignments are rare but serious errors that lead to wrong-site treatments.PurposeAn automatic error detection algorithm was developed that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images.MethodsAlgorithm training and test data consisted of planning CTs and CBCTs from 480 patients undergoing radiotherapy treatment in the thoracic and abdominal regions at two radiotherapy clinics. The clinically applied registration was used to derive true-negative (no error) data. The setup and planning images were then misaligned by one vertebral-body in both the superior and inferior directions, simulating the most likely misalignment scenarios. For each of the aligned and misaligned 3D image pairs, 2D slice pairs were automatically extracted in each anatomical plane about a point within the vertebral column. The three slice pairs obtained were then inputted to the EDM that returned a probability of vertebral misalignment. One model (EDM1 ) was trained solely on data from institution 1. EDM1 was further trained using a lower learning rate on a dataset from institution 2 to produce a fine-tuned model, EDM2 . Another model, EDM3 , was trained from scratch using a training dataset composed of data from both institutions. These three models were validated on a randomly selected and unseen dataset composed of images from both institutions, for a total of 303 image pairs. The model performances were quantified using a receiver operating characteristic analysis. Due to the rarity of vertebral-body misalignments in the clinic, a minimum threshold value yielding a specificity of at least 99% was selected. Using this threshold, the sensitivity was calculated for each model, on each institution's test set separately.ResultsWhen applied to the combined test set, EDM1 , EDM2 , and EDM3 resulted in an area under curve of 99.5%, 99.4%, and 99.5%, respectively. EDM1 achieved a sensitivity of 96% and 88% on Institution 1 and Institution 2 test set, respectively. EDM2 obtained a sensitivity of 95% on each institution's test set. EDM3 achieved a sensitivity of 95% and 88% on Institution 1 and Institution 2 test set, respectively.ConclusionThe proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments in CBCT-guided radiotherapy that was sufficiently high to allow for practical implementation. It was found that fine-tuning the model on a multi-facility dataset can further enhance the generalizability of the algorithm.

PurposeCraniospinal irradiation (CSI) using tomotherapy has advantages over standard 3-dimensional techniques. However, there is a paucity of published data on craniospinal setup reproducibility to guide appropriate planning treatment volume (PTV) margins. We sought to evaluate the setup accuracy of patients undergoing CSI to optimize PTV margins.Methods and materialsWe measured residual setup deviation between simulation computed tomography (CT) and daily megavoltage CT after couch shifts made by therapists after megavoltage CT-based image registration for 10 patients who completed CSI at our institution. Translational displacement values were recorded at the sella, top of T1, and top of L5 in the anteroposterior (AP) and lateral planes. Systematic and random error were calculated from displacement values. Using z score analysis, we calculated minimal PTV margins to encompass 90% of recorded fractions at each level. We evaluated whether patient characteristics predict for increased setup error using standard statistical techniques.ResultsThe mean setup deviation in the AP plane across all treatments was 2.49, 3.40, and 3.83 mm at the sella, T1, and L5, respectively. Mean lateral setup error was 2.86, 4.02, and 5.46 mm at the sella, T1, and L5, respectively. Systematic error ranged from 0.75 to 1.01 mm at the sella, 1.09 to 1.37 mm at T1, and 1.30 to 1.50 mm at L5. Random error ranged from 1.35 to 1.41 mm at the sella, 1.48 to 1.73 mm at T1, and 2.26 to 2.37 mm at L5. The minimum margin to cover 90% of the treatments was 6.4, 8.2, and 10.5 mm at the sella, T1, and L5, respectively. There appeared to be a correlation between older age and lateral setup error in the L spine approaching statistical significance (R, 0.629; P = .052).ConclusionsSetup error increases in the caudal direction of the spine and is greater in the lateral plane compared with the AP plane. We recommend a PTV margin of 5 to 7 mm in the brain and 10 mm in the spine.

Introduction: Open access to digital research output is increasing, but academic library policies can place restrictions on public access to libraries. This paper reports on a preliminary study to investigate the correlation between academic library access policies and institutional positions of openness to knowledge. Method: This primarily qualitative study used document and data analysis to examine the content of library access or use policies of twelve academic institutions in eight countries. The outcomes were statistically correlated with institutional open access publication policies and practices. Analysis: We used an automated search tool together with manual searching to retrieve Web-based library access policies, then categorised and counted the levels and conditions of public access. We compared scores for institutional library access feature with open access features and percentages of open access publications. Results: Academic library policies may suggest open public access but multi-layered user categories, privileges and fees charged can inhibit such access, with disparities in openness emerging between library policies and institutional open access policies. Conclusion: As open access publishing options and mandates expand, physical entry and access to print and electronic resources in academic libraries is contracting. This conflicts with global library and information commitments to open access to knowledge.

