iRadiology最新文献

{"title":"Exposing the Rad to Radionuclide: Why Early Education in Theranostics Is Important to Radiologists","authors":"Andy Ho,&nbsp;Nicola Luppino,&nbsp;Lincoln J. Lim","doi":"10.1002/ird3.106","DOIUrl":"https://doi.org/10.1002/ird3.106","url":null,"abstract":"<p>\u0000 \u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"79-85"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521820","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":"An investigation into the applicability of rapid artificial intelligence-assisted compressed sensing in brain magnetic resonance imaging performed at 5 Tesla field strength","authors":"Liqiang Zhou,&nbsp;Jiaqi Wang","doi":"10.1002/ird3.108","DOIUrl":"https://doi.org/10.1002/ird3.108","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Brain magnetic resonance imaging (MRI) at 5 T offers unprecedented spatial resolution but is often limited by long scan times. Acceleration techniques, such as compressed sensing (CS) and artificial intelligence-assisted compressed sensing (ACS), have the potential to speed up the acquisition process while maintaining image quality. This study aims to evaluate and compare the performance of CS and ACS (with various acceleration factors) in brain MRI imaging at 5 T.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this study, we enrolled 12 healthy volunteers and compared ACS-accelerated 5 T brain MRI with conventional methods of CS. The ACS acceleration factors for the brain protocol, consisting of 3D T1-weighted sequences and 2D T2-weighted sequences, were optimized in a pilot study on healthy volunteers (acceleration factor, 2.06–3.41× in T2-weighted imaging and 3.52–8.49× in T1-weighted imaging). We evaluated the images acquired from patients using various acceleration methods on the basis of acquisition times, the signal-to-noise ratio (SNR), the contrast-to-noise ratio, subjective image quality, and diagnostic agreement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our findings revealed that ACS acceleration significantly reduced the acquisition times for T1- and T2-weighted sequences by up to 43% and 53%, respectively, compared with traditional CS at 5 T. Importantly, this acceleration was achieved while maintaining excellent image quality, demonstrated by higher or comparable SNR and contrast-to-noise ratio values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The optimal ACS acceleration factors for 5 T brain MRI were determined to be 2.73× for 2D T2-weighted sequences and 6.5× for 3D T1-weighted sequences. ACS not only facilitates rapid imaging but also ensures comparable image quality and diagnostic performance, highlighting its potential to revolutionize high-field MRI scanning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"584-593"},"PeriodicalIF":0.0,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248945","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":"Rete Middle Cerebral Artery","authors":"Yongming Wang,&nbsp;Xi Chen,&nbsp;Junsheng Liu","doi":"10.1002/ird3.109","DOIUrl":"https://doi.org/10.1002/ird3.109","url":null,"abstract":"<p>A 53 year-old woman presented with a 4 year history of recurrent dizziness episodes. Each episode lasted approximately 1 week and was not accompanied by nausea, or vomiting. Comprehensive vestibular assessments, including vestibular function tests, Dix–Hallpike maneuver, and the roll test, were all negative. The patient reported no atherosclerotic risk factors, such as hypertension, hyperlipidemia or diabetes mellitus, and her family history was unremarkable. General physical and neurological examinations were unremarkable. Central causes of dizziness were considered. A computed tomography angiography of the head revealed a plexiform configuration in the M1 segment of the right middle cerebral artery (MCA), with no other significant abnormalities (Figure 1a). Moyamoya disease was considered a possible diagnosis. Subsequent digital subtraction angiography demonstrated the absence of a normal right M1 segment, with a twig-like vascular network connecting to the distal MCA (Figure 1b,c). Additionally, the right carotid canal was found to be smaller than the left, suggesting potential congenital variants consistent with rete MCA (Figure 1d). The patient was treated with betahistine 6 mg three times daily, resulting in alleviation of her dizziness.</p><p>Rete MCA, also known as twig-like MCA, is a rare vascular anomaly characterized by the presence of a twig-like arterial network in the M1 segment of the MCA. This variant may present with either hemorrhagic or ischemic strokes, or, as in this case, with no clinical symptoms. The primary differential diagnoses for rete MCA include moyamoya disease/syndrome and atherosclerotic occlusion. In contrast to these conditions, rete MCA is typically unilateral, occurs exclusively in the M1 segment with a distinctive twig-like appearance, and does not involve atherosclerotic risk factors. Notably, it preserves the caliber and flow of the distal MCA. In surgical settings, rete MCA often presents as a white cord-like structure. Accurate diagnosis is essential, particularly for asymptomatic patients, to avoid unnecessary concerns about occlusion. For symptomatic individuals, surgical intervention may be considered.</p><p><b>Yongming Wang:</b> writing–original draft (lead). <b>Xi Chen:</b> writing–original draft (equal). <b>Junsheng Liu:</b> writing–review and editing (equal).