Journal of Medical Imaging and Radiation Sciences最新文献

{"title":"Current Status of Personalized Care in Radiotherapy","authors":"","doi":"10.1016/j.jmir.2024.101464","DOIUrl":"10.1016/j.jmir.2024.101464","url":null,"abstract":"<div><div>There have been constant changes and progressive developments in the technology used for the planning and delivery of radiotherapy, especially with the utilisation of artificial intelligence (AI) and telemedicine in cancer diagnosis/treatments/outcomes. Advancements in medical technology and a growing understanding of individual patient characteristics have facilitated a paradigm shift towards personalized care in radiotherapy. With the ever-increasing complexity of the radiotherapy patient pathway and limited healthcare resources, we, as healthcare professionals within radiation oncology, are obliged to respond to these ongoing advances efficiently through various resource optimization initiatives with a patient-centered personalised care focus. The radiotherapy workflows and models of care should be continuously evolved with an emphasis on high-quality communications/interactions between patients and clinicians/radiation therapists, in order to support the consistent engagement of patients and their families at each stage of their cancer care journey. Against this background, this presentation will include the following:</div><div>- Complexity of radiation therapy pathways and the importance of patient-centered personalised care</div><div>- Current landscape of personalized approaches in radiotherapy, encompassing the integration of advanced imaging modalities and patient-specific factors</div><div>- Existing challenges on radiation therapy resources and workforce supply</div><div>- Examples of innovative model of care such as case expert model and radiation therapist advanced practice with the focus personalised care in radiotherapy</div><div>- A glimpse into the future – how should we move forward as a multi-professional group for patient-centered personalised care optimisation.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530549","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":"Assessing the Diagnostic Reference Levels of Head CT Scan at Garoua Regional Medical Imaging Center","authors":"","doi":"10.1016/j.jmir.2024.101506","DOIUrl":"10.1016/j.jmir.2024.101506","url":null,"abstract":"<div><h3>Introduction</h3><div>Diagnostic Reference Levels (DRLs) are benchmarks used in medical imaging to optimize radiation doses while maintaining diagnostic image quality. They have emerged as a critical tool in this endeavor, serving as benchmarks to ensure that radiation doses from CT scans are kept within acceptable limits without compromising diagnostic efficacy. This study focuses on establishing DRLs for head CT scans at the Garoua Regional Hospital.</div></div><div><h3>Methods</h3><div>A cross sectional study was performed from January to December 2022. Our sample included adult patients who have Head CT examination. Some variable parameters considered are: The patient age, sex, CT indications, the DLP, the CTDIvol, the voltage, rotation time and slice thickness. The DRL for each type of indication was defined as the 75th percentile of its PDL and CTDIvol</div></div><div><h3>Results</h3><div>621 CT scan were analyzed revealing a male predominance (52.5%) and diverse indications, with strokes and traumas accounting for 27.2%. The mean age of patients was 42 ± 7 years (18 - 99 years), 37.8% were between 18 to 29 years old. The Dose distributions, specifically CTDIvol and DLP, are detailed for different protocols including strokes, non-vascular cases, sinus examinations, and angiography: 692±243 mGy·cm, 669±152mGy·cm, 78±23mGy·cm, 320±82mGy·cm. The study highlights the prevalence of stroke and trauma protocols, emphasizing the need for tailored imaging approaches for diverse neurological conditions. Comparative analyses with international practices in Uganda and Ireland reveal variations in radiation dose metrics across locations and devices.</div></div><div><h3>Conclusion</h3><div>The findings underscore the importance of optimizing radiation doses while maintaining diagnostic efficacy. The study not only contributes to regional DRLs but also provides a foundation for enhancing patient safety, refining imaging practices, and promoting dose optimization strategies in head CT scans.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530382","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":"Coexisting Management and Professional Skills in Radiology Technologists","authors":"","doi":"10.1016/j.jmir.2024.101501","DOIUrl":"10.1016/j.jmir.2024.101501","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>In the past, the development of professional skills by medical practitioners has greatly benefited patients. Currently, management skills, which should be developed in balance with professional skills, seem to have been neglected. The increasing imbalance between these two skills has rendered communication between healthcare professionals and patients, and among healthcare professionals increasingly difficult. This study was conducted to clarify the current status of management and professional skills among radiology technologists and to explore solutions to the imbalance problem.</div></div><div><h3>Methods</h3><div>This literature review examines previous studies, focusing on the views of the various authors regarding management skills. The subsequent interview survey included a total of 17 participants with professional training in radiology working at seven medical institutions nationwide (seven department heads and nine subordinates, and one former director of the Japan Radiological Technologists Association, who was added to supplement the analysis).</div></div><div><h3>Results</h3><div>Management skill enhancement requires learning, teaching, and mentoring. Some universities and other educational institutions have focused on the importance of management skills and are making efforts to focus on collaborative education of many professions. However, some staff members have reacted negatively to progressive initiatives such as \"reassignment to other departments,\" including open communities with diversity. Clarification of the reasons for such negative reactions is the subject of this study.</div></div><div><h3>Conclusions</h3><div>A demonstrated need exists to enhance professional skills in radiology. Radiology technologists are not AI; they are flesh-and-blood people in the workplace, and workplaces require management skills. Professional skills can be augmented with management skills. However, it is not possible to augment management skills with professional skills.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530419","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":"Differentiation of Benign and Malignant Lymph Nodes using Ultrasound-based Radiomics and Machine Learning","authors":"","doi":"10.1016/j.jmir.2024.101544","DOIUrl":"10.1016/j.jmir.2024.101544","url":null,"abstract":"<div><h3>Background</h3><div>The evaluation of lymph node characteristics is crucial for tumor staging and patient prognosis assessment, but cytological and histopathological examinations of lymph nodes are invasive and costly. This study aims to develop machine learning models for differentiating benign and malignant lymph nodes based on radiomics features of grayscale ultrasound images and patients‘ clinical characteristics.</div></div><div><h3>Methods</h3><div>Between 2021 and 2023, a total of 285 ultrasound images of lymph nodes were collected from 88 patients. The diagnosis of lymph nodes was confirmed by pathological examination. The image feature reduction process was done by student's t-test, Pearson correlation analysis, and Random Forest feature importance selection. Six well-established machine learning models, including Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), k-nearest Neighbors (KNN), Random Forest, XGBoost, and LightGBM, were developed using a combination of patient's clinical features and radiomics features of ultrasound images. The cases were randomly divided into training and test sets in an 8:2 ratio, and the area under the receiver operating characteristic curve (AUC) was adopted to evaluate model performance.</div></div><div><h3>Results</h3><div>There were 135 malignant and 150 benign cases in this study, including neck and axillary lymph nodes. A total of 11 radiomics features and one clinical feature were generated after the selection process, and they were used to build the final model. The AUC values of the SGD, SVM, KNN, Random Forest, XGBoost, and LightGBM in differentiating benign and malignant lymph nodes were 0.817, 0.765, 0.746, 0.816, 0.766, and 0.747, respectively.</div></div><div><h3>Conclusion</h3><div>By utilizing machine learning models, particularly the SGD and Random Forest, it is possible for radiomics features from ultrasound images to effectively classify benign and malignant lymph nodes, thereby improving diagnostic efficiency.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530425","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":"A Case Study: Maxillofacial MRI of a Fetal with a Complaint of Narrowing of the Upper Alveolar Process","authors":"","doi":"10.1016/j.jmir.2024.101505","DOIUrl":"10.1016/j.jmir.2024.101505","url":null,"abstract":"<div><h3>Purpose</h3><div>Tooth buds anomalies coincide with genetic disorders, and prenatal identification may contribute to a more accurate diagnosis. And fetal cleft lip and palate (CLP) is a common congenital facial malformation, which not only affects the appearance of children but also causes malnutrition in children with the difficulty of sucking milk. The purpose of the presentation was to improve the feasibility of fetal magnetic resonance imaging in visualizing intrauterine tooth buds alignment, CLP conditions and image quality.</div></div><div><h3>Method</h3><div>A 29-year-old pregnant woman was referred to our institution with an ultrasound report of a narrow upper alveolar process. We used 3.0T MRI Steady-State-Free-Precession (bSSFP) and Single-Shot Fast Spin-Echo (SS-FSE) sequences to examine this fetus for tooth bud abnormalities and CLP. Sagittal scanning is performed with the fetus swallowing amniotic fluid so that the tongue and palate are separated to better show the continuity of the hard and soft palate. The oblique axial position is scanned along the sagittal superior alveolar process to show the development of the tooth buds.</div></div><div><h3>Result</h3><div>Compared to the SS-FSE sequence, bSSFP sequence improves the SNR and contrast, and better shows the alignment of the tooth buds as well as the palate. It was finally confirmed that the fetus had a narrow upper alveolar eminence for abnormal tooth buds alignment (deciduous central incisor teeth, lateral incisor teeth and cusp incisors)and that there was no CLP.</div></div><div><h3>Conclusion</h3><div>The use of the bSSFP sequence to better shows fetal maxillofacial structures in the presence of amniotic fluid swallowing, also improves diagnostic accuracy and the diagnosis of associated syndromes.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530381","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":"A preliminary study of deep learning-based compressed sensing accelerated mDIXON for segmented coronary adipose tissue evaluation in patients with suspected coronary artery disease","authors":"","doi":"10.1016/j.jmir.2024.101503","DOIUrl":"10.1016/j.jmir.2024.101503","url":null,"abstract":"<div><h3>Background</h3><div>The secretion of dysfunctional PCAT is positively correlated with coronary artery stenosis, degree of calcification, and plaque progression. It is important to develop novel clinical diagnostic tools for coronary heart disease based on PCAT assessment. Homsi et al. introduced and validated coronary magnetic resonance angiography (MRA), based on the three-dimensional (3D)-modified Dixon (mDIXON) technique, for epicardial adipose tissue quantification. Therefore, the present study was to use non-contrast-enhanced compressed sensing artificial intelligence framework 3D mDIXON coronary MRA for PCAT quantification in patients with suspected CAD. It also evaluated segmented PCAT's relationship with coronary plaque characteristics and stenosis severity.</div></div><div><h3>Methods</h3><div>The study protocol was approved by the institutional ethics committee of the hospital. We included 35 symptomatic patients with CAD (111 arteries with plaque, 169 without plaque) (Figure 1). All the subjects underwent CMR on a 3T clinical MR scanner to evaluate segmented PCAT volume and fat-fraction of 8 coronary segments. We manually traced the segmented PCAT volume, and calculated the fat fraction of the segmented PCAT by formula: only fat images (F)/F + only water images (W). We compared the segmented PCAT volume and fat-fraction across 8 coronary segments with different plaque types and degrees of stenosis defined with CCTA and explored the relationship between them.</div></div><div><h3>Results</h3><div>The coronary segments with plaques had a higher segmented PCAT volume and fat-fraction than those without plaques. Meanwhile, segmented PCAT volume around mixed plaques was larger than non-calcified or calcified plaques (p = 0.014 and p &lt; 0.001) (Figure 3). There was a moderate correlation between the segmented PCAT volume and plaque type (r = 0.493, p &lt; 0.001). The fat-fraction had similar results (r = 0.480, p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The non-contrast-enhanced, whole-heart coronary MRA framework with CSAI is able to measure segmented PCAT volume and fat-fraction. The segmented PCAT volume is more significantly associated with the coronary plaque characters than fat-fraction.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530421","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":"Pancreatic Cancer Research from the Perspective of the RTT","authors":"","doi":"10.1016/j.jmir.2024.101455","DOIUrl":"10.1016/j.jmir.2024.101455","url":null,"abstract":"<div><div>Pancreatic cancer has the lowest one-year survival of any cancer in the UK. This illustrates the poor outlook suffered by patients and recognises it as a cancer of unmet need that requires further investigation. Pancreatic cancer is a global problem and according to worldwide cancer statistics, it is the 7^th top cause of cancer death. This presentation will cover a general background of ongoing pancreatic research to first highlight the bigger picture, before focussing on the current landscape of radiotherapy (RT) research for pancreatic cancer. This will be from a RTT perspective and will include a detailed case study of building a research career that investigates the many challenges of treating these patients.</div><div>RT challenges include abdominal motion that causes many uncertainties throughout the full RT pathway, and impacts image quality at each stage. Topics to be discussed will be accuracy and precision of treatment planning and delivery, acquisition of high-quality images with image guided-RT (IGRT), dealing with dose-limiting organs at risk, and determining response using advanced functional imaging protocols. The discussed issues require multi-disciplinary strategies to overcome them.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530387","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":"Improved patient outcomes and risk mitigation in Emergency Departments using a hybrid Radiographer Comment model","authors":"","doi":"10.1016/j.jmir.2024.101538","DOIUrl":"10.1016/j.jmir.2024.101538","url":null,"abstract":"<div><h3>Background/Purpose</h3><div>Verbal communication of medical imaging findings can be misinterpreted and lacks transparency. A hybrid model using radiographer comments and verbal notification to Emergency Departments (ED) was piloted across five hospitals for the more timely and safe communication of abnormal general x-ray appearances at point of care. Pilot data was evaluated to identify patient benefits and risks.</div></div><div><h3>Methods</h3><div>A multidisciplinary steering group advised on design and implementation strategies. The radiographer comments were transmitted from imaging consoles to ED dashboards and verbal calls provided to the ED doctor/referring team for critical/urgent conditions.</div><div>Radiographer comments (n=1102) were sent to five Emergency Departments (ED) by 69 radiographers (24/7) for a minimum of three months. Positive Predictive Values (PPV), reporting Turn Around Times (TAT) and clinically significant cases were collected at pilot sites. Radiographer comments were compared with radiology reports and classified as True Positive (TP), False Positive (FP) or indeterminate (ID) by two independent auditors. FP and ID comments were investigated with ED referrers and/or site radiologists. Risk assessments were conducted by two independent radiologists using low, moderate, high-very high categories. Radiology report discrepancies found incidentally were confirmed with ED doctors and further imaging data. Wilson Score Intervals determined confidence levels.</div></div><div><h3>Results</h3><div>The average pooled PPV was 0.96; (0.949 - 0.972; 95% CI). Incorrect comments (42) were analysed for potential harm; (3.9%; 95% CI: 2.9 - 5.3). A risk assessment for these demonstrated 37 low, five moderate and no high-very high-risk cases. 282 patient benefits (26.4 %; 95% CI: 23.8 – 29.1%) and 42 radiology report discrepancies were identified; (3.9%; 95% CI: 2.9 - 5.3).</div></div><div><h3>Conclusions</h3><div>The model is based on patient advocacy and has the potential to save lives. A quarter of patients benefited from radiographer comments. Risk mitigation was possible in 3.9% of cases. No adverse outcomes were reported.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530410","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":"FOCUS DWI and Deep Learning Reconstruction in breast MRI: A comparison with conventional DWI","authors":"","doi":"10.1016/j.jmir.2024.101543","DOIUrl":"10.1016/j.jmir.2024.101543","url":null,"abstract":"<div><h3>Purpose</h3><div>To employ deep-learning based reconstruction (DLR) to improve the SNR of FOCUS DWI for breast imaging in Asian patients and investigate the feasibility and performance of reduced-FOV FOCUS DWI and FOCUS DWI with deep learning-based reconstruction (DLR) for breast MRI in Asian patients with small breast volumes.</div></div><div><h3>Materials and Methods</h3><div>Forty-nine female patients suspected of having breast cancer from July 2023 to December 2023. They underwent breast MRI examinations using three sequences: Conventional DWI, Focus DWI, Focus-DLR DWI. Two radiologists independently assessed image quality using a 5-point Likert scale. They also outlined the lesions, calculating the signal-to-noise ratio (SNR) of the lesion, the Contrast-to-Noise Ratio (CNR) between the lesion and surrounding tissue, and the Apparent Diffusion Coefficient (ADC) of the lesion. Image scores, SNR, CNR and ADC were compared using the Friedman test.</div></div><div><h3>Results</h3><div>FOCUS-DLR DWI had higher scores in terms of the overall image quality, the anatomical details, lesion conspicuity, artifacts and distortion than conventional DWI (P&lt;0.001, P&lt;0.001, P&lt;0.001, P&lt;0.001, P&lt;0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional DWI and FOCUS DWI (P&lt;0.001, P&lt;0.001), while there were no statistically significant differences between FOCUS-DWI and conventional DWI(P&gt;0.05). What's more, in terms of CNR values and ADC values, there were no significant difference among three sequences.</div></div><div><h3>Conclusion</h3><div>Our findings indicate that FOCUS DWI with deep learning-based reconstruction produces superior images than conventional DWI, enhancing the applicability of this technique in clinical practice. Deep learning-based reconstruction provides a new direction for optimizing DWI imaging techniques in Asian breast MRI.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530424","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":"Application of Artificial Intelligence in Neck Ultrasound in the Era of Precision Medicine","authors":"","doi":"10.1016/j.jmir.2024.101458","DOIUrl":"10.1016/j.jmir.2024.101458","url":null,"abstract":"<div><div>The importance of artificial intelligence (AI) in medical healthcare is increasingly becoming apparent. There is a rapid growth of scientific research in medical AI in the past years, from 1,623 studies in 2012 to 29,947 studies in 2021, and many of these studies are related to radiology. By 2023, the FDA has approved 700 AI healthcare algorithms and 527 (75.3%) are in radiology. In AI-empowered radiology, the application of AI in ultrasound imaging is emerging which includes ultrasound of liver, beast, thyroid gland, lymph node, etc. Ultrasound is commonly used for the evaluation of head and neck masses. In patients with thyroid nodules, ultrasound is used for the differentiation of benign and malignant nodules, and guiding fine-needle aspiration. Ultrasound is also a common imaging modality to assess neck lymph nodes in head and neck cancer patients. Various AI-empowered and computer-assisted diagnostic tools for ultrasound examination of thyroid nodules are available. AI-based algorithms for lymph node segmentation and classification in ultrasound images are emerging. They help clinicians improve diagnostic accuracy and guide patient management. In this talk, different AI-empowered diagnostic tools for thyroid and lymph node ultrasound imaging will be introduced and discussed.</div></div>","PeriodicalId":46420,"journal":{"name":"Journal of Medical Imaging and Radiation Sciences","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530403","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}