PurposeTo estimate the overall spatial distortion on clinical patient images for a 0.35 T MR-guided radiotherapy system.MethodsTen patients with head-and-neck cancer underwent CT and MR simulations with identical immobilization. The MR images underwent the standard systematic distortion correction post-processing. The images were rigidly registered and landmark-based analysis was performed by an anatomical expert. Distortion was quantified using Euclidean distance between each landmark pair and tagged by tissue interface: bone-tissue, soft tissue, or air-tissue. For baseline comparisons, an anthropomorphic phantom was imaged and analyzed.ResultsThe average spatial discrepancy between CT and MR landmarks was 1.15 ± 1.14 mm for the phantom and 1.46 ± 1.78 mm for patients. The error histogram peaked at 0-1 mm. 66% of the discrepancies were

Summary: Background: The DynamX™ bioadaptor is the first coronary implant technology with a unique mechanism of unlocking the bioadaptor frame after polymer resorption over 6 months, uncaging the vessel while maintaining a dynamic support to the vessel. It aims to achieve the acute performance of drug-eluting stents (DES) with the advantages of restoration of vessel function. Methods: This international, single blinded, randomised controlled (1:1) trial compared a sirolimus-eluting bioadaptor with a contemporary zotarolimus-eluting stent (DES) in 34 hospitals in Europe, Japan and New Zealand. Patients with de novo coronary lesions and absence of acute myocardial infarction were enrolled from January 2021 to Feburary 2022. The implantation of the bioadaptor followed the standards of DES. An imaging subset of 100 patients had angiographic and intravascular ultrasound assessments, and 20 patients additionally optical coherence tomography. Data collection will continue through 5 years, we herein report 12-month data based on an intention-to-treat population. This trial is registered at ClinicalTrials.gov (NCT04192747). Findings: 445 patients were randomised between January 2021 and February 2022. Device, lesion and procedural success rates, and acute gain were similar amongst the groups. The primary endpoint, 12-month target lesion failure, was 1.8% [95% CI: 0.5; 4.6] (n = 4) versus 2.8% [95% CI: 1.0; 6.0] (n = 6), pnon-inferiority < 0.001 for the bioadaptor and the DES, respectively (Δ-1.0% [95% CI: −3.3; 1.4]). One definite or probable device thrombosis occurred in each group. The 12-month imaging endpoints showed superior effectiveness of the bioadaptor such as in-device late lumen loss (0.09 mm [SD 0.34] versus 0.25 mm [SD 0.39], p = 0.04), and restored compliance and cyclic pulsatility (%mid in-device lumen area change of 7.5% versus 2.7%, p

Este trabalho busca compreender a construção teórica dos graduandos em Psicologia pertencentes ao oitavo semestre de uma instituição privada de ensino superior de Brasília. Trabalhou-se com uma situação-problema cujo intuito era compreendermos, a partir das respostas dos discentes, se existe a produção singular do sujeito que aprende em sua resposta ou se ele reproduz teorias aleatoriamente. Foram 37 participantes e nesse trabalho constam 10 respostas. Esta pesquisa, por ser de caráter interpretativo e construtivo, a ênfase está na produção teórica do pesquisador. Para isso, utilizamos a Pesquisa Epistemológica Qualitativa (1997, 2002, 2005) e a Teoria da Subjetividade (2003, 2004a, 2007), que tem como autor o psicólogo cubano Gonzalez Rey, na confecção metodológica-teórica. Poucos alunos conseguiram gerar uma resposta criativa perante a situação-problema. O que se viu foi o mimetismo teórico, a dicotomização dos fenômenos psicológicos, a análise a-histórica do homem e o conhecimento embasado nos princípios do positivismo nas respostas.