</p><p>This study was approved by the Ethics Committee of The People's Hospital of Pingchang in 2024 (Approval Number: 20240731-121).</p><p>The patient provided informed consent for the publication of the case.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"86-87"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521798","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":"Propofol-Induced Moderate–Deep Sedation Modulates Pediatric Neural Activity: A Functional Connectivity Study","authors":"Qiang Zheng,&nbsp;Yiyu Zhang,&nbsp;Lin Zhang,&nbsp;Jian Wang,&nbsp;Jungang Liu","doi":"10.1002/ird3.110","DOIUrl":"https://doi.org/10.1002/ird3.110","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Previous studies have demonstrated the underlying neurophysiologic mechanism during general anesthesia in adults. However, the mechanism of propofol-induced moderate–deep sedation (PMDS) in modulating pediatric neural activity remains unknown, which therefore was investigated in the present study based on functional magnetic resonance imaging (fMRI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 41 children (5.10 ± 1.14 years, male/female 21/20) with fMRI were employed to construct the functional connectivity network (FCN). The network communication, graph-theoretic properties, and network hub identification were statistically analyzed (<i>t</i> test and Bonferroni correction) between sedation (21 children) and awake (20 children) groups. All involved analyses were established on the whole-brain FCN and seven sub-networks, which included the default mode network (DMN), dorsal attentional network (DAN), salience network (SAN), auditory network (AUD), visual network (VIS), subcortical network (SUB), and other networks (Other).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Under PMDS, significant decreases in network communication were observed between SUB-VIS, SUB-DAN, and VIS-DAN, and between brain regions from the temporal lobe, limbic system, and subcortical tissues. However, no significant decrease in thalamus-related communication was observed. Most graph-theoretic properties were significantly decreased in the sedation group, and all graphical features of the DMN showed significant group differences. The superior parietal cortex with different neurological functions was identified as a network hub that was not greatly affected.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Although the children had a depressed level of neural activity under PMDS, the crucial thalamus-related communication was maintained, and the network hub superior parietal cortex stayed active, which highlighted clinical practices that the human body under PMDS is still perceptible to external stimuli and can be awakened by sound or touch.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"61-71"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521970","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":"Foot pain as first presenting symptom of renal cell carcinoma, due to metastatic lesion in medial cuneiform","authors":"Christopher Kleimeyer,&nbsp;Stephanie Stoddart,&nbsp;Simon Platt,&nbsp;Craig A. Buchan","doi":"10.1002/ird3.107","DOIUrl":"https://doi.org/10.1002/ird3.107","url":null,"abstract":"<p>Metastatic renal cell carcinoma (RCC) lesions in the foot are a rare entity and uncommon finding in a series of foot radiographs ordered to investigate foot pain. We report the case of a 72 year old male who experienced left foot pain for a year, before developing intermittent haematuria and right flank pain, and subsequently being found to have right RCC with an osseous metastatic lesion in the left medial cuneiform.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"603-608"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248145","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":"Key imaging perspectives on Achilles tendon tears—A radiological roadmap: Pictorial essay","authors":"Amit Patle,&nbsp;Annapurna Srirambhatla,&nbsp;Abhishek J. Arora,&nbsp;Maheshwar Lakkireddy,&nbsp;Madhavan Velladurai,&nbsp;Deepak Kumar Maley,&nbsp;Mohit Kapoor","doi":"10.1002/ird3.104","DOIUrl":"https://doi.org/10.1002/ird3.104","url":null,"abstract":"<p>Achilles tendon tears (ATT) are commonly encountered in clinical practice although it is the strongest tendon in the body. ATT are reported to occur in 0.04% of the population annually. ATT may result from high impact sports trauma such as sudden dorsiflexion while weight bearing or repeated microtrauma to a compromised tendon. Multiple factors can decrease the tensile strength of the tendon predisposing it to rupture. The treatment approach for ATT is multifaceted, dependent on factors such as the degree of tear, distance between the retracted portions, tear location, and also on patient factors such as occupation and underlying predisposing factors. In this pictorial essay, we present the imaging features of normal Achilles tendon and in ATT across different modalities and outline essential reporting criteria for ATT.