European Radiology Experimental最新文献

{"title":"CLINTERVENTIONAL protocol: a randomized controlled trial to evaluate clinical consultations and audiovisual tools for interventional radiology.","authors":"Pedro Blas García Jurado, Juan José Espejo Herrero, María Sagrario Lombardo Galera, María Eugenia Pérez Montilla, Sara Barranco Acosta, José García-Revillo, Pilar Font Ugalde, Marina Álvarez Benito","doi":"10.1186/s41747-024-00545-y","DOIUrl":"10.1186/s41747-024-00545-y","url":null,"abstract":"<p><p>Interventional radiology (IR) has evolved rapidly, but the clinical integration of interventional radiologists has not kept pace with technical advancements. This trial will address a gap in the literature by providing a robust investigation into specific measures for enhancing the clinical role of interventional radiologists, with potential implications for improving patient experiences and outcomes. The single-center randomized controlled trial will include 428 patients undergoing IR procedures. The control group will receive information about the procedure from the ordering physician, while the experimental group will have an additional consultation with an interventional radiologist and be shown procedure-specific explanatory videos. The primary outcomes are patients' knowledge, satisfaction with the information and communication, and anxiety. Data collection will involve specific questionnaires and scales. This trial is designed to investigate the importance of proactive clinical roles in patient care within IR. The study explores the potential of consultations and audiovisual tools, highlighting their role in educating patients about procedures. The results may help foster a more widespread acceptance of clinical responsibilities in IR and underscore the pivotal role of audiovisual aids in patient education and satisfaction.</p><p><strong>Trial registration: </strong>NCT05461482 at clinicaltrials.gov.</p><p><strong>Relevance statement: </strong>This randomized controlled trial will assess the impact of clinical consultations and explanatory audiovisual tools on patient understanding, satisfaction, and anxiety in interventional radiology. The findings could help establish a more proactive clinical role for interventional radiologists and improve the overall quality of patient-centered care.</p><p><strong>Key points: </strong>We describe the protocol of an interventional radiology randomized clinical trial. The control group will receive procedure information from the referring physician and the experimental group receives additional consultation with interventionalists and views a video. Knowledge, satisfaction with information, and patient anxiety will be evaluated. This study will provide insights about the benefits of consultations and videos in interventional radiology.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"6"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985079","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":"Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?","authors":"Brendan S Kelly, Sophie Duignan, Prateek Mathur, Henry Dillon, Edward H Lee, Kristen W Yeom, Pearse A Keane, Aonghus Lawlor, Ronan P Killeen","doi":"10.1186/s41747-024-00547-w","DOIUrl":"10.1186/s41747-024-00547-w","url":null,"abstract":"<p><strong>Background: </strong>The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).</p><p><strong>Methods: </strong>Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression. Its performance was compared to pretrained U-Net and ViT models. Accuracy was the primary evaluation metric and 95% confidence interval (CIs) were calculated by bootstrapping. We included 170 patients with MS (50 males, 120 females), aged 21-74 years (mean 42.3), imaged at a single institution from 2019 to 2021, each with 2-5 MRI studies (496 in total).</p><p><strong>Results: </strong>One hundred seventy patients were included, 110 for training, 30 for tuning, and 30 for testing; 100 unseen paired images were randomly selected from the test set for evaluation. Both U-Net and ViT had 94% (95% CI: 89-98%) accuracy while GPT4V had 85% (77-91%). GPT4V gave cautious nonanswers in six cases. GPT4V had precision (specificity), recall (sensitivity), and F1 score of 89% (75-93%), 92% (82-98%), 91 (82-97%) compared to 100% (100-100%), 88 (78-96%), and 0.94 (88-98%) for U-Net and 94% (87-100%), 94 (88-100%), and 94 (89-98%) for ViT.</p><p><strong>Conclusion: </strong>The performance of GPT4V combined with its accessibility suggests has the potential to impact AI radiology research. However, misclassified cases and overly cautious non-answers confirm that it is not yet ready for clinical use.</p><p><strong>Relevance statement: </strong>GPT4V can identify the radiologic progression of MS in a simplified experimental setting. However, GPT4V is not a medical device, and its widespread availability highlights the need for caution and education for lay users, especially those with limited access to expert healthcare.