Journal of Medical Imaging and Radiation Sciences最新文献

{"title":"Message de la rédactrice en chef","authors":"","doi":"10.1016/j.jmir.2024.101739","DOIUrl":"10.1016/j.jmir.2024.101739","url":null,"abstract":"","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"10.1016/j.jmir.2024.101740","DOIUrl":"10.1016/j.jmir.2024.101740","url":null,"abstract":"","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Board/Masthead","authors":"","doi":"10.1016/S1939-8654(24)00482-X","DOIUrl":"10.1016/S1939-8654(24)00482-X","url":null,"abstract":"","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S193986542400482X/pdfft?md5=01cad8f5b27864c1852db7cace9e5606&pid=1-s2.0-S193986542400482X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing","authors":"","doi":"10.1016/j.jmir.2024.101745","DOIUrl":"10.1016/j.jmir.2024.101745","url":null,"abstract":"<div><h3>Background</h3><p>The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore “federated testing” as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations.</p></div><div><h3>Methods</h3><p>Our study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV.</p></div><div><h3>Results</h3><p>Our study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing.</p></div><div><h3>Conclusion</h3><p>Achieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic radiography clinical resources in a workplace-based learning setting","authors":"","doi":"10.1016/j.jmir.2024.101742","DOIUrl":"10.1016/j.jmir.2024.101742","url":null,"abstract":"<div><h3>Introduction</h3><p>Workplace-based learning (WBL) is a method that combines theoretical knowledge with practical experience, promoting learner autonomy. Diagnostic radiography students in South Africa face challenges due to inconsistencies in clinical resources from the public and private sectors. Insufficient clinical resources can negatively impact students' competence and confidence in their skills. This study explored diagnostic radiography students’ understanding of clinical resources.</p></div><div><h3>Methods</h3><p>A qualitative exploratory-descriptive and contextual study with 21 participants at a single Higher Education institution (HEI) in South Africa was conducted through one-on-one semi-structured interviews on Microsoft Teams. The data collection tool was an interview guide. Students’ narratives were transcribed and analysed using thematic analysis.</p></div><div><h3>Results</h3><p>Thematic analysis revealed four themes: Theme one: Defining clinical resources in WBL; Theme two: Experiences with clinical resources in WBL; Theme three: Accessibility of clinical resources in WBL and Theme four: Defining clinical resources in WBL.</p></div><div><h3>Conclusion</h3><p>Clinical resources are essential for radiography learning, promoting skills development and independent work. Balancing these resources with other activities is crucial for a comprehensive WBL experience. However, unequal access can hinder learning. Alternative solutions should be explored for specialised imaging techniques. Integrating simulation and collaborative practice between clinical practices and HEIs can enhance students' preparedness for real-world scenarios.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1939865424004739/pdfft?md5=a1298221f84784fd1b6a0b9b5569e4a1&pid=1-s2.0-S1939865424004739-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers","authors":"","doi":"10.1016/j.jmir.2024.101741","DOIUrl":"10.1016/j.jmir.2024.101741","url":null,"abstract":"<div><h3>Introduction</h3><p>Artificial Intelligence (AI) is increasingly implemented in medical imaging practice, however, its impact on radiographers practice is not well studied. The aim of this study was to explore the perceived impact of AI on radiographers’ activities and profession in Switzerland.</p></div><div><h3>Methods</h3><p>A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted.</p></div><div><h3>Results</h3><p>A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 64% of them did not feel confident with AI-terminology. Participants viewed AI as an opportunity (57%), while 19% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centered care, research, and patient safety. The significant threats identified were reduction on work positions (23%), decrease of the radiographers’ expertise level due to automation bias (16%). Participants (68%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (88%). 93% of the participants mentioned that AI education should be included at undergraduate education program.</p></div><div><h3>Conclusion</h3><p>Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The empathy project: Staff confidence and training needs in psychosocial patient care","authors":"","doi":"10.1016/j.jmir.2024.101735","DOIUrl":"10.1016/j.jmir.2024.101735","url":null,"abstract":"<div><h3>Introduction</h3><p>Radiation Therapists (RT) must balance highly technical procedures and psychosocial patient care during their daily work practices. While RTs are in a unique position to form rapport with patients and provide support and information, many RTs lack confidence in the psychosocial care of patients, and struggle to identify and address emotional cues. This study aimed to assess the confidence, competence, and training needs of RTs regarding psychosocial patient care.</p></div><div><h3>Methods</h3><p>All RTs at the Radiation Oncology Princess Alexandra Hospital Deparmtents were invited to participate in an electronic survey in May 2021, and May 2023 via email. The survey was distributed using SurveyMonkey and utilised a series of Likert-scale and open-ended questions.</p></div><div><h3>Results</h3><p>Staff reported high confidence in addressing technical treatment issues (98 % of respondents) and communicating with anxious (78 %) and distressed (78 %) patients. However, lower confidence was indicated with managing patient conflict (58 %) and communicating with patients who were depressed (53 %) or suffering mental health conditions (74 %). Staff were concerned about saying the wrong thing and experienced stress when they couldn't adequately communicate with patients requiring psychosocial support.</p><p>The most significant barriers to providing adequate psychosocial care included time, staff numbers, and workload. Staff requested training in emotional cues, communication, anxiety and depression, and understanding psychosocial and mental health conditions.