Academic Radiology最新文献

{"title":"Image Quality Variation with Gantry Rotation Time and Reconstruction Algorithm in Ultra-high-resolution CT.","authors":"Minori Hoshika, Shingo Kayano, Noriaki Akagi, Tomohiro Inoue, Yoshinori Funama","doi":"10.1016/j.acra.2025.09.027","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.027","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>In ultra-high-resolution CT (U-HRCT), longer gantry rotation times are sometimes used to maintain image quality when using a small focal spot. This study aimed to evaluate the impact of gantry rotation time on image quality for deep learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and filtered back projection (FBP).</p><p><strong>Materials and methods: </strong>A phantom was scanned on a U-HRCT scanner at four dose levels and four gantry rotation times, with images reconstructed using DLR, MBIR, and FBP algorithms. Image quality was evaluated for noise characteristics and high-contrast resolution. Noise was characterized using the noise power spectrum (NPS) to compute the noise magnitude ratio and central frequency ratio for MBIR and DLR relative to FBP, while high-contrast resolution was determined from the profile curve.</p><p><strong>Results: </strong>MBIR and FBP demonstrated consistent image quality across all rotation times, with no statistically significant differences observed. In contrast, DLR showed significantly lower high-contrast resolution at a 1.0 s rotation time compared to 0.5-0.75 s (p<0.05). At 1.0 s, DLR also exhibited an unfavorable shift of the NPS toward lower frequencies, indicating degraded noise texture.</p><p><strong>Conclusion: </strong>While DLR delivers superior image quality at gantry rotation times of 0.5-0.75s, it exhibits a loss of resolution and altered noise texture at 1.0 s. This degradation is likely attributable to the algorithm's limitations when processing data distributions that were underrepresented in its training set. Therefore, to optimize diagnostic performance, scan parameters must be carefully tailored to the specific reconstruction algorithm.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Limitations and Generalizability of the Study on the Association Between Serum Carotenoid Concentrations and Neuroanatomical Changes Detected by Magnetic Resonance Imaging.","authors":"Hongling Zhang, Shengjun You","doi":"10.1016/j.acra.2025.09.030","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.030","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Quality Improvement and Artificial Intelligence Performance in Pulmonary Embolism Detection at Deep Learning Reconstruction-Based Ultra-low Radiation Dose CT Pulmonary Angiography.","authors":"Jinjuan Lu, Leilei Shen, Chun Zhou, Zhenghong Bi, Xiaodan Ye, Zicheng Zhao, Mengsu Zeng, Mingliang Wang","doi":"10.1016/j.acra.2025.09.018","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.018","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To evaluate the image quality of deep learning reconstruction (DLR)-based ultra-low dose (ULD) CT pulmonary angiography (CTPA) images and determine whether the artificial intelligence (AI) software can improve the diagnostic performance of radiologist for detecting pulmonary embolism (PE) with ULD images.</p><p><strong>Materials and methods: </strong>This prospective two-center study enrolled 144 patients with suspected PE who underwent CTPA from July to October 2024. Patients were randomized into two groups equally. Images in the routine-dose (RD) group were reconstructed using hybrid-iterative reconstruction (HIR), while ULD images were reconstructed using HIR and DLR. A subset of 56 participants (1:1 PE to non-PE ratio) in ULD group was randomly selected and evaluated by three radiologists with and without AI software. Reference standard was established by expert consensus. Interrater reliability was determined by intraclass correlation coefficient (ICC). The diagnostic results and interpretation times were recorded.</p><p><strong>Results: </strong>There were no significant differences in demographics between the two groups. ULD-DLR images exhibited significantly higher objective and subjective image quality compared to both RD-HIR and ULD-HIR images. Interobserver agreement was moderate for RD-HIR (ICC=0.77) and excellent for ULD-DLR images (ICC=0.84). For radiologist detection of PE assisted by AI, both ULD-HIR and ULD-DLR cohorts exhibited near-perfect accuracy, outperforming unassisted readings (sensitivity 79.8% vs. 91.7% and specificity 95.5% vs. 99.2% in ULD-HIR; sensitivity 90.5% vs. 96.4% and specificity 95.8% vs. 100.0% in ULD-DLR). AI assistance reduced interpretation time by 19.7% for ULD-HIR and 15.6% for ULD-DLR scans. The effective dose of ULD group was decreased by 74% compared to RD group.</p><p><strong>Conclusion: </strong>DLR can maintain the CTPA image quality even at ultra-low dose level, further ensuring the accuracy and efficiency of AI-assisted PE diagnosis while improving radiation safety.