iRadiology最新文献

{"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}

{"title":"Exploring the feasibility of integrating ultra-high field magnetic resonance imaging neuroimaging with multimodal artificial intelligence for clinical diagnostics","authors":"Yifan Yuan,&nbsp;Kaitao Chen,&nbsp;Youjia Zhu,&nbsp;Yang Yu,&nbsp;Mintao Hu,&nbsp;Ying-Hua Chu,&nbsp;Yi-Cheng Hsu,&nbsp;Jie Hu,&nbsp;Qi Yue,&nbsp;Mianxin Liu","doi":"10.1002/ird3.102","DOIUrl":"https://doi.org/10.1002/ird3.102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The integration of 7 Tesla (7T) magnetic resonance imaging (MRI) with advanced multimodal artificial intelligence (AI) models represents a promising frontier in neuroimaging. The superior spatial resolution of 7TMRI provides detailed visualizations of brain structure, which are crucial forunderstanding complex central nervous system diseases and tumors. Concurrently, the application of multimodal AI to medical images enables interactive imaging-based diagnostic conversation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this paper, we systematically investigate the capacity and feasibility of applying the existing advanced multimodal AI model ChatGPT-4V to 7T MRI under the context of brain tumors. First, we test whether ChatGPT-4V has knowledge about 7T MRI, and whether it can differentiate 7T MRI from 3T MRI. In addition, we explore whether ChatGPT-4V can recognize different 7T MRI modalities and whether it can correctly offer diagnosis of tumors based on single or multiple modality 7T MRI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>ChatGPT-4V exhibited accuracy of 84.4% in 3T-vs-7T differentiation and accuracy of 78.9% in 7T modality recognition. Meanwhile, in a human evaluation with three clinical experts, ChatGPT obtained average scores of 9.27/20 in single modality-based diagnosis and 21.25/25 in multiple modality-based diagnosis. Our study indicates that single-modality diagnosis and the interpretability of diagnostic decisions in clinical practice should be enhanced when ChatGPT-4V is applied to 7T data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In general, our analysis suggests that such integration has promise as a tool to improve the workflow of diagnostics in neurology, with a potentially transformative impact in the fields of medical image analysis and patient management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 5","pages":"498-509"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561554","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 and radiomics applied to prostate cancer bone metastasis imaging: A review","authors":"S. J. Pawan,&nbsp;Joseph Rich,&nbsp;Jonathan Le,&nbsp;Ethan Yi,&nbsp;Timothy Triche,&nbsp;Amir Goldkorn,&nbsp;Vinay Duddalwar","doi":"10.1002/ird3.99","DOIUrl":"https://doi.org/10.1002/ird3.99","url":null,"abstract":"<p>The skeletal system is the most common site of metastatic prostate cancer and these lesions are associated with poor outcomes. Diagnosing these osseous metastatic lesions relies on radiologic imaging, making early detection, diagnosis, and monitoring crucial for clinical management. However, the literature lacks a detailed analysis of various approaches and future directions. To address this gap, we present a scoping review of quantitative methods from diverse domains, including radiomics, machine learning, and deep learning, applied to imaging analysis of prostate cancer with clinical insights. Our findings highlight the need for developing clinically significant methods to aid in the battle against prostate bone metastasis.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 6","pages":"527-538"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.99","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253358","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":"Three-dimensional time of flight magnetic resonance angiography at 5.0T: Visualization of the superior cerebellar artery","authors":"Ning Tian,&nbsp;Xiangsen Jiang,&nbsp;Lei Yu,&nbsp;Zudong Yin,&nbsp;Dan Yu,&nbsp;Jie Gan","doi":"10.1002/ird3.98","DOIUrl":"https://doi.org/10.1002/ird3.98","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>To explore the utility of 5.0T ultra-high field magnetic resonance (MR) for the assessment of the superior cerebellar artery (SCA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Imaging data from 55 patients (19 men and 36 women) who underwent three-dimensional time of flight MR angiography (3D-TOF-MRA) with 5.0T MRI in the Shandong University Affiliated Shandong Provincial Third Hospital from May 22, 2023 to June 16, 2023 were retrospectively analyzed. The origin, caliber, and course of the SCA were recorded. An independent sample <i>t</i>-test