</p><p><strong>Key points: </strong>Without fine-tuning or the need for prior coding experience, GPT4V can perform a zero-shot radiologic change detection task with reasonable accuracy. However, in absolute terms, in a simplified \"spot the difference\" medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V's performance metrics were more similar to the ViT than the U-net. This is an exploratory experimental study and GPT4V is not intended for use as a medical device.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"9"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985076","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":"Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!","authors":"Ioanna Chouvarda, Sara Colantonio, Ana S C Verde, Ana Jimenez-Pastor, Leonor Cerdá-Alberich, Yannick Metz, Lithin Zacharias, Shereen Nabhani-Gebara, Maciej Bobowicz, Gianna Tsakou, Karim Lekadir, Manolis Tsiknakis, Luis Martí-Bonmati, Nikolaos Papanikolaou","doi":"10.1186/s41747-024-00543-0","DOIUrl":"10.1186/s41747-024-00543-0","url":null,"abstract":"<p><p>Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging. A total of 49 responses were obtained and analysed to identify trends and patterns. While TGs valued transparency and traceability the most, CGs pointed out the importance of explainability. Among the topics where TGs may benefit from further exposure are stability and robustness checks, and mitigation of fairness issues. On the other hand, CGs seemed more reluctant towards synthetic data for validation and would benefit from exposure to cross-validation techniques, or segmentation metrics. Topics emerging from the open questions were utility, capability, adoption and trustworthiness. These findings on current trends in AI validation strategies may guide the creation of guidelines necessary for training the next generation of professionals working with AI in healthcare and contribute to bridging any technical-clinical gap in AI validation. RELEVANCE STATEMENT: This study recognised current gaps in understanding and applying AI validation strategies in cancer imaging and helped promote trust and adoption for interdisciplinary teams of technical and clinical researchers. KEY POINTS: Clinical and technical researchers emphasise interpretability, external validation with diverse data, and bias awareness in AI validation for cancer imaging. In cancer imaging AI research, clinical researchers prioritise explainability, while technical researchers focus on transparency and traceability, and see potential in synthetic datasets. Researchers advocate for greater homogenisation of AI validation practices in cancer imaging.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"7"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985088","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 of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.","authors":"Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li","doi":"10.1186/s41747-024-00535-0","DOIUrl":"10.1186/s41747-024-00535-0","url":null,"abstract":"<p><strong>Background: </strong>Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.</p><p><strong>Methods: </strong>In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018-06/2023). The training cohort comprised 344 patients from five centers, and the test cohort included 101 patients from the sixth center. A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. Comparison with existing clinical scores (MSS, SEDAN, GRASPS) was performed using the DeLong test.</p><p><strong>Results: </strong>Of the 445 individuals, 202 (45.4%) had HT, 79 (17.8%) had hemorrhagic infarction, and 123 (27.6%) had PH. In the test cohort, the area under the receiver operating characteristic curve (AUROC) of the clinical, imaging, and ensemble model for HT prediction was 0.877, 0.920, and 0.937, respectively. The ensemble model for HT prediction outperformed MSS, SEDAN, and GRASPS scores (p ≤ 0.023). The ensemble model predicted PH and PH-2 with AUROC of 0.858 and 0.806, respectively.</p><p><strong>Conclusion: </strong>Developing and validating an integrated model that can predict HT and its subtypes in AIS patients following IVT based on NCCT and clinical data is feasible.</p><p><strong>Relevance statement: </strong>The clinical, imaging, and ensemble models based on noncontrast CT and clinical data outperformed existing clinical scores in predicting hemorrhagic transformation of AIS and its subtypes with poor prognosis, facilitating personalized treatment decisions.</p><p><strong>Key points: </strong>The models demonstrated the capability to predict hemorrhagic transformation of acute ischemic stroke quickly, accurately, and reliably. The proposed models outperformed existing clinical scores in predicting hemorrhagic transformation. The ensemble model provided risk assessment of parenchymal hemorrhage and parenchymal hemorrhage-2 outperforming existing clinical scores.