Journal of Medical Radiation Sciences最新文献

{"title":"Continuing Professional Development – Answers","authors":"","doi":"10.1002/jmrs.828","DOIUrl":"10.1002/jmrs.828","url":null,"abstract":"<p>Maximise your Continuing Professional Development (CPD) by reading the selected articles and answer the five questions. Please remember to self-claim your CPD and retain your supporting evidence.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 4","pages":"616-617"},"PeriodicalIF":1.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818318","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":"Quantifying non-clinical outcomes of ultra-hypofractionated breast radiotherapy in Western NSW-A narrative review.","authors":"Faith Yeo, Rachael Beldham-Collins, Paul Roth, Rodney Hammond","doi":"10.1002/jmrs.842","DOIUrl":"https://doi.org/10.1002/jmrs.842","url":null,"abstract":"<p><p>Ultra-Hypofractionated Whole Breast Radiotherapy (U-WBRT) has been proven to be a viable treatment option for breast cancer patients receiving radiation therapy, however, due to its novelty our understanding of its non-clinical benefits is still evolving. With increasing U-WBRT selection during COVID and in rural and regional settings such as the Western New South Wales Local Health District (WNSWLHD), it's important to quantify the savings when compared to other fractionation schedules (e.g. Conventional fractionation (C-WBRT) involving 25 fractions and Moderate hypofractionation (M-WBRT) with 15 fractions.) Using literature sourced from Medline, Embase, Pubmed and reports from relevant websites and organisations this narrative review investigates quantifiable methods of assessing non-clinical benefits of U-WBRT in rural settings according to the triple bottom line philosophy. This review was able to identify a standard set of quantifiable metrics that can compare the non-clinical benefits of various fractionation schedules, with relevance to a rural setting. These include: fractionation trends, financial subsidy, average linear accelerator (Linac) minutes, hospital visits, travel time and distance, Linac energy consumption, travel and Linac carbon emissions. By identifying these metrics, non-clinical benefits between the fractionation schedules can easily be quantified and compared.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794781","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":"Acknowledging the peer reviewers of Journal of Medical Radiation Sciences, October 2023–September 2024","authors":"","doi":"10.1002/jmrs.838","DOIUrl":"10.1002/jmrs.838","url":null,"abstract":"&lt;p&gt;The editorial review board, the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) and the New Zealand Institute of Medical Radiation Technology (NZIMRT) acknowledge the following peer reviewers for their dedication and commitment to the journal. Thank you for sharing your valuable time and knowledge.&lt;/p&gt;&lt;p&gt;James Stanley&lt;/p&gt;&lt;p&gt;Peter Kench&lt;/p&gt;&lt;p&gt;Amy Brown&lt;/p&gt;&lt;p&gt;Karen Dobeli&lt;/p&gt;&lt;p&gt;Georgia Halkett&lt;/p&gt;&lt;p&gt;Andrew Kilgour&lt;/p&gt;&lt;p&gt;Scott Jones&lt;/p&gt;&lt;p&gt;Chandra Makanjee&lt;/p&gt;&lt;p&gt;Martin Mitchell&lt;/p&gt;&lt;p&gt;Robba Rai&lt;/p&gt;&lt;p&gt;John Robinson&lt;/p&gt;&lt;p&gt;Clare Singh&lt;/p&gt;&lt;p&gt;Adam Steward&lt;/p&gt;&lt;p&gt;Francis Zarb&lt;/p&gt;&lt;p&gt;Dana Al-Mousa&lt;/p&gt;&lt;p&gt;Nigel Anderson&lt;/p&gt;&lt;p&gt;Marilyn Baird&lt;/p&gt;&lt;p&gt;Rachael Beldham-Collins&lt;/p&gt;&lt;p&gt;Pippa Bresser&lt;/p&gt;&lt;p&gt;Mikaela Dell'Oro&lt;/p&gt;&lt;p&gt;Victoria Earl&lt;/p&gt;&lt;p&gt;Ernest Ekpo&lt;/p&gt;&lt;p&gt;Sheryl