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pragmatic Framework for Addressing Challenges and Mitigating Bias in Radiology Residency Selection: An Academic Residency Program's Experience.","authors":"Inas Mohamed, Michael Wien, Emily Meyers, Shannon Sullivan, Emily Plas, Ameya Nayate, Navid Faraji, Nikhil Ramaiya","doi":"10.1016/j.acra.2025.09.034","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.034","url":null,"abstract":"<p><p>Radiology residency programs face substantial challenges every Match cycle due to the overwhelming number of applications, time constraints, and the constant paradigm shifts. The residency selection process is multifaceted, and biases can compromise every stage. Interviews are among the most notoriously subjective yet pivotal steps in the hiring process. Enhancing objective judgment in the residency selection is feasible by standardizing the process, shifting to a more structured interview format, and engaging a diverse array of individuals in the decision-making. Throughout the 2022-2025 ERAS cycles, our academic university-affiliated diagnostic radiology residency program instituted and continuously evolved a multilevel model for standardizing the residency application evaluation with the primary goal of providing an equal opportunity to all applicants, away from prejudices and biases, and to gauge residents who would resonate with our program's culture and mission. This manuscript clarifies the challenges and the subjectivity inherent in the residency selection process and details the specific strategies implemented to promote impartial applicants' evaluation. These strategies could potentially offer a systematic framework for programs seeking to reduce bias in the screening and assessment of residency applications.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency, Quality, and Content of Feedback to Radiology Residents: A Needs Assessment.","authors":"David Yang, Austin R Pantel, Scott Simpson, Sophia R O'Brien","doi":"10.1016/j.acra.2025.09.028","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.028","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Radiology trainee and faculty experiences with feedback are underexamined in the literature. We conducted a survey-based needs assessment to better understand radiology trainee and faculty perceptions of the feedback interactions at our institution.</p><p><strong>Materials and methods: </strong>A voluntary, anonymous, IRB-approved survey was sent to radiology residents and faculty in our academic radiology department in November 2024. Questions included demographics, frequency and setting of feedback, and feedback quality, content, and preferences.</p><p><strong>Results: </strong>22 of 68 residents (32%) and 40 of 145 faculty (28%) submitted the survey. Compared to faculty, residents perceived that feedback exchanges occurred less frequently (p<0.01), included less explanations of why changes were made to their report (p<0.01), and included less actionable next steps (p<0.01). A majority of residents desired feedback on their performance relative to expectations based on year in training; however, overall performance was reported by residents to be one of the least common topics in feedback exchanges. Multiple barriers to feedback were identified, including lack of time, high workload, and lack of resident initiation.</p><p><strong>Conclusion: </strong>Residents perceived feedback to occur less frequently and to include discussion of multiple topics less often than faculty perceived, aligning with findings from other specialties. Notably, residents desired feedback on their overall performance, as well as explanations of why changes were made to their report and actionable next steps. Future research can investigate radiology feedback culture, barriers, and best practices to design interventions to optimize radiology feedback interactions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clarifying Perceptions About Promotion, Tenure and Faculty Compensation in Academic Radiology.","authors":"Ajay Malhotra, Dheeman Futela, Seyedmehdi Payabvash, Dheeraj Gandhi","doi":"10.1016/j.acra.2025.08.051","DOIUrl":"https://doi.org/10.1016/j.acra.2025.08.051","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Potential of T1 Mapping in Breast MRI and the Need for Standardization.","authors":"Deniz Esin Tekcan Sanli, Ahmet Necati Sanli","doi":"10.1016/j.acra.2025.09.025","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.025","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical Appraisal of Imaging- and Pathology-based Machine Learning Models for HER2-Positive Breast Cancer: A Review of Feature Selection and Biological Plausibility.","authors":"Deniz Esin Tekcan Sanli, Ahmet Necati