iRadiology最新文献

{"title":"White Matter Hyperintensities: Cerebral Small-Vessel Diseases and White Matter Microstructural Impairments","authors":"Hui Huang,&nbsp;Wei Song,&nbsp;Pengyu Wang,&nbsp;Ye Zhu,&nbsp;Lei Zheng,&nbsp;Chenzhen Shen,&nbsp;Hui Xu,&nbsp;Jianfeng Qiu","doi":"10.1002/ird3.121","DOIUrl":"https://doi.org/10.1002/ird3.121","url":null,"abstract":"<p>White matter hyperintensities (WMH) are very widespread in older adults and are imaging features of both cerebral small-vessel disease and white matter microstructural impairments. Recent studies have demonstrated a close association between WMH and some common diseases in older adults, including Alzheimer's disease and hypertension. Thus, studies of WMH are important for avoiding the occurrence of these diseases and improving the health status of older adults. This review summarizes the literature relating to WMH in terms of epidemiology, clinical presentation, pathogenesis, imaging features, and therapy. It also analyzes the limitations of present studies and provides perspectives on future directions.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"5-25"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521980","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":"Incidental Finding of Deep Vein Thrombosis on Routine Knee Magnetic Resonance Imaging","authors":"Karoline Kant,&nbsp;Michael Facek,&nbsp;Craig Buchan","doi":"10.1002/ird3.119","DOIUrl":"https://doi.org/10.1002/ird3.119","url":null,"abstract":"<p>A 53 year old male presented to his local doctor with an acute knee injury, limited knee movement and a haemoarthrosis. Patient did not have calf tenderness or clinical suspicion for deep vein thrombosis (DVT). He was referred for a routine acute knee magnetic resonance imaging (MRI) scan. This MRI demonstrated a full thickness anterior cruciate ligament tear with additional findings, of a probable incidental DVT. The relevant MRI findings suggestive of DVT in this case included perivascular edema and expanded caliber of the involved medial gastronomical veins on the axial proton density fat suppressed sequence (Figure 1), increased intraluminal T1 signal within the involved segment of vein and susceptibility within the vein related to haemosiderin deposition/blood products on the gradient echo sequence. DVTs are rarely reported on MRI scans. Awareness of suspicious imaging findings for both radiologists and surgeons may aid in recognition of incidental DVTs and improve management.</p><p><b>Karoline Kant:</b> writing–original draft (equal). <b>Michael Facek:</b> writing–review &amp; editing (equal). <b>Craig Buchan:</b> conceptualization (equal), writing–review &amp; editing (equal).</p><p>The authors have nothing to report.</p><p>The patient has provided consent for publication of this image.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"88-89"},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521966","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":"Physician Attitudes About Ultrasound-Guided Procedures","authors":"Emma Barry,&nbsp;Sanyukta Deshmukh,&nbsp;Vivian Zhang,&nbsp;Antoan Koshar,&nbsp;Haider Butt,&nbsp;Kenneth Rowe,&nbsp;Siamak Moayedi","doi":"10.1002/ird3.114","DOIUrl":"https://doi.org/10.1002/ird3.114","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>We aimed to study physician attitudes toward ultrasound-guided procedures and possible improvements. We hypothesized that the usage of ultrasound in procedures may be limited by a high barrier of entry and that most physicians would choose to adopt software that provides real-time image guidance if accessible.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A voluntary, cross-sectional survey of physicians at a single site was conducted using a five-point Likert scale. Data analysis included both descriptive and inferential statistical analyses and stratified by categorical descriptors, including variables of formal training, years of experience, and specialty of practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>One hundred sixteen physicians responded to the survey. The majority disagreed that there was a steep learning curve (57.5%) and that they need more time to identify structures under ultrasound (85.0%). Overall attitudes were mixed about the use of additional software to improve ease of use, but most (55.4%) had positive opinions toward the addition of real-time 3D reconstruction. Respondents without formal training were significantly more likely to agree that additional software would improve ease of ultrasound-guided procedures (<i>p</i> = 0.0389). Radiologists were significantly more likely to perceive a steeper learning curve and less likely to advocate for supplemental software compared to emergency medicine physicians, surgeons, or anesthesiologists.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Surveyed physicians demonstrated comfort with ultrasound-guided procedures and a mixed stance toward the use of additional software to assist with procedures. Those without formal training had significantly more positive attitudes toward the use of additional technology to augment ultrasound-guided procedures, suggesting a knowledge gap that may benefit from such technology.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"72-78"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521954","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":"Systematic Review of Phase Aberration Correction Algorithms for Transcranial Focused Ultrasound","authors":"Mingyu Wang,&nbsp;Zhouyang Xu,&nbsp;Bingbing Cheng","doi":"10.1002/ird3.112","DOIUrl":"https://doi.org/10.1002/ird3.112","url":null,"abstract":"<p>Transcranial focused ultrasound (tFUS) is an emerging modality with strong potential for non-invasively treating brain disorders. However, the inhomogeneity and complex structure of the skull induce substantial phase aberrations and pressure attenuation; these can distort and shift the acoustic focus, thus hindering the efficiency of tFUS therapy. To achieve effective treatments, phased array transducers combined with aberration correction algorithms are commonly implemented. The present report aims to provide a comprehensive review of the current methods used for tFUS phase aberration correction. We first searched the PubMed and Web of Science databases for studies on phase aberration correction algorithms, identifying 54 articles for review. Relevant information, including the principles of algorithms and refocusing performances, were then extracted from the selected articles. The phase correction algorithms involved two main steps: acoustic field estimation and transmitted pulse adjustment. Our review identified key benchmarks for evaluating the effectiveness of these algorithms, each of which was used in at least three studies. These benchmarks included pressure and intensity, positioning error, focal region size, peak sidelobe ratio, and computational efficiency. Algorithm performances varied under different benchmarks, thus highlighting the importance of application-specific algorithm selection for achieving optimal tFUS therapy outcomes. The present review provides a thorough overview and comparison of various phase correction algorithms, and may offer valuable guidance to tFUS researchers when selecting appropriate phase correction algorithms for specific applications.