Investigative Radiology最新文献

{"title":"Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT: Evaluation of Visual Scoring and Automated Quantification Algorithms.","authors":"Bjarne Kerber, Falko Ensle, Jonas Kroschke, Cecilia Strappa, Anna Rita Larici, Thomas Frauenfelder, Lisa Jungblut","doi":"10.1097/RLI.0000000000001128","DOIUrl":"10.1097/RLI.0000000000001128","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.</p><p><strong>Methods: </strong>One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings.</p><p><strong>Results: </strong>X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema ( P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans ( P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45).</p><p><strong>Conclusions: </strong>The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"291-298"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142893695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Developments and Future Perspectives in Magnetic Resonance Imaging and Computed Tomography Contrast Media.","authors":"Thomas Frenzel, Thomas Wels, Hubertus Pietsch, Laura Schöckel, Peter Seidensticker, Jan Endrikat","doi":"10.1097/RLI.0000000000001180","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001180","url":null,"abstract":"<p><strong>Abstract: </strong>This review provides a comprehensive analysis of recent advancements in computed tomography (CT) and magnetic resonance imaging (MRI) contrast media, offering a critical evaluation of current trends and exploring future directions in the field. New clinical developments within the last 5-8 years are considered as well as clinical efficacy and safety aspects.For CT, the general safety of low- and iso-osmolar iodinated contrast agents and their effect on renal and thyroid function are reviewed. Special attention is given to contrast-enhanced mammography and a short outlook to photon-counting CT is provided.For MRI, a brief update on general safety, nephrogenic systemic fibrosis and the presence of gadolinium in the brain is given. The 2 new high-relaxivity gadolinium-based contrast agents, gadopiclenol and gadoquatrane (in late-stage clinical development), are highlighted.The review also describes targeted gadolinium-based contrast agents, superparamagnetic iron oxide particles, and developments of manganese-based contrast agents. It also introduces the emerging field of glymphatic imaging.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.","authors":"Vera Inka Josephin Graeve, Simin Laures, Andres Spirig, Hasan Zaytoun, Claudia Gregoriano, Philipp Schuetz, Felice Burn, Sebastian Schindera, Tician Schnitzler","doi":"10.1097/RLI.0000000000001122","DOIUrl":"10.1097/RLI.0000000000001122","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an artificial intelligence (AI) algorithm for iPE detection. Furthermore, we investigate the anatomic distribution patterns within missed iPE cases and mortality within a 90-day follow-up in patients before and after AI use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;This institutional review board-approved observational single-center study included 5298 chest computed tomography scans performed for reasons other than suspected pulmonary embolism (PE). We compared 2 cohorts: cohort 1, consisting of 1964 patients whose original radiology reports were generated before the implementation of an AI algorithm, and cohort 2, consisting of 3334 patients whose scans were analyzed after the implementation of an Food and Drug Administration-approved and CE-certified AI algorithm for iPE detection (Aidoc Medical, Tel Aviv, Israel). For both cohorts, any discrepancies between the original radiology reports and the AI results were reviewed by 2 thoracic imaging subspecialized radiologists. In the original radiology report and in case of discrepancies with the AI algorithm, the expert review served as reference standard. Sensitivity, specificity, prevalence, negative predictive value (NPV), and positive predictive value (PPV) were calculated. The rates of missed iPEs in both cohorts were compared statistically using STATA (Version 17.1). Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In cohort 1 (mean age 70.6 years, 48% female [n = 944], 52% male [n = 1020]), the prevalence of confirmed iPE was 2.2% (n = 42), and the AI detected 61 suspicious iPEs, resulting in a sensitivity of 95%, a specificity of 99%, a PPV of 69%, and an NPV of 99%. Radiologists missed 50% of iPE cases in cohort 1. In cohort 2 (mean age 69 years, 47% female [n = 1567], 53% male [n = 1767]), the prevalence of confirmed iPEs was 1.7% (56/3334), with AI detecting 59 suspicious cases (sensitivity 90%, specificity 99%, PPV 95%, NPV 99%). The rate of missed iPEs by radiologists dropped to 7.1% after AI implementation, showing a significant improvement ( P &lt; 0.001). Most overlooked iPEs (61%) were in the right lower lobe. The survival analysis showed no significantly decreased 90-day mortality rate, with a hazards ratio of 0.95 (95% confidence interval, 0.45-1.96; P = 0.88).