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"594-602"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252504","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":"Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review","authors":"Yituo Wang,&nbsp;Zeru Zhang,&nbsp;Ying Peng,&nbsp;Silu Chen,&nbsp;Shuai Zhou,&nbsp;Jiqiang Liu,&nbsp;Song Gao,&nbsp;Guangming Zhu,&nbsp;Cong Han,&nbsp;Bing Wu","doi":"10.1002/ird3.105","DOIUrl":"https://doi.org/10.1002/ird3.105","url":null,"abstract":"<p>The field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross-validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"557-570"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252362","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":"Deep learning-based reconstruction on intensity-inhomogeneous diffusion magnetic resonance imaging","authors":"Zaimin Zhu,&nbsp;He Wang,&nbsp;Yong Liu,&nbsp;Fangrong Zong","doi":"10.1002/ird3.100","DOIUrl":"https://doi.org/10.1002/ird3.100","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Ultra high field diffusion magnetic resonance imaging (dMRI) provides diffusion-weighted (DW) images with a high signal-to-noise ratio, but increases inhomogeneity, which affects the accuracy of dMRI metric reconstruction. Current methods for correcting inhomogeneity rarely consider the accuracy of the reconstructed dMRI metrics. Deep learning models for reconstructing metrics from dMRI signals typically assume that DW images have a homogeneous intensity. To address these challenges, we propose a deep learning model capable of directly reconstructing high-accuracy dMRI metric maps from inhomogeneous DW images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An attention-based <i>q</i>-space inhomogeneity-resistant reconstruction network (qIRR-Net) is proposed for the voxel-wise reconstruction of diffusion tensor imaging and diffusion kurtosis imaging metrics. A training procedure based on data augmentation and consistency loss is introduced to ensure that the reconstruction results of qIRR-Net are not affected by signal inhomogeneity. The 3T and 7T dMRI data from the Human Connectome Project are used for model training, testing, and evaluation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>On the 3T dMRI data with simulated inhomogeneity, qIRR-Net improves the peak signal-to-noise ratio by 5.39 and the structural similarity index measure by 0.18 compared with weighted linear least-squares fitting. On the 7T dMRI data, the metric maps reconstructed by qIRR-Net not only exhibit clearer tissue structures but also demonstrate greater stability compared with the weighted linear least-squares results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed qIRR-Net enables the accurate reconstruction of dMRI metrics from inhomogeneous DW images. This approach could potentially be expanded to obtain multiple artifact-free metric maps from ultrahigh field dMRI for neuroscience research and neurology applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"571-583"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143247461","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":"An unusual large mass of sclerosing angiomatoid nodular transformation","authors":"Maoli Xu,&nbsp;Zhibing Ruan","doi":"10.1002/ird3.103","DOIUrl":"https://doi.org/10.1002/ird3.103","url":null,"abstract":"<p>A 23-year-old man was admitted to the hospital after a physical examination revealed a space-occupying lesion in the spleen that had been present for over 2 months. The patient reported no significant symptoms, and laboratory tests showed no abnormalities. Abdominal computed tomography (CT) and abdominal magnetic resonance imaging scans identified a large soft tissue mass in the spleen, measuring 7.1 cm × 5.4 cm × 6.6 cm. A laparoscopic splenectomy was performed. During the procedure, the mass was observed to be dark red, encapsulated, and of medium consistency. Histological examination revealed destruction of the spleen's red and white pulp structure, with notable infiltration of lymphocytes, plasma cells, and histiocytes. Additionally, fibrous tissue hyperplasia and hyalinosis were present, with lobulated nodules forming in certain areas. Immunohistochemical staining results were positive for Vim, CD31, CD4, CD8, CD20, CD3, CD68, SMA, and IgG. The final pathological diagnosis was sclerosing hemangiomatoid nodular transformation of the spleen (sinus lacunar type; Figure 1).</p><p><b>Maoli Xu</b>: Writing—original draft (equal). <b>Zhibing Ruan</b>: Supervision (equal).</p><p>The authors declare that they have no conflicts of interest.</p><p>Not applicable.</p><p>The patient provided written informed consent at the time of entering this study.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 5","pages":"522-523"},"PeriodicalIF":0.0,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560403","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":"Fairness in artificial intelligence-driven multi-organ image segmentation","authors":"Qing Li,&nbsp;Yizhe Zhang,&nbsp;Longyu Sun,&nbsp;Mengting Sun,&nbsp;Meng Liu,&nbsp;Zian Wang,&nbsp;Qi Wang,&nbsp;Shuo Wang,&nbsp;Chengyan Wang","doi":"10.1002/ird3.101","DOIUrl":"https://doi.org/10.1002/ird3.101","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision-making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi-organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi-organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi-organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi-organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi-organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi-organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi-organ segmentation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"539-556"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253101","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}