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This article is concerned with digital, multimodal feedback that supports learning and assessment within education. Drawing on the research literature alongside a case study from a postgraduate program in digital education, I argue that approaching feedback as an ongoing dialogue presented in richly multimodal and digital form can support opportunities for learning that are imaginative, critical, and in-tune with our increasingly digitally-mediated society. Using the examples of a reflective blogging exercise and an assignment built in the Second Life virtual world, I demonstrate how the tutor’s emphasis on providing feedback in multimodal form, alongside more conventional print-based approaches, inspired and emboldened students towards the creation of apt and sophisticated coursework. At the same time, the crafting of multimodal feedback carries resource implications and can sit uncomfortably with some deep-rooted assumptions around language-based representations of academic knowledge. This article should be seen in the context of a growing pedagogic and institutional interest in feedback around assessment, alongside the emergence of new ways of communicating and consuming academic content in richly multimodal ways. In this setting, multimodality, technology, and interaction refers to the digitally-mediated dialogue that takes place between the student and tutor around assessment.

Purpose Little is known about the scale of clinical implementation of automated treatment planning techniques in the United States. In this work, we examine the barriers and facilitators to adoption of commercially available automated planning tools into the clinical workflow using a survey of medical dosimetrists. Methods/materials Survey questions were developed based on a literature review of automation research and cognitive interviews of medical dosimetrists at our institution. Treatment planning automation was defined to include auto‐contouring and automated treatment planning. Survey questions probed frequency of use, positive and negative perceptions, potential implementation changes, and demographic and institutional descriptive statistics. The survey sample was identified using both a LinkedIn search and referral requests sent to physics directors and senior physicists at 34 radiotherapy clinics in our state. The survey was active from August 2020 to April 2021. Results Thirty‐four responses were collected out of 59 surveys sent. Three categories of barriers to use of automation were identified. The first related to perceptions of limited accuracy and usability of the algorithms. Eighty‐eight percent of respondents reported that auto‐contouring inaccuracy limited its use, and 62% thought it was difficult to modify an automated plan, thus limiting its usefulness. The second barrier relates to the perception that automation increases the probability of an error reaching the patient. Third, respondents were concerned that automation will make their jobs less satisfying and less secure. Large majorities reported that they enjoyed plan optimization, would not want to lose that part of their job, and expressed explicit job security fears. Conclusion To our knowledge this is the first systematic investigation into the views of automation by medical dosimetrists. Potential barriers and facilitators to use were explicitly identified. This investigation highlights several concrete approaches that could potentially increase the translation of automation into the clinic, along with areas of needed research.

There are several unexplored regions of the short-duration radio transient phase space. One such unexplored region is the luminosity gap between giant pulses (from pulsars) and cosmologically located fast radio bursts (FRBs). The Survey for Transient Astronomical Radio Emission 2 (STARE2) is a search for such transients out to 7 Mpc. STARE2 has a field of view of 3.6 steradians and is sensitive to 1 millisecond transients above ∼300 kJy. With a two-station system we have detected and localized a solar burst, demonstrating that the pilot system is capable of detecting short duration radio transients. We found no convincing non-solar transients with duration between 65 s and 34 ms in 200 days of observing, limiting with 95% confidence the all-sky rate of transients above ∼300 kJy to <40 sky−1 yr−1. If the luminosity function of FRBs could be extrapolated down to 300 kJy for a distance of 10 kpc, then one would expect the rate to be ∼2 sky−1 yr−1.