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"26-46"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521750","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":"Anatomic Boundary-Aware Explanation for Convolutional Neural Networks in Diagnostic Radiology","authors":"Han Yuan","doi":"10.1002/ird3.113","DOIUrl":"https://doi.org/10.1002/ird3.113","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Convolutional neural networks (CNN) have achieved remarkable success in medical image analysis. However, unlike some general-domain tasks where model accuracy is paramount, medical applications demand both accuracy and explainability due to the high stakes affecting patients' lives. Based on model explanations, clinicians can evaluate the diagnostic decisions suggested by CNN. Nevertheless, prior explainable artificial intelligence methods treat medical image tasks akin to general vision tasks, following end-to-end paradigms to generate explanations and frequently overlooking crucial clinical domain knowledge.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a plug-and-play module that explicitly integrates anatomic boundary information into the explanation process for CNN-based thoracopathy classifiers. To generate the anatomic boundary of the lung parenchyma, we utilize a lung segmentation model developed on external public datasets and deploy it on the unseen target dataset to constrain model explanations within the lung parenchyma for the clinical task of thoracopathy classification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Assessed by the intersection over union and dice similarity coefficient between model-extracted explanations and expert-annotated lesion areas, our method consistently outperformed the baseline devoid of clinical domain knowledge in 71 out of 72 scenarios, encompassing 3 CNN architectures (VGG-11, ResNet-18, and AlexNet), 2 classification settings (binary and multi-label), 3 explanation methods (Saliency Map, Grad-CAM, and Integrated Gradients), and 4 co-occurred thoracic diseases (Atelectasis, Fracture, Mass, and Pneumothorax).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We underscore the effectiveness of leveraging radiology knowledge in improving model explanations for CNN and envisage that it could inspire future efforts to integrate clinical domain knowledge into medical image analysis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"3 1","pages":"47-60"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521978","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 medical imaging","authors":"Bin Huang,&nbsp;Bo Gao","doi":"10.1002/ird3.111","DOIUrl":"https://doi.org/10.1002/ird3.111","url":null,"abstract":"&lt;p&gt;With the rapid development of science and technology, the application of artificial intelligence (AI) in various fields is constantly expanding, especially in the field of medical imaging [&lt;span&gt;1&lt;/span&gt;]. AI technology is suitable to be applied to standardized digital medical image big data based on digital imaging and communications in medicine protocol and picture archiving and communication system. With the integration of AI technology, this field is undergoing profound transformation, not only improving the accuracy and efficiency of diagnosis, but also significantly reducing the workload of doctors [&lt;span&gt;2&lt;/span&gt;]. At present, AI is widely used in medical imaging, including risk modeling and stratification, personalized screening, diagnosis (including classification of molecular pathologic subtypes), treatment response prediction, prognosis prediction, image segmentation, and image quality control. AI can help doctors identify and analyze lesions in various medical images, especially in diseases such as lung, breast, and prostate cancer. The research mainly focuses on the identification of benign and malignant, the measurement of risk factors, prognosis judgment and treatment guidance, and it is increasingly being used in the field of psychoradiology [&lt;span&gt;3&lt;/span&gt;]. In addition, AI is also focused on reducing image acquisition time and improving data quality. Through deep learning algorithms, AI can optimize imaging parameters, improve imaging quality, and reduce noise and artifacts.&lt;/p&gt;&lt;p&gt;This special issue of AI includes seven latest studies, which covers artificial intelligence of disease diagnosis and prediction, imaging technology model construction, image segmentation and quality control. Wang et al. [&lt;span&gt;4&lt;/span&gt;] used systematic review to summarize the technical methods, clinical applications and existing problems of artificial intelligence in cerebrovascular diseases, they found that the availability of algorithms, reliability of validation, and consistency of evaluation metrics may facilitate better clinical applicability and acceptance. Zhu et al. [&lt;span&gt;5&lt;/span&gt;] proposed a diffusion magnetic diffusion magnetic (dMRI) index reconstruction model based on deep learning methods-qIRR-Net and a training framework based on data enhancement and consistency loss, The reconstruction of dMRI index is realized without the influence of signal inhomogeneity, and the model validity is verified on simulated inhomogeneity data and real ultra-high field data, thus promoting the application of ultra-high field dMRI technology in medicine and clinic. Artificial intelligence-assisted compressed sensing is a deep learning technology based on convolutional neural networks which can reconstruct images with ultra-high resolution and reduce noise. On the premise of ensuring the quality of the image, the collection time of the sequence is greatly shortened. In this special issue, The application of assisted compressed sensing technology in 5T MRI","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"525-526"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252565","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":"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}