BackgroundA steep rise in the use of transcatheter aortic valve implantation (TAVI) for the management of symptomatic severe aortic stenosis occurred. Minimalist TAVI procedures and streamlined patient pathways within experienced Heart Valve Centres are designed to overcome the challenges of ever-increasing procedural volume.AimsThe 2022 European TAVI Pathway Survey aims to describe contemporary TAVI practice across Europe.Materials and methodsBetween October and December 2022, TAVI operators from 32 European countries were invited to complete an online questionnaire regarding their current practice.ResultsResponses were available from 147 TAVI centres in 26 countries. In 2021, the participating centres performed a total number of 27,223 TAVI procedures, with a mean of 185 TAVI cases per centre (median 138; IQR 77–194). Treatment strategies are usually (87%) discussed at a dedicated Heart Team meeting. Transfemoral TAVI is performed with local anaesthesia only (33%), with associated conscious sedation (60%), or under general anaesthesia (7%). Primary vascular access is percutaneous transfemoral (99%) with secondary radial access (52%). After uncomplicated TAVI, patients are transferred to a high-, medium-, or low-care unit in 28%, 52%, and 20% of cases, respectively. Time to discharge is day 1 (12%), day 2 (31%), day 3 (29%), or day 4 or more (28%).ConclusionReported adoption of minimalist TAVI techniques is common among European TAVI centres, but rates of next-day discharge remain low. This survey highlights the significant progress made in refining TAVI treatment and pathways in recent years and identifies possible areas for further improvement.

PurposeDose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes.MethodsThe proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.ResultsThe dosimetric accuracy of the context-based method displays

Objective:Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity.Methods:We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis.Results:The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950.Conclusions:Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups.Advances in knowledge:The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

Stroke following transcatheter aortic valve replacement (TAVR) is a significant and life-threatening adverse event. The vast majority of these incidents occur during the TAVR procedure or within the first 24 h following TAVR, with a notable prevalence of cerebral embolic events. In response to this concern, cerebral embolic protection devices (CEPDs) have been designed to mitigate the risk of peri-procedural ischemic stroke during TAVR. The primary objective of CEPDs is to diminish the intraprocedural burden associated with new silent ischemic brain injuries. Despite the development of several CEPDs, their clinical efficacy remains uncertain. In this review, we delve into a comprehensive analysis of the utilization of CEPDs in patients undergoing TAVR, exploring insights from the existing literature. Additionally, we aim to present future perspectives and discuss the clinical implications associated with the incorporation of CEPDs in TAVR procedures. [ABSTRACT FROM AUTHOR]

Background and purpose: Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity. Materials and methods: Algorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting. Results: ARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions. Conclusion: The high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.

ObjectiveA recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed.MethodsUsing a unique set of digital phantoms based on patient data and verified with a motion phantom, this work identifies the minimum scanner parameters required to successfully generate free-breathing artefact-free fast-helical scans. A motion phantom and 5 patients were imaged 25 times under free-breathing conditions in alternating directions with a 64-slice CT scanner employing a low-dose fast-helical protocol. A series of high temporal resolution (0.1 s) 5DCT scan data sets was generated in each case. A simulated CT scanner was used to "image" each free-breathing data set. Various CT scanner detector widths and rotation times were simulated, and verified using the motion phantom results. Motion-induced artefacts were quantified in patient images using structural similarity maps to determine the similarity between axial slices.ResultsIncreasing amounts of motion-induced artefacts were observed with increasing rotation times >0.2 s for 16 mm detector configuration.ConclusionThe current generation of 16-slice CT scanners, which are present in the majority of Radiation Oncology departments, are not capable of generating free-breathing sorting artefact-free images required for 5DCT. Advances in knowledge: A recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed. The results suggest that the current generation of 16-slice CT scanners, present in the majority of Radiation Oncology departments, are not capable of generating the free-breathing images required for 5DCT.

Introduction Type 1 diabetes (T1D) is a lifelong illness that affects over 2500 children in Ireland. Management involves complex daily regimens including frequent blood glucose monitoring, pharmacotherapy, dietary management, and physical activity (PA). PA is an important modifiable lifestyle factor. Unfortunately, children with T1D remain physically inactive. Children with T1D face disease-specific barriers and facilitators to PA engagement. All aspects of T1D management for children are supported or supervised by parents. Thus, the purpose of this study was to examine parents’ and children’s perceptions of barriers and facilitators to PA engagement.Research design and methods 43 parent and child dyads participated. Parents completed a self-report survey. Children completed a modified version of the Physical Activity Questionnaire for Children (PAQ-C) that explored habitual PA patterns, perceived facilitators and barriers to PA engagement.Results 21 females, 22 males and their parents (36 mothers, 7 fathers) participated. 69% of males and 90% of females reported that having diabetes did affect their PA participation. 54% of males and 48% of females were insufficiently active based on their total PAQ-C score (