was used to compare the differences in quantitative indexes between the two groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 123 superior cerebellar arteries were detected in 55 patients. We found that 86.99% of superior cerebellar arteries were longer than the P3 segment of the posterior cerebral artery. The superior cerebellar arteries were divided into nine types according to the origin of the SCA, with Type A accounting for the highest proportion (approximately 49.09%). The mean diameter of the SCA was 1.11 ± 0.22 mm, while the mean diameters of the right and left sides were 1.13 ± 0.24 mm and 1.07 ± 0.27 mm, respectively. There were no differences in SCA diameters between the two sides (<i>p</i> &gt; 0.05).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>3D-TOF-MRA with ultra-high field 5.0T MR can effectively evaluate the SCA, and provides a new effective imaging evaluation method for clinical practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 5","pages":"491-497"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.98","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561714","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":"Ultra-high field magnetic resonance imaging in theranostics of mental disorders","authors":"Yajun Yin,&nbsp;Qiyong Gong","doi":"10.1002/ird3.97","DOIUrl":"https://doi.org/10.1002/ird3.97","url":null,"abstract":"&lt;p&gt;Mental disorders comprise a range of abnormal states that affect an individual's cognition, emotion, behavior, and social functioning, potentially distorting their perception of reality and seriously impacting their daily life, work, and interpersonal relationships. Mental disorders, including anxiety disorders, depression, schizophrenia, and bipolar disorder, impact not only individuals, but also their families and societies at large. The incidence of mental disorders increased by 31.6% between 1990 and 2007, and this trend continued between 2007 and 2017 (percentage change: 13.5%) [&lt;span&gt;1&lt;/span&gt;]. In China, the lifetime prevalence of mental disorders is 16.6% and has been reported to exhibit a trend toward increasing over time [&lt;span&gt;2&lt;/span&gt;]. In terms of the global disease burden, mental disorders were reported to account for 5.3% of total disability-adjusted life years in 2019, underscoring their significant impact on public health [&lt;span&gt;3&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Biomarkers derived from magnetic resonance imaging (MRI) provide objective and quantifiable data on both the anatomy and function of the target organ (e.g., the human brain). Because of their non-invasive nature, these MRI-derived biomarkers are increasingly recognized as being among the most clinically feasible tools. Psychoradiology, an emerging radiology subspecialty bridging medical imaging and psychiatry, represents the frontier of neuroimaging applications in the elucidation and evaluation of mental health issues. Since they were introduced in 2016, researchers and clinicians have been developing norms, protocols, and strategies to facilitate the clinical application of psychoradiological techniques [&lt;span&gt;4, 5&lt;/span&gt;]. The quantitative analysis of psychoradiological data has potentials for identifying the objective and diagnostic biomarkers with highly predictive value related to mental disorders. Although considerable progress has been made in the field of psychoradiology, further clinical application of imaging-based diagnostics for mental disorders remains challenging, primarily because of limitations in the reproducibility and generalizability of diagnostic models. While psychoradiology also offers potential insights into aberrant brain mechanisms and enhances the interpretability of neuromarkers, its progress appears to be approaching a plateau because of the resolution limitations of current MRI technology at the mesoscopic level. The emergence of ultra-high field MRI (UHF-MRI; typically 7T and above) has provided the opportunity to open a new chapter in the development of psychoradiology, adding spatial sampling that yields superior resolution, higher signal-to-noise ratios, increased sensitivity, amplified signal change [&lt;span&gt;6&lt;/span&gt;], and enhanced microvascular contribution.&lt;/p&gt;&lt;p&gt;In terms of structural imaging, a UHF-MRI allows the depiction of fine structures and subregions with superior clarity, such as the detailed visualization of the dentate granule cell layer of","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"2 5","pages":"427-429"},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.97","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561713","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}