Foster&lt;/p&gt;&lt;p&gt;Ziba Gandomkar&lt;/p&gt;&lt;p&gt;David Gilmore&lt;/p&gt;&lt;p&gt;Therese Gunn&lt;/p&gt;&lt;p&gt;Peter Hanna&lt;/p&gt;&lt;p&gt;Richard W. Harbron&lt;/p&gt;&lt;p&gt;James Hayes&lt;/p&gt;&lt;p&gt;Lynne Hazell&lt;/p&gt;&lt;p&gt;Bronwyn Hilder&lt;/p&gt;&lt;p&gt;Peter Hogg&lt;/p&gt;&lt;p&gt;Yobelli Jimenez&lt;/p&gt;&lt;p&gt;Gerhardus Koch&lt;/p&gt;&lt;p&gt;Gordon Mander&lt;/p&gt;&lt;p&gt;Kristie Matthews&lt;/p&gt;&lt;p&gt;Sibusiso Mdletshe&lt;/p&gt;&lt;p&gt;Martin Necas&lt;/p&gt;&lt;p&gt;Michael Neep&lt;/p&gt;&lt;p&gt;Jodie Nixon&lt;/p&gt;&lt;p&gt;Tracey Pieterse&lt;/p&gt;&lt;p&gt;William Rae&lt;/p&gt;&lt;p&gt;Oona Reardon&lt;/p&gt;&lt;p&gt;Meegan Shepherd&lt;/p&gt;&lt;p&gt;Zhonghua Sun&lt;/p&gt;&lt;p&gt;John Thompson&lt;/p&gt;&lt;p&gt;Kenton Thompson&lt;/p&gt;&lt;p&gt;Adrienne Young&lt;/p&gt;&lt;p&gt;Sarah Lewis&lt;/p&gt;&lt;p&gt;Kamarul Abdullah&lt;/p&gt;&lt;p&gt;Laura Adamson&lt;/p&gt;&lt;p&gt;Verity Ahern&lt;/p&gt;&lt;p&gt;Theophilus Akudjedu&lt;/p&gt;&lt;p&gt;Sophie Alexander&lt;/p&gt;&lt;p&gt;Tonima Ali&lt;/p&gt;&lt;p&gt;Haney Alsleem&lt;/p&gt;&lt;p&gt;Sally Ball&lt;/p&gt;&lt;p&gt;Luke Barclay&lt;/p&gt;&lt;p&gt;Stevens Barry&lt;/p&gt;&lt;p&gt;Salvatore Berlangieri&lt;/p&gt;&lt;p&gt;Eva Bezak&lt;/p&gt;&lt;p&gt;Anita Bowman&lt;/p&gt;&lt;p&gt;Robyn Brady&lt;/p&gt;&lt;p&gt;Vicki Braithwaite&lt;/p&gt;&lt;p&gt;Maura Brown&lt;/p&gt;&lt;p&gt;Rachel Burton&lt;/p&gt;&lt;p&gt;Gemma Busuttil&lt;/p&gt;&lt;p&gt;Ethan Butson&lt;/p&gt;&lt;p&gt;Ke Cao&lt;/p&gt;&lt;p&gt;Yasmin Casmod&lt;/p&gt;&lt;p&gt;Chelsea Castillo&lt;/p&gt;&lt;p&gt;Crispen Chamunyonga&lt;/p&gt;&lt;p&gt;Shayne Chau&lt;/p&gt;&lt;p&gt;Nahid Chegeni&lt;/p&gt;&lt;p&gt;Deanne Chester&lt;/p&gt;&lt;p&gt;Kabilan Chokkappan&lt;/p&gt;&lt;p&gt;Jillian Clarke&lt;/p&gt;&lt;p&gt;James Crowhurst&lt;/p&gt;&lt;p&gt;Jennifer Dang&lt;/p&gt;&lt;p&gt;Rob Davidson&lt;/p&gt;&lt;p&gt;Jenna Dean&lt;/p&gt;&lt;p&gt;Pradip Deb&lt;/p&gt;&lt;p&gt;Edel Doyle&lt;/p&gt;&lt;p&gt;Elisabeth Elder&lt;/p&gt;&lt;p&gt;Kirsten Elleray&lt;/p&gt;&lt;p&gt;Doaa Elwadia&lt;/p&gt;&lt;p&gt;Andrew England&lt;/p&gt;&lt;p&gt;Brendan Erskine&lt;/p&gt;&lt;p&gt;Hafsa Essop&lt;/p&gt;&lt;p&gt;Mel Evans&lt;/p&gt;&lt;p&gt;Andrew Firman&lt;/p&gt;&lt;p&gt;Alannah Flockton&lt;/p&gt;&lt;p&gt;Janniko Georgiadis&lt;/p&gt;&lt;p&gt;Nicola Giannotti&lt;/p&gt;&lt;p&gt;Gopinath Gnanasegaran&lt;/p&gt;&lt;p&gt;Frances Grey&lt;/p&gt;&lt;p&gt;Roshini Gunewardena&lt;/p&gt;&lt;p&gt;Catriona Hargrave&lt;/p&gt;&lt;p&gt;Patrick Horsley&lt;/p&gt;&lt;p&gt;Chamandra Kammies&lt;/p&gt;&lt;p&gt;Abel Karera&lt;/p&gt;&lt;p&gt;Alannah Kejda&lt;/p&gt;&lt;p&gt;Toni Kelly&lt;/p&gt;&lt;p&gt;Ben Kennedy&lt;/p&gt;&lt;p&gt;Thandokuhle Khoza&lt;/p&gt;&lt;p&gt;Scott King&lt;/p&gt;&lt;p&gt;Tracy Kirkbride&lt;/p&gt;&lt;p&gt;Caroline Landelle&lt;/p&gt;&lt;p&gt;Drew Latty&lt;/p&gt;&lt;p&gt;Andrew Le&lt;/p&gt;&lt;p&gt;Fiona Lee&lt;/p&gt;&lt;p&gt;Margot Lehman&lt;/p&gt;&lt;p&gt;Shantel Lewis&lt;/p&gt;&lt;p&gt;Gaorui Liu&lt;/p&gt;&lt;p&gt;Kelly Lloyd&lt;/p&gt;&lt;p&gt;Magdalena Lutaka&lt;/p&gt;&lt;p&gt;Vanathy Manivasahan&lt;/p&gt;&lt;p&gt;Sonyia McFadden&lt;/p&gt;&lt;p&gt;Lisa McGuire&lt;/p&gt;&lt;p&gt;Glenda McLean&lt;/p&gt;&lt;p&gt;Vaughan Moutrie&lt;/p&gt;&lt;p&gt;Andrew Murphy&lt;/p&gt;&lt;p&gt;Soma Nesan&lt;/p&gt;&lt;p&gt;Michelle O'Connor&lt;/p&gt;&lt;p&gt;Katrina O'Keefe&lt;/p&gt;&lt;p&gt;Peter O'Reilly&lt;/p&gt;&lt;p&gt;Craig Opie&lt;/p&gt;&lt;p&gt;Brooke Osborne&lt;/p&gt;&lt;p&gt;Sharon Oultram&lt;/p&gt;&lt;p&gt;Vanessa Panettieri&lt;/p&gt;&lt;p&gt;Eric Pei Ping Pang","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":"71 4","pages":"618-620"},"PeriodicalIF":1.