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"8"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985081","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":"Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI.","authors":"Vaanathi Sundaresan, Giovanna Zamboni, Robert A Dineen, Dorothee P Auer, Stamatios N Sotiropoulos, Nikola Sprigg, Mark Jenkinson, Ludovica Griffanti","doi":"10.1186/s41747-024-00544-z","DOIUrl":"10.1186/s41747-024-00544-z","url":null,"abstract":"<p><p>Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute-MNI space are publicly available. RELEVANCE STATEMENT: Our method to automatically characterise cerebral microbleeds (size and location) showed a mean absolute error of 2.5 mm for size estimation and an over 90% accuracy for rating of infratentorial, deep and lobar regions. This is a promising approach to automatically provide clinically relevant cerebral microbleeds metrics. KEY POINTS: We present a method to automatically characterise cerebral microbleeds according to size and location. The method achieved a mean absolute error of 2.5 mm for size estimation. Automated rating for infratentorial, deep, and lobar regions achieved an over 90% overall accuracy. We made the code and atlas of Microbleed Anatomical Rating Scale regions publicly available.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"5"},"PeriodicalIF":3.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972493","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":"Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases.","authors":"Benjamin Böttcher, Marly van Assen, Roberto Fari, Philipp L von Knebel Doeberitz, Eun Young Kim, Eugene A Berkowitz, Felix G Meinel, Carlo N De Cecco","doi":"10.1186/s41747-024-00539-w","DOIUrl":"10.1186/s41747-024-00539-w","url":null,"abstract":"<p><strong>Background: </strong>This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).</p><p><strong>Methods: </strong>Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions. Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. Dataset 2 was used for sessions B and D with optional CBIR use, assessing time spending and frequency of CBIR usage. Accuracy was assessed on the CT pattern level, comparing readers' diagnoses with reference diagnoses and CBIR results with region-of-interest (ROI) patterns.</p><p><strong>Results: </strong>Diagnostic accuracy and confidence of readers showed an increasing trend with CBIR use compared to no CBIR use (53.6% versus 35.7% and 50.0% versus 32.2%, respectively). Time for reading significantly decreased in both datasets (A versus C: 104 s versus 54 s, p < 0.001; B versus D: 88.5 s versus 70 s, p = 0.009), whereas time for research increased with CBIR software use (A versus C: 31 s versus 81 s, p = 0.040). CBIR results showed a high pattern-based accuracy of overall 73.4%. Comparison between readers indicates a slightly higher accuracy of CBIR results when more than one ROI was used as input (77.7% versus 70.1%).</p><p><strong>Conclusion: </strong>CBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment.</p><p><strong>Relevance statement: </strong>Content-based image retrieval software improves the assessment of interstitial lung diseases (ILD) in high-resolution CT, especially for radiology residents, by increasing diagnostic accuracy and confidence while reducing interpretation time. This can provide educational benefits and more time-efficient management of complex cases.</p><p><strong>Key points: </strong>A content-based image retrieval (CBIR) software improves diagnostic accuracy and confidence for in-training radiologists for interstitial lung disease (ILD) assessment on computed tomography (CT). A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment. CBIR algorithms benefit from the input of multiple regions of interest per ILD case.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"4"},"PeriodicalIF":3.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972494","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":"Long-term effects of linear versus macrocyclic GBCAs on gene expression in the central nervous system of mice.","authors":"Chuanbing Wang, Yuxia Tang, Jiajia Tang, Jie Zhang, Siqi Wang, Feiyun Wu, Shouju Wang","doi":"10.1186/s41747-024-00546-x","DOIUrl":"10.1186/s41747-024-00546-x","url":null,"abstract":"<p><strong>Background: </strong>We examined chronic gadolinium retention impact on gene expression in the mouse central nervous system (CNS) after injection of linear or macrocyclic gadolinium-based contrast agents (GBCAs).</p><p><strong>Methods: </strong>From 05/2022 to 07/2023, 36 female mice underwent weekly intraperitoneal injections of gadodiamide (2.5 mmol/kg, linear), gadobutrol (2.5 mmol/kg, macrocyclic), or saline. Mice were sacrificed on day 29 or 391 after a 1-year washout. Assessments included magnetic resonance imaging (MRI), mechanical hyperalgesia tests, and inductively coupled plasma mass spectrometry to measure gadolinium levels. Ribonucleic acid (RNA) sequencing and bioinformatic analyses identified differentially expressed genes (DEGs), with validation by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot (WB).</p><p><strong>Results: </strong>Post-gadodiamide, MRI showed increased signal intensity in the deep cerebellar nuclei (pre, 0.997 ± 0.006 versus post, 1.086 ± 0.013, p < 0.001). Mechanical hyperalgesia tests indicated transient sensory