Background Stereotactic body radiotherapy (SBRT) is becoming increasingly used in treating localized prostate cancer (PCa), with evidence showing similar toxicity and efficacy profiles when compared with longer courses of definitive radiation. Magnetic resonance imaging (MRI)-guided radiotherapy has multiple potential advantages over standard computed tomography (CT)-guided radiotherapy, including enhanced prostate visualization (abrogating the need for fiducials and MRI fusion), enhanced identification of the urethra, the ability to track the prostate in real-time, and the capacity to perform online adaptive planning. However, it is unknown whether these potential advantages translate into improved outcomes. This phase III randomized superiority trial is designed to prospectively evaluate whether toxicity is lower after MRI-guided versus CT-guided SBRT. Methods Three hundred men with localized PCa will be randomized in a 1:1 ratio to SBRT using CT or MRI guidance. Randomization will be stratified by baseline International Prostate Symptom Score (IPSS) (≤15 or > 15) and prostate gland volume (≤50 cc or > 50 cc). Five fractions of 8 Gy will be delivered to the prostate over the course of fourteen days, with or without hormonal therapy and elective nodal radiotherapy (to a dose of 5 Gy per fraction) as per the investigator’s discretion. The primary endpoint is the incidence of physician-reported acute grade ≥ 2 genitourinary (GU) toxicity (during the first 90 days after SBRT), as assessed by the CTCAE version 4.03 scale. Secondary clinical endpoints include incidence of acute grade ≥ 2 gastrointestinal (GI) toxicity, 5-year cumulative incidences of physician-reported late grade ≥ 2 GU and GI toxicity, temporal changes in patient-reported quality of life (QOL) outcomes, 5-year biochemical recurrence-free survival and the proportion of fractions of MRI-guided SBRT in which online adaptive radiotherapy is used. Discussion The MIRAGE trial is the first randomized trial comparing MRI-guided with standard CT-guided SBRT for localized PCa. The primary hypothesis is that MRI-guided SBRT will lead to an improvement in the cumulative incidence of acute grade ≥ 2 GU toxicity when compared to CT-guided SBRT. The pragmatic superiority design focused on an acute toxicity endpoint will allow an early comparison of the two technologies. Trial registration Clinicaltrials.gov identifier: NCT04384770. Date of registration: May 12, 2020. https://clinicaltrials.gov/ct2/show/NCT04384770 Protocol version Version 2.1, Aug 28, 2020.

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 day's 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.

Purpose The commercial 0.35‐T magnetic resonance imaging (MRI)‐guided radiotherapy vendor ViewRay recently introduced upgraded real‐time imaging frame rates based on compressed sensing techniques. Furthermore, additional motion tracking algorithms were made available. Compressed sensing allows for increased image frame rates but may compromise image quality. To assess the impact of this upgrade on respiratory gating accuracy, we evaluated gated dose distributions pre‐ and post‐upgrade using a motion phantom and radiochromic film. Methods Seven motion waveforms (four artificial, two patient‐derived free‐breathing, and one breath‐holding) were used to drive an MRI‐compatible motion phantom. A treatment plan was developed to deliver a 3‐cm diameter spherical dose distribution typical of a stereotactic body radiotherapy plan. Gating was performed using 4‐frames per second (fps) imaging pre‐upgrade on the “default” tracking algorithm and 8‐fps post‐upgrade using the “small mobile targets” (SMT) and “large deforming targets” (LDT) tracking algorithms. Radiochromic film was placed in a moving insert within the phantom to measure dose. The planned and delivered dose distributions were compared using the gamma index with 3%/3‐mm criteria. Dose–area histograms were produced to calculate the dose to 95% (D95) of the sphere planning target volume (PTV) and two simulated gross tumor volumes formed by contracting the PTV by 3 and 5 mm, respectively. Results Gamma pass rates ranged from 18% to 93% over the 21 combinations of breathing trace and gating conditions examined. D95 ranged from 206 to 514 cGy. On average, the LDT algorithm yielded lower gamma and D95 values than the default and SMT algorithms. Conclusion Respiratory gating at 8 fps with the new tracking algorithms provides similar gating performance to the original algorithm with 4 fps, although the LDT algorithm had lower accuracy for our non‐deformable target. This indicates that the choice of deformable image registration algorithm should be chosen deliberately based on whether the target is rigid or deforming.