Sanli","doi":"10.1016/j.acra.2025.09.026","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.026","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-Based Habitat Model for Predicting Tumor Response and Survival in Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Combining Molecular Targeted Agents and Immune Checkpoint Inhibitors.","authors":"Xiao Shen, Jin-Xing Zhang, Hai-Tao Yan, Jin Liu, Sheng Liu, Hai-Bin Shi, Qing-Quan Zu","doi":"10.1016/j.acra.2025.09.022","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.022","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To develop and validate a CT-based habitat model for predicting tumor response and overall survival (OS) in patients with unresectable hepatocellular carcinoma (uHCC) undergoing transarterial chemoembolization, combined with molecular-targeted agents, and immune checkpoint inhibitors (TACE-MTAs-ICIs).</p><p><strong>Materials and methods: </strong>A total of 200 patients treated with TACE-MTAs-ICIs between June 2019 and August 2024 were retrospectively included. Voxel-level radiomic features were extracted from contrast-enhanced CT images, and tumor habitats were identified using K-means clustering. Radiomic features were extracted from both habitat subregions and the entire tumor volume (conventional radiomics). A support vector machine (SVM) model was developed to predict tumor response, with SHapley Additive exPlanations (SHAP) analysis applied for interpretability. In parallel, a Cox proportional hazards model was constructed to predict OS. Independent clinical risk factors were incorporated with radiomic features to build a combined model. Model performance was evaluated and compared using multiple statistical metrics.</p><p><strong>Results: </strong>In the test cohort, the habitat model achieved strong performance for tumor response prediction (AUC: 0.881) and OS stratification (C-index: 0.703; 1-year AUC: 0.788; 2-year AUC: 0.806), outperforming the conventional radiomics model. Notably, the integrated model combining habitat features and clinical variables further improved predictive accuracy, yielding an AUC of 0.884 for response prediction and superior OS discrimination (C-index: 0.749; 1-year AUC: 0.831; 2-year AUC: 0.841).</p><p><strong>Conclusion: </strong>The proposed CT-based habitat model enables a more accurate and interpretable assessment of treatment response and OS in HCC, offering valuable noninvasive biomarkers that reflect intra-tumor heterogeneity. This approach holds promise for improving individualized treatment planning and clinical outcomes.</p><p><strong>Data availability statement: </strong>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ischiofemoral Impingement: Morphologic Associations, Pelvic Asymmetry, and Clinical Correlates in a Retrospective Observational Study.","authors":"Gizem Timoçin Yığman, Hande Özen Atalay, Erol Erinç Dokuyucu","doi":"10.1016/j.acra.2025.09.023","DOIUrl":"https://doi.org/10.1016/j.acra.2025.09.023","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To investigate the association between quadratus femoris edema (QFE) and pelvic morphometric measurements, pathological findings, and pain in adults with narrowed ischiofemoral space (IFS), and to evaluate whether right-left asymmetry in pelvic morphology influences QFE development.</p><p><strong>Methods: </strong>This retrospective single-center study included 116 adults (mean age: 54.2 years; 88.8% female) who underwent bony pelvis MRI between January 2020-June 2025 and had IFS < 15 mm. Exclusion criteria included hip surgery, femoral avascular necrosis, pediatric hip disorders, post-traumatic deformities, and nondiagnostic examinations. MRI assessments included QFE, IFS, quadratus femoris space (QFS), ischial angle, intertuberous distance, femoral neck angle, and hamstring tendon area, as well as the presence of femoroacetabular impingement, labral tears, hamstring tendinosis.</p><p><strong>Results: </strong>QFE was present in 97 patients (83.6%). IFS and QFS were significantly smaller in edema-positive patients (p<0.001), with large-to-moderate effect sizes. Left femoral neck angle was modestly but significantly higher in the edema group (p=0.016), while other morphometrics showed no significant differences. Left-sided hamstring tendinosis was the only pathological finding associated with QFE (p=0.019). Pain was common (86.2%) but not correlated with QFE (p>0.05). Right-left asymmetry was significantly greater for QFS (p=0.005) and ischial angle (p=0.012) in patients with QFE compared to those without QFE.</p><p><strong>Conclusion: </strong>QFE is strongly linked to reduced IFS, QFS and to asymmetries of QFS, ischial angle, but not consistently to pain. This is the first study to explore the correlation between hip pain and QFE in adults as well as role of morphometric asymmetry in QFE development highlighting the value of bilateral assessment.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}