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The implementation of an AI algorithm significantly reduced the rate of missed iPEs from 50% to 7.1%, thereby enhancing diagnostic accuracy. Despite this improvement, the 90-day mortality rate remained unchanged. These findings highlight the AI tool's potential to assist radiologists in accurately identifying iPEs, although its implementation does not s","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"260-266"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.","authors":"Lennart Walger, Tobias Bauer, David Kügler, Matthias H Schmitz, Fabiane Schuch, Christophe Arendt, Tobias Baumgartner, Johannes Birkenheier, Valeri Borger, Christoph Endler, Franziska Grau, Christian Immanuel, Markus Kölle, Patrick Kupczyk, Asadeh Lakghomi, Sarah Mackert, Elisabeth Neuhaus, Julia Nordsiek, Anna-Maria Odenthal, Karmele Olaciregui Dague, Laura Ostermann, Jan Pukropski, Attila Racz, Klaus von der Ropp, Frederic Carsten Schmeel, Felix Schrader, Aileen Sitter, Alexander Unruh-Pinheiro, Marilia Voigt, Martin Vychopen, Philip von Wedel, Randi von Wrede, Ulrike Attenberger, Hartmut Vatter, Alexandra Philipsen, Albert Becker, Martin Reuter, Elke Hattingen, Josemir W Sander, Alexander Radbruch, Rainer Surges, Theodor Rüber","doi":"10.1097/RLI.0000000000001125","DOIUrl":"10.1097/RLI.0000000000001125","url":null,"abstract":"<p><strong>Objectives: </strong>Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics.</p><p><strong>Materials and methods: </strong>We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models.</p><p><strong>Results: </strong>Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign ( P < 0.001) and cortical thickening ( P < 0.001). In contrast, AI models were sensitive to abnormal gyration ( P < 0.01) or gray-white matter blurring ( P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs ( P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) ( P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17).</p><p><strong>Conclusions: </strong>This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"253-259"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142500556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Catheter Verification: An Understandable AI Model for Efficient Assessment of Central Venous Catheter Placement in Chest Radiography.","authors":"Jonas Stroeder, Malte Multusch, Lennart Berkel, Lasse Hansen, Axel Saalbach, Heinrich Schulz, Mattias P Heinrich, Yannic Elser, Jörg Barkhausen, Malte Maria Sieren","doi":"10.1097/RLI.0000000000001126","DOIUrl":"10.1097/RLI.0000000000001126","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection.</p><p><strong>Materials and methods: </strong>The study utilized 2 datasets: the publicly accessible RANZCR CLiP dataset and a bespoke in-house dataset of 1006 annotated supine chest x-rays. Three deep learning models were trained: a classification network, a segmentation network, and a combination of both. These models were evaluated using receiver operating characteristic analysis, area under the curve, DICE similarity coefficient, and Hausdorff distance.</p><p><strong>Results: </strong>The combined model demonstrated superior performance with an area under the curve of 0.99 for correctly positioned CVCs and 0.95 for misplacements. The model maintained high efficacy even with reduced training data from the local dataset. Sensitivity and specificity rates were high, and the model effectively managed the segmentation and classification tasks, even in images with multiple CVCs and other support materials.</p><p><strong>Conclusions: </strong>This study illustrates the potential of AI-based models in accurately and reliably determining CVC placement in chest x-rays. The proposed method shows high accuracy and offers improved interpretability, important for clinical decision-making. The findings also highlight the importance of dataset quality and diversity in training AI models for medical image analysis.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"267-274"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.","authors":"Wei Zhou, Afrouz Ataei, Donglai Huo, Liqiang Ren, Lorna P Browne, Xin Zhou, Jason P Weinman","doi":"10.1097/RLI.0000000000001119","DOIUrl":"10.1097/RLI.0000000000001119","url":null,"abstract":"<p><strong>Purpose: </strong>The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit.</p><p><strong>Materials and methods: </strong>In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDI vol ) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options.</p><p><strong>Results: </strong>At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher ( P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher ( P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL.</p><p><strong>Conclusions: </strong>The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"245-252"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow.","authors":"Connor Krolak, Angela Wei, Marissa Shumaker, Manjiri Dighe, Michalakis Averkiou","doi":"10.1097/RLI.0000000000001127","DOIUrl":"10.1097/RLI.0000000000001127","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time ( RT ), mean transit time ( MTT ), peak intensity ( PI ), and area under the curve ( AUC ), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination ( R2 ) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT .&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit par","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"281-290"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142465523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epineural Scarring Visualization and Noninvasive Quantification of a Severe Posttraumatic Complication: An Experimental Magnetic Resonance Neurography Study.","authors":"Merle Brunnée, Martin Aman, Maximilian Mayrhofer-Schmid, Manuel Fischer, Simeon C Daeschler, Tess Klemm, Ulrich Kneser, Kianush Karimian-Jazi, Michael O Breckwoldt, Leila Harhaus, Sabine Heiland, Martin Bendszus, Arne H Boecker, Daniel Schwarz","doi":"10.1097/RLI.0000000000001132","DOIUrl":"10.1097/RLI.0000000000001132","url":null,"abstract":"<p><strong>Aim: </strong>Peripheral nerve scarring is a severe yet common complication following nerve injury or surgery that can lead to impaired nerve function, including chronic pain and sensory or motor deficits. In this study, we aimed to establish high-resolution magnetic resonance neurography (MRN) to accurately visualize and monitor de novo-formed epineural fibrotic adhesions (EFAs) of the sciatic nerve in a rat nerve injury model.</p><p><strong>Methods: </strong>Employing an established model to induce overshooting EFA, the study included 3 experimental groups of animals (n = 6 each): a positive control group (PC), an intervention group (IG), and a sham group. All groups underwent surgical nerve exposure: both PC and IG received an application of 10 μL 2.5% glutaraldehyde to induce EFA, but only IG received an additional preventive wrapping of the nerve with a collagen-containing matrix. Magnetic resonance imaging was performed 6, 8, and 12 weeks postoperatively using a standardized protocol including T2w and T1w without and with contrast media. Motor function and nerve regeneration was assessed using the visual static sciatic index. Histological specimens were obtained 12 weeks postoperatively and correlated with imaging.</p><p><strong>Results: </strong>On high-resolution MRN, prominently contrast-enhancing epineural sleeves were present in vivo, which corresponded to histologically confirmed EFA (ratio of EFA to nerve area MRN 1.512 ± 0.106 vs histological ratio 1.459 ± 0.208, nonsignificant). As expected, average EFA in IG (0.310 ± 0.118 mm 2 ) was smaller than in PC (0.909 ± 0.212 mm 2 , P < 0.01). Also, the average EFA in sham (0.386 ± 0.030 mm 2 ) was less pronounced than in PC ( P < 0.01). There was no significant difference in the average EFA between IG und sham. The EFA correlated with the functional outcome, which was measured by visual static sciatic index (correlation coefficient -0.59, P < 0.05).</p><p><strong>Conclusions: </strong>The results of the present study for the first time confirm the clinical observation that epineural thickening on contrast-enhanced T1w imaging following manipulation to a nerve indeed corresponds to overshooting epineural scarring, which may be linked to impaired nerve function. This can be followed noninvasively in vivo over time providing an important basis for clinical decision-making in cases where further invasive therapies may be necessary.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"275-280"},"PeriodicalIF":7.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging of Cardiovascular Implantable Electronic Device Leads With Photon-Counting Detector Computed Tomography.","authors":"Simon Schalla, Joachim E Wildberger, Mitch J F G Ramaekers, Casper Mihl, Michael C McDermott, Lion Stammen, Kevin Vernooy, Thomas G Flohr","doi":"10.1097/RLI.0000000000001177","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001177","url":null,"abstract":"<p><strong>Objectives: </strong>Computed tomography (CT) imaging of cardiovascular implantable electronic device (CIED) leads is currently hampered by large metal artifacts. Recently, photon-counting detector CT (PCD-CT) has been clinically introduced, offering high-resolution imaging with thin slice thicknesses and improved contrast-to-noise ratios. Suspected complications of CIED such as perforation, infection and venous obstruction could potentially be imaged with PCD-CT if metal artifacts were effectively reduced through adapted scan protocols and iterative metal artifact reduction algorithms (IMAR). The study evaluated the impact of various scan and reconstruction parameters, including different IMAR settings, on CIED lead visualization with PCD-CT in order to develop an optimized scan and reconstruction protocol for imaging leads.</p><p><strong>Materials and methods: </strong>Five different CIED leads were evaluated in a nonbeating heart phantom using a dual-source PCD-CT with electrocardiography-gated spectral standard resolution (collimation 144 × 0.4 mm) and nonspectral ultra-high resolution (UHR) mode (collimation 120 × 0.2 mm) spiral data acquisition. One scan was performed for each lead and each acquisition mode. Images were reconstructed with different slice thicknesses (0.2 mm, 0.4 mm, 0.6 mm), convolution kernels (Bv40, Bv44, Bv48, and Bv56), virtual monoenergetic energy levels (60-140 keV in steps of 10 keV), without and with different IMAR settings. The extent of metal artifacts was objectively evaluated using 4 different parameters. Additionally, 3 observers subjectively assessed image quality using a 5-point scale.</p><p><strong>Results: </strong>Metal artifacts increased with sharper kernels and higher keV levels in virtual monoenergetic reconstructions. The artifacts were not dependent on slice thickness. No significant differences in metal artifacts were observed between UHR and standard-resolution scans when using similar reconstruction parameters. IMAR effectively reduced artifacts across all kernels, slice thicknesses, and keV levels, with the \"neuro coils\" setting showing the best performance. Subjective analysis of image quality revealed that thinnest slices and sharpest kernels (0.2 mm, Bv56) allowed for better delineation of fine structures, such as the shape of helices, while reconstructions with thicker slices and softer kernels (0.6 mm, Bv40) were preferred for visualizing general lead appearance and adjacent anatomical structures.</p><p><strong>Conclusions: </strong>Ultra-high and standard resolution PCD-CT with IMAR enables good-quality imaging of CIED leads, showing even small details without compromising the visibility of nearby structures. A dedicated acquisition and reconstruction protocol comprising an UHR scan with 2 reconstructions (0.2 mm/Bv56 and 0.6 mm/Bv40, using IMAR) appears optimal for PCD-CT imaging of CIED leads.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Every Drop (Photon) Counts: Current Applications and Future Challenges of Photon-Counting Detector CT in Abdominal Imaging.","authors":"Domenico De Santis, Antonella Del Gaudio, Curzio Santangeli, Federica Fanelli, Fiammetta Pacelli, Lucrezia Capece Minutolo Del Sasso, Marta Zerunian, Michela Polici, Tiziano Polidori, Francesco Pucciarelli, Daniele Marin, Andrea Laghi, Damiano Caruso","doi":"10.1097/RLI.0000000000001178","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001178","url":null,"abstract":"<p><strong>Abstract: </strong>Photon-counting detector computed tomography (PCD-CT) is a breakthrough innovation over conventional single-energy and dual-energy CT equipped with energy-integrating detectors (EID). Because of increased spatial resolution and improved material differentiation, PCD-CT aims at improving the diagnosis of various abdominal conditions. This technology offers several advantages over EID-based CT scanners, including higher spatial and contrast resolution, reduced electronic noise, and low radiation dose exposure. Additionally, because spectral information is generated within the detectors, PCD-CT offers the possibility of routine spectral examinations and refines material decomposition through available multienergy imaging, further enhancing tissue characterization and image contrast. With most scientific literature focused on cardiovascular applications, abdominal imaging is an open field for technical and clinical research in PCD-CT. This review aims to provide a general overview of the technical principles of PCD-CT, its applications in abdominal imaging, and to summarize the main literature findings of its clinical applications in the liver, pancreas, adrenals, genitourinary system, bowel, peritoneum, and abdominal vessels. We will also highlight the pros and cons observed in clinical practice and offer insights into potential future developments of PCD-CT in abdominal imaging.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}