Ke Cao, Josephine Yeung, Yasser Arafat, Jing Qiao, Richard Gartrell, Mobin Master, Justin M C Yeung, Paul N Baird
Introduction: This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.
Methods: A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.
Results: The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001).
Conclusions: Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.
{"title":"Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients.","authors":"Ke Cao, Josephine Yeung, Yasser Arafat, Jing Qiao, Richard Gartrell, Mobin Master, Justin M C Yeung, Paul N Baird","doi":"10.1002/jmrs.798","DOIUrl":"https://doi.org/10.1002/jmrs.798","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.</p><p><strong>Methods: </strong>A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.</p><p><strong>Results: </strong>The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001).</p><p><strong>Conclusions: </strong>Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081409","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}
Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 71, Issue 4 December 2024.
{"title":"Continuing Professional Development - Medical Imaging","authors":"","doi":"10.1002/jmrs.795","DOIUrl":"10.1002/jmrs.795","url":null,"abstract":"<p>Maximise your CPD by reading the following selected article and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence. Answers will be available via the QR code and published in JMRS – Volume 71, Issue 4 December 2024.</p><p>Scan this QR code to find the answers.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"318"},"PeriodicalIF":2.1,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912173","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}
Magdalena Dolic BRadMedImag (Hons), Yaxuan Peng BRadMedImag (Hons), Keshav Dhingra BRadMedImag (Hons), Kristal Lee BRadMedImag (Hons), John McInerney HDipHPE. GCHPE, PGCertIV Leadership and Management, PGCert CT Imaging, PGDip IV cannulation, BSc(Rad) Hons
Introduction
In 2020, the Medical Radiation Practice Board of Australia made several revisions to its professional capabilities. To address this, medical radiation practitioners, including diagnostic radiographers, are required to escalate urgent findings in all radiographic settings. However, the confidence of radiographers in articulating descriptions of radiographic findings varies despite this requirement. This cross-sectional study explores how the implementation of eportfolio affects student self-perceived confidence in identifying and describing radiographic findings in both an academic and a clinical setting.
Methods
A Qualtrics survey was distributed to second-year radiography students who had used eportfolios. The survey comprised of four questions using a Likert-scale and one open-ended question. Quantitative data were analysed using the Wilcoxon signed-rank test and qualitative data was thematically assessed.
Results
Overall, 55 of 65 radiographic students (85%) completed the survey. Confidence (strongly agree and agree) decreased from 89% to 74% between academic and clinical environments when identifying abnormalities, and 89% to 73% when describing findings. This finding highlights the challenges students face when in the clinical environment. Wilcoxon signed rank test analysed a statistically significant relation between the two environments (P < 0.05). However, the relationship between identifying and describing skills was not statistically significant (P > 0.05). Following a review of the qualitative data, three recurring themes were identified among responses.
Conclusion
ePortfolios assist in improving confidence in identification and description of radiographic abnormalities, particularly in an academic setting. The clinical environment presents unique challenges which may limit student clinical performance; however, this requires further investigation.
{"title":"ePortfolios: Enhancing confidence in student radiographers' communication of radiographic anatomy and pathology. A cross-sectional study","authors":"Magdalena Dolic BRadMedImag (Hons), Yaxuan Peng BRadMedImag (Hons), Keshav Dhingra BRadMedImag (Hons), Kristal Lee BRadMedImag (Hons), John McInerney HDipHPE. GCHPE, PGCertIV Leadership and Management, PGCert CT Imaging, PGDip IV cannulation, BSc(Rad) Hons","doi":"10.1002/jmrs.787","DOIUrl":"10.1002/jmrs.787","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>In 2020, the Medical Radiation Practice Board of Australia made several revisions to its professional capabilities. To address this, medical radiation practitioners, including diagnostic radiographers, are required to escalate urgent findings in all radiographic settings. However, the confidence of radiographers in articulating descriptions of radiographic findings varies despite this requirement. This cross-sectional study explores how the implementation of eportfolio affects student self-perceived confidence in identifying and describing radiographic findings in both an academic and a clinical setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A Qualtrics survey was distributed to second-year radiography students who had used eportfolios. The survey comprised of four questions using a Likert-scale and one open-ended question. Quantitative data were analysed using the Wilcoxon signed-rank test and qualitative data was thematically assessed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Overall, 55 of 65 radiographic students (85%) completed the survey. Confidence (strongly agree and agree) decreased from 89% to 74% between academic and clinical environments when identifying abnormalities, and 89% to 73% when describing findings. This finding highlights the challenges students face when in the clinical environment. Wilcoxon signed rank test analysed a statistically significant relation between the two environments (<i>P</i> < 0.05). However, the relationship between identifying and describing skills was not statistically significant (<i>P</i> > 0.05). Following a review of the qualitative data, three recurring themes were identified among responses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>ePortfolios assist in improving confidence in identification and description of radiographic abnormalities, particularly in an academic setting. The clinical environment presents unique challenges which may limit student clinical performance; however, this requires further investigation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"403-411"},"PeriodicalIF":1.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855114","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}
Quality management systems (QMS) in nuclear medicine is an essential component of the Quality program and is instrumental in the safe delivery of a high standard clinical service. The IAEA QUANUM program is a nuclear medicine specific audit program that can be used to assess the standards of a nuclear medicine department and its service delivery. Regular internal and external audits are encouraged as part of the QMS.