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmrs.838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769833","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":"Sonographic localisation of lymph nodes suspicious of metastatic breast cancer to surgical axillary levels.","authors":"Michelle Fenech, Tracey Burke, Grace Arnett, Alisha Tanner, Natasha Werder","doi":"10.1002/jmrs.840","DOIUrl":"https://doi.org/10.1002/jmrs.840","url":null,"abstract":"<p><p>The axillary lymph node (LN) burden of breast cancer patients guides multidisciplinary management and treatment regimes. Sonographic imaging is used to identify the presence, number and location of axillary LNs suspicious of malignancy and used to guide nodal fine needle aspirations and biopsies. Axillary LNs suspicious of harbouring breast cancer metastasis can be localised to three surgical axillary levels, numbered according to their location relative to the pectoralis minor muscle and lymph flow. To sonographically identify and localise suspicious axillary LNs, an understanding of the axillary anatomy, muscular sonographic landmarks, surgical axillary levels, and the sonographic technique to image and distinguish between benign and suspicious LNs is required.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648322","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":"Impact of pre-examination video education in Gd-EOB-DTPA-enhanced liver MRI: A comparative study.","authors":"Hongfang Huang, Chenhui Li, Zisan Zeng, Junli Liang","doi":"10.1002/jmrs.833","DOIUrl":"https://doi.org/10.1002/jmrs.833","url":null,"abstract":"<p><strong>Introduction: </strong>Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and early diagnosis via gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre-examination video education on anxiety, satisfaction and image quality in Gd-EOB-DTPA-enhanced liver MRI.</p><p><strong>Methods: </strong>We prospectively enrolled 480 patients who underwent Gd-EOB-DTPA-enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups.</p><p><strong>Results: </strong>The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre-contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05).</p><p><strong>Conclusion: </strong>Supplementing routine pre-scan care with video guidance for Gd-EOB-DTPA-enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video-based interventions to enhance the MRI experience for patients.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622170","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":"Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence.","authors":"Sibusiso Mdletshe, Alan Wang","doi":"10.1002/jmrs.837","DOIUrl":"https://doi.org/10.1002/jmrs.837","url":null,"abstract":"<p><p>The rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. image enhancement, segmentation and three-dimensional reconstruction) and artificial intelligence (AI) into the undergraduate MI curriculum. By examining current educational practices, gaps and limitations that hinder the development of future-ready MI professionals are identified. A comprehensive curriculum framework is proposed, incorporating essential computational skills, advanced image processing techniques and state-of-the-art AI tools, such as large language models like ChatGPT. The proposed curriculum framework aims to improve the quality of MI education significantly and better equip students for future professional practice and challenges while enhancing diagnostic accuracy, improving workflow efficiency and preparing students for the evolving demands of the MI field.