Purpose Image‐guided radiotherapy (IGRT) research sometimes involves simulated changes to patient positioning using retrospectively collected clinical data. For example, researchers may simulate patient misalignments to develop error detection algorithms or positioning optimization algorithms. The Brainlab ExacTrac system can be used to retrospectively “replay” simulated alignment scenarios but does not allow export of digitally reconstructed radiographs (DRRs) with simulated positioning variations for further analysis. Here we describe methods to overcome this limitation and replicate ExacTrac system DRRs by using projective geometry parameters contained in the ExacTrac configuration files saved for every imaged subject. Methods Two ExacTrac DRR generators were implemented, one with custom MATLAB software based on first principles, and the other using libraries from the Insight Segmentation and Registration Toolkit (ITK). A description of perspective projections for DRR rendering applications is included, with emphasis on linear operators in real projective space P3 ${\\mathbb{P}^3}$ . We provide a general methodology for the extraction of relevant geometric values needed to replicate ExacTrac DRRs. Our generators were tested on phantom and patient images, both acquired in a known treatment position. We demonstrate the validity of our methods by comparing our generated DRRs to reference DRRs produced by the ExacTrac system during a treatment workflow using a manual landmark analysis as well as rigid registration with the elastix software package. Results Manual landmarks selected between the corresponding DRR generators across patient and phantom images have an average displacement of 1.15 mm. For elastix image registrations, we found that absolute value vertical and horizontal translations were 0.18 and 0.35 mm on average, respectively. Rigid rotations were within 0.002 degrees. Conclusion Custom and ITK‐based algorithms successfully reproduce ExacTrac DRRs and have the distinctive advantage of incorporating any desired 6D couch position. An open‐source repository is provided separately for users to implement in IGRT patient positioning research.

Background This study utilizes interviews of clinical medical physicists to investigate self‐reported shortcomings of the current weekly chart check workflow and opportunities for improvement. Methods Nineteen 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. Results Physicists 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. Conclusion This 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.

Technological advances in MRI-guided radiation therapy (MRIgRT) have improved real-time visualization of the prostate and its surrounding structures over CT-guided radiation therapy. Seminal studies have demonstrated safe dose escalation achieved through ultrahypofractionation with MRIgRT due to planning target volume (PTV) margin reduction and treatment gating. On-table adaptation with MRI-based technologies can also incorporate real-time changes in target shape and volume and can reduce high doses of radiation to sensitive surrounding structures that may move into the treatment field. Ongoing clinical trials seek to refine ultrahypofractionated radiotherapy treatments for prostate cancer using MRIgRT. Though these studies have the potential to demonstrate improved biochemical control and reduced side effects, limitations concerning patient treatment times and operational workflows may preclude wide adoption of this technology outside of centers of excellence. In this review, we discuss the advantages and limitations of MRIgRT for prostate cancer, as well as clinical trials testing the efficacy and toxicity of ultrafractionation in patients with localized or post-prostatectomy recurrent prostate cancer.

Background The use of deep learning‐based auto‐contouring algorithms in various treatment planning services is increasingly common. There is a notable deficit of commercially or publicly available models trained on large or diverse datasets containing high‐dose‐rate (HDR) brachytherapy treatment scans, leading to poor performance on images that include HDR implants. Purpose To implement and evaluate automatic organs‐at‐risk (OARs) segmentation models for use in prostatic‐and‐gynecological computed tomography (CT)‐guided high‐dose‐rate brachytherapy treatment planning. Methods and materials 1316 computed tomography (CT) scans and corresponding segmentation files from 1105 prostatic‐or‐gynecological HDR patients treated at our institution from 2017 to 2024 were used for model training. Data sources comprised six CT scanners including a mobile CT unit with previously reported susceptibility to image streaking artifacts. Two UNet‐derived model architectures, UNet++ and nnU‐Net, were investigated for bladder and rectum model training. The models were tested on 100 CT scans and clinically‐used segmentation files from 62 prostatic‐or‐gynecological HDR brachytherapy patients, disjoint from the training set, collected in 2024. Performance was evaluated using the Dice‐Similarity‐Coefficient (DSC) between model predicted contours and clinically‐used contours on slices in common with the Clinical‐Target‐Volume (CTV). Additionally, a blinded evaluation of ten random test cases was conducted by three experienced planners. Results Median (interquartile range) 3D DSC on CTV‐containing slices were 0.95 (0.04) and 0.87 (0.09) for the UNet++ bladder and rectum models, respectively, and 0.96 (0.03) and 0.88 (0.10) for the nnU‐Net. The rank‐sum test did not reveal statistically significant differences in these DSC (p = 0.15 and 0.27, respectively). The blinded evaluation scored trained models higher than clinically‐used contours. Conclusion Both UNet‐derived architectures perform similarly on the bladder and rectum and are adequately accurate to reduce contouring time in a review‐and‐edit context during HDR brachytherapy planning. The UNet++ models were chosen for implementation at our institution due to lower computing hardware requirements and are in routine clinical use.