</p></div><div><h3>Conclusion</h3><p>While staff perceptions and motivations of psychosocial care were overwhelmingly positive, further training is required to improve patient psychosocial care. Future steps include implementing psychosocial training resources in the departments, and reassessing staff confidence post training.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiographers’ perspectives on immobilisation and restraint mechanisms in paediatric radiography","authors":"","doi":"10.1016/j.jmir.2024.101738","DOIUrl":"10.1016/j.jmir.2024.101738","url":null,"abstract":"<div><h3>Introduction</h3><p>The utilisation of immobilisation or restraint during paediatric radiography is a multifactorial issue with associated legal and ethical considerations. Current practice regularly presents challenges for radiographers, with a notable absence of any professional best-practice guidance. This study investigated radiographers’ perspectives in Ireland on the use of immobilisation or restraint methods and the factors influencing these choices and aimed to fill the 20-year gap in European radiographers’ perspectives gaining an up-to-date understanding and contribute to the global conversation in this regard.</p></div><div><h3>Methods</h3><p>An anonymous online survey design was constructed using the Google Forms platform and distributed to hospital radiology departments across the Southern region of Ireland. Additionally, a short survey was send to the head of the nine radiology departments to audit the existing availability of immobilisation and/or restraint devices for paediatric radiography. Descriptive statistics (frequencies and percentages) were undertaken. Bar charts were utilised where appropriate to display trends.</p></div><div><h3>Results</h3><p>One hundered and three responses were received. The majority of radiographers reported using immobilisation (95 %) or restraint (89 %) techniques during paediatric radiography, although 70 % acknowledged the lack of training regarding restraint techniques. However, 93 % reported trying alternative techniques such as distraction and negotiation to avoid immobilisation or restraint. A desire for further guidance and training was reported, with 80 % of respondents noting training as essential or potentially beneficial.</p></div><div><h3>Conclusions</h3><p>Ambiguity remains surrounding the distinction between the terms “restraint” and “immobilisation” in paediatric radiography. Radiographers' use of restraint and immobilisation during paediatric radiography is influenced by the child's age, distress level, radiation protection and safety, and the ability of the child to understand instructions. Appropriate up-to-date guidelines and training would be recommended to clarify terminology and promote the safe practice of restraint and immobilisation during paediatric radiography.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous implementation of unrelated tumour sites on the MR Linac: A review of the commissioning process from a radiographer perspective and lessons learned","authors":"","doi":"10.1016/j.jmir.2024.101728","DOIUrl":"10.1016/j.jmir.2024.101728","url":null,"abstract":"<div><h3>Introduction</h3><p>This work reports on a systematic approach to select MRI sequences, quantify inter-observer image registration variation and determine patient positioning for the clinical implementation of MR-guided adaptive radiotherapy (MRgRT) in patients with oropharyngeal (H&amp;N) and lung cancer.</p></div><div><h3>Methods</h3><p>A total of 30 participants (N=10 H&amp;N and N=10 lung cancer patients and N=10 healthy participants) were scanned on the Elekta Unity Magnetic Resonance Linear Accelerator (MRL). Participant experience questionnaires were used to determine the most appropriate positioning device for lung treatments and tolerability of H&amp;N immobilization devices within the confined MR Linac environment. Visual guided assessments (VGAs) completed by three observers (one oncologist and two radiographers) were used to determine the most suitable tissue weighting (using vendor-provided 3D T1w and T2w sequences) for online image registration. Offline MRI to CT and MRI to MRI rigid registrations were undertaken by nine radiographers using bony and soft tissue matching. Single-factor ANOVA and paired t-tests were utilized to determine the interobserver variation.</p></div><div><h3>Results</h3><p>Based on oncologist and patient feedback, lung cancer patients would be treated in a vac-bag with their arms by their sides, while H&amp;N cancer patients would be immobilized using a 5-point fixation device and 5-point personalized thermoplastic shell. There was no clear preference for T1w or T2w images in the H&amp;N cohort. However, observers preferred T2w sequences for tumour and organ at risk (OAR) visualization in the lung images. When a bony match was conducted, single-factor ANOVA tests showed no statistically significant differences between all H&amp;N image registration types (p=0.09). For the soft-tissue registrations, T1w-CT and T1w-T1w registrations showed a statistically significant (p=0.01) reduction in inter-observer variability over T2w-CT registrations. Paired t-tests showed no statistically significant differences for bony or soft tissue matches using T1w or T2w sequences to the planning CT in the lung cohorts (p=0.63 and p=0.52, respectively).</p></div><div><h3>Conclusion</h3><p>We describe the systematic approach to the selection of strategies for imaging, immobilization, and online image registration we used for H&amp;N and lung cancer treatments on the MRL. This has facilitated the selection of the most appropriate adaptive MRgRT strategies for treating these sites at our institution.</p></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1939865424004594/pdfft?md5=13946487b9dff14db73bc95ab7995cb2&pid=1-s2.0-S1939865424004594-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sonographers in healthcare systems: A comparative commentary between the Danish and Canadian sonographic landscape","authors":"","doi":"10.1016/j.jmir.2024.101718","DOIUrl":"10.1016/j.jmir.2024.101718","url":null,"abstract":"","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}