{"title":"The importance of quality management systems in nuclear medicine departments","authors":"Kunthi Pathmaraj MSc (Radiation Physics), BSc Applied Science (Medical Radiations), Grad Dip Computer Science","doi":"10.1002/jmrs.793","DOIUrl":"10.1002/jmrs.793","url":null,"abstract":"<p>Quality management systems (QMS) in nuclear medicine is an essential component of the Quality program and is instrumental in the safe delivery of a high standard clinical service. The IAEA QUANUM program is a nuclear medicine specific audit program that can be used to assess the standards of a nuclear medicine department and its service delivery. Regular internal and external audits are encouraged as part of the QMS.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"167-169"},"PeriodicalIF":2.1,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682113","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}
Natasha Roos B Tech (Radiography), Tintswalo Brenda Mahlaola M Tech (Radiography), Lynne Hazell PhD (Health Sciences)
Introduction
Posterior tibial nerve (PTN) cross-sectional area (CSA) reference values for the diagnosis of tarsal tunnel syndrome (TTS) using ultrasound imaging exist in several countries but not in South Africa (SA). Therefore, the objective was to measure the CSA reference values for PTN in SA.
Methods
Ultrasound CSA measurements of PTN in both ankles on 112 participants were performed, the mean measurement was recorded, and the effect of race, age, gender, and body mass index (BMI) were recorded.
Results
In this study, the primary variables age and BMI affect the CSA measurement of the PTN. A positive correlation was found between PTN asymptomatic size and age (r = 0.196, P < 0.05), size and BMI (r = 0.200, P < 0.05). Age (categories) had a mean value of 3.17 for the age group 36–45 years (95% confidence interval (CI) 2.9–3.4). The mean BMI was 30.0 kg/m2 (CI 28.57–31.08). As for the asymptomatic PTN, a mean CSA reference value of 0.10 cm2 was obtained.
Conclusion
With increase in age and BMI, a greater PTN measurement will occur. Race appears to be a contributing factor, but further research is needed in this regard. The reference CSA value for normal PTN should be set at 0.10 cm2 for all racial groups for a basic musculoskeletal ultrasound exam protocol in South Africa.
{"title":"Cut-off value for a normal posterior tibial nerve to diagnose tarsal tunnel syndrome amongst people of different race in Pretoria, South Africa","authors":"Natasha Roos B Tech (Radiography), Tintswalo Brenda Mahlaola M Tech (Radiography), Lynne Hazell PhD (Health Sciences)","doi":"10.1002/jmrs.792","DOIUrl":"10.1002/jmrs.792","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Posterior tibial nerve (PTN) cross-sectional area (CSA) reference values for the diagnosis of tarsal tunnel syndrome (TTS) using ultrasound imaging exist in several countries but not in South Africa (SA). Therefore, the objective was to measure the CSA reference values for PTN in SA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Ultrasound CSA measurements of PTN in both ankles on 112 participants were performed, the mean measurement was recorded, and the effect of race, age, gender, and body mass index (BMI) were recorded.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In this study, the primary variables age and BMI affect the CSA measurement of the PTN. A positive correlation was found between PTN asymptomatic size and age (<i>r</i> = 0.196, <i>P</i> < 0.05), size and BMI (<i>r</i> = 0.200, <i>P</i> < 0.05). Age (categories) had a mean value of 3.17 for the age group 36–45 years (95% confidence interval (CI) 2.9–3.4). The mean BMI was 30.0 kg/m<sup>2</sup> (CI 28.57–31.08). As for the asymptomatic PTN, a mean CSA reference value of 0.10 cm<sup>2</sup> was obtained.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>With increase in age and BMI, a greater PTN measurement will occur. Race appears to be a contributing factor, but further research is needed in this regard. The reference CSA value for normal PTN should be set at 0.10 cm<sup>2</sup> for all racial groups for a basic musculoskeletal ultrasound exam protocol in South Africa.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"396-402"},"PeriodicalIF":1.8,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681619","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}
The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.