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603832","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":"Deep learning in image segmentation for cancer","authors":"Robba Rai BAppSc (DiagRad), MHlthSc (MRI), PhD","doi":"10.1002/jmrs.839","DOIUrl":"10.1002/jmrs.839","url":null,"abstract":"<p>This article discusses the role of deep learning (DL) in cancer imaging, focusing on its applications for automatic image segmentation. It highlights two studies that demonstrate how U-Net- and convolutional neural networks–based architectures have improved the speed and accuracy of body composition analysis in CT scans and rectal tumour segmentation in MRI images. While the results are promising, the article stresses the need for further research to address issues like image quality variability across different imaging systems.\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 4","pages":"505-508"},"PeriodicalIF":1.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583050","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":"Molecular theranostics: principles, challenges and controversies.","authors":"Geoffrey Currie","doi":"10.1002/jmrs.836","DOIUrl":"https://doi.org/10.1002/jmrs.836","url":null,"abstract":"<p><p>Theranostics is a new term for long-established principles in nuclear medicine. The generalisability of the term means there is a very broad use of the term across the medical literature, not all of which is consistent with the intent in nuclear medicine. The term molecular theranostics better reflects the philosophy and application in nuclear medicine. Even with a clearer definition, there are a number of challenges or controversies whose debate provides a richer understanding of the principles and applications of molecular theranostics. Radioiodine imaging and therapy of hyperthyroidism and thyroid cancer provide the historical context for theranostics. The prototype molecular theranostic is the ⁶⁸Ga/¹⁷⁷Lu DOTATATE pair that targets somatostatin receptor subtype 2 in neuroendocrine tumors. The potential value of precision medicine of radiation dosimetry in molecular theranostics needs a balanced discussion with limitations of reactive dosimetry and the opportunities for predictive or pre-treatment dosimetry. Despite challenges and limitations, molecular theranostics is a powerful tool in the precision medicine landscape. Molecular theranostics is a vehicle for improved outcomes in cancer patients with a future-facing portfolio of opportunities.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142562452","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":"Occupational burnout in nuclear medicine technologists working in Australia and New Zealand - results of a multi-national survey.","authors":"Melissa Shields, Daphne James, Lynne McCormack","doi":"10.1002/jmrs.834","DOIUrl":"https://doi.org/10.1002/jmrs.834","url":null,"abstract":"<p><strong>Introduction: </strong>Occupational burnout can be associated with negative feelings about the workplace and feeling that a person's efforts are of little consequence. Within a healthcare setting, occupational burnout can be attributed to a high workload or a non-supportive work environment. Higher levels of burnout are associated with increased absenteeism and turnover, increased medical errors and decreased patient care. The aim of this study was to investigate the levels of occupational burnout within nuclear medicine technologists (NMTs) working in Australia and New Zealand.</p><p><strong>Methods: </strong>An online questionnaire was distributed via QuestionPro. The questionnaire consisted of four sections, including the Professional Quality of Life Scale (ProQOL) Version 5 (2009) measuring compassion satisfaction, burnout and secondary traumatic stress in the workplace. For this study, only the burnout component of this scale is reported.</p><p><strong>Results: </strong>There were 162 survey responses. Of the 18 New Zealand participants, 10 (56%) reported moderate levels of burnout. Of the 144 Australian participants, 114 (79%) reported moderate levels of burnout. No NMTs reported high levels of burnout. All states of Australia were represented in the survey, with Queensland, Western Australia and Victoria having the highest number of participants reporting moderate levels of burnout.</p><p><strong>Conclusion: </strong>This study revealed that more than half of New Zealand participants and three quarters of Australian participants reported moderate levels of burnout. It is imperative to address the wellbeing needs of NMTs working in Australia and New Zealand at an individual and organisational level to support NMTs to be more engaged in their work and help organisations retain staff.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142502242","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":"Size-specific dose estimates calculated using patient size measurements from scanned projection radiograph in high-resolution chest computed tomography.","authors":"Ajit Brindhaban","doi":"10.1002/jmrs.830","DOIUrl":"https://doi.org/10.1002/jmrs.830","url":null,"abstract":"<p><strong>Introduction: </strong>Size-specific dose estimates (SSDE) are used to assess patient-specific radiation exposure in Computed Tomography (CT), complementing the volume CT dose index (CTDIvol). This study compared SSDE calculated using patient's lateral size from scan projection radiograph (SPR) with SSDE calculated using water equivalent diameter (D_w) from tomographic images in adult chest high-resolution CT (HRCT).</p><p><strong>Methods: </strong>In a single-centre study, the CTDIvol and dose-length product (DLP) were recorded from HRCT dose reports of adult patients. Lateral width (SLat), at the centre of the scan range, from the SPR was measured and the SSDE (SSDE_R) was calculated using conversion factors related to SLat. Average CT number, area of the slice, and lateral size of the patient (AxLat) were measured on the middle slice. The D_w and SSDE from D_w (SSDE_W) were calculated. SSDE_R and SSDE_W were compared using Wilcoxon signed rank test. Correlation between patient size and dosimetry parameters were investigated using Spearman Correlation test with statistical significance at P < 0.05. Bland-Altman plot was also used to test agreement between the two SSDE values.</p><p><strong>Results: </strong>Median CTDIvol, DLP, SSDE_R and SSDE_W were 11.0 mGy, 372 mGy.cm, 11.6 mGy and 12.9 mGy, respectively. Small but statistically significant differences (P < 0.03) were found between SLat and AxLat as well as between SSDE_R and SSDE_W. Bland-Altman analysis resulted in borderline agreement between SSDE values. Moderate correlations were observed between dosimetry quantities and patient size measurements (ρ > 0.640; P < 0.001). SSDEw showed statistically significant correlation (ρ = 0.587 and P < 0.001) with SSDE_R.</p><p><strong>Conclusion: </strong>SSDE_R may be used to assess patients' absorbed radiation dose, before the scan, in adult chest HRCT. The median value of SSDE_R was about 10% lower than the median value SSDE_W. However, the SSDE_W should be used after the scan to establish effective dose and radiation risk to the patient.</p>","PeriodicalId":16382,"journal":{"name":"Journal of Medical Radiation Sciences","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142502243","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}