{"title":"Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready","authors":"Vanessa Panettieri PhD, Giovanna Gagliardi PhD","doi":"10.1002/jmrs.791","DOIUrl":"10.1002/jmrs.791","url":null,"abstract":"<p>The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 2","pages":"174-176"},"PeriodicalIF":2.1,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679761","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}
The currently accepted best practice radiation treatment for left breast cancer patients is Deep Inspiration Breath Hold (DIBH) where patients hold a deep breath to reduce late cardiac and pulmonary effects from treatment. DIBH can be challenging and induce or exacerbate anxiety in patients due to the perceived pressure to reduce radiation treatment side effects. This study explored the experiences of patients treated with Deep Inspiration Breath Hold Radiation Therapy (DIBH-RT) to improve patient-centred care and inform the design of multimedia educational tools for future patients undergoing DIBH.
Methods
This descriptive qualitative study was underpinned by a social constructivist approach to create new educational and patient care approaches based on previous patients' experiences. Semi-structured interviews were conducted with patients who had completed DIBH-RT for breast cancer. Data was analysed with reflexive thematical analysis.
Results
Twenty-two patients were interviewed with five key themes identified: (1) informational needs, (2) care needs, (3) autonomy, (4) DIBH performance influencers and (5) other centredness. Recommendations were derived from these themes to improve future treatments of DIBH patients. These recommendations revolved around improvements to education, patient-centred care and strategies to improve self-efficacy with breath holding.
Conclusion
Patients offer a wealth of knowledge regarding their lived experiences with treatment which can enhance future patients' experiences if incorporated into their education and care. Eliciting patients' views of their DIBH-RT treatment highlighted the need to improve patient self-efficacy with DIBH through familiarity with their planned treatment from new multimedia education, and foster patient care to enhance their experience.
{"title":"Eliciting the views of left breast cancer patients' receiving deep inspiration breath hold radiation therapy to inform the design of multimedia education and improve patient-centred care for prospective patients","authors":"Kathleene Dower BApSc (MRS), MHSM, Georgia K.B. Halkett PhD, FASMIRT, BMedRad(Hons), GAICD, Haryana Dhillon BSc MA (Psych), PhD, Diana Naehrig Dr.Med., FMH RadOnc, MSc CoachPsych, PhD, Moira O'Connor BA (Hons), MSc, PhD","doi":"10.1002/jmrs.790","DOIUrl":"10.1002/jmrs.790","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The currently accepted best practice radiation treatment for left breast cancer patients is Deep Inspiration Breath Hold (DIBH) where patients hold a deep breath to reduce late cardiac and pulmonary effects from treatment. DIBH can be challenging and induce or exacerbate anxiety in patients due to the perceived pressure to reduce radiation treatment side effects. This study explored the experiences of patients treated with Deep Inspiration Breath Hold Radiation Therapy (DIBH-RT) to improve patient-centred care and inform the design of multimedia educational tools for future patients undergoing DIBH.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This descriptive qualitative study was underpinned by a social constructivist approach to create new educational and patient care approaches based on previous patients' experiences. Semi-structured interviews were conducted with patients who had completed DIBH-RT for breast cancer. Data was analysed with reflexive thematical analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Twenty-two patients were interviewed with five key themes identified: (1) informational needs, (2) care needs, (3) autonomy, (4) DIBH performance influencers and (5) other centredness. Recommendations were derived from these themes to improve future treatments of DIBH patients. These recommendations revolved around improvements to education, patient-centred care and strategies to improve self-efficacy with breath holding.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Patients offer a wealth of knowledge regarding their lived experiences with treatment which can enhance future patients' experiences if incorporated into their education and care. Eliciting patients' views of their DIBH-RT treatment highlighted the need to improve patient self-efficacy with DIBH through familiarity with their planned treatment from new multimedia education, and foster patient care to enhance their experience.</p>\u0000 </section>\u0000 </div>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 3","pages":"384-395"},"PeriodicalIF":1.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694613","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":"Sponsor Acknowledgement","authors":"","doi":"10.1002/jmrs.765","DOIUrl":"https://doi.org/10.1002/jmrs.765","url":null,"abstract":"","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 S1","pages":"1-2"},"PeriodicalIF":2.1,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537640","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":"Poster Abstracts","authors":"","doi":"10.1002/jmrs.767","DOIUrl":"https://doi.org/10.1002/jmrs.767","url":null,"abstract":"","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 S1","pages":"100-132"},"PeriodicalIF":2.1,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537642","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":"Oral Abstracts","authors":"","doi":"10.1002/jmrs.766","DOIUrl":"https://doi.org/10.1002/jmrs.766","url":null,"abstract":"","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 S1","pages":"3-99"},"PeriodicalIF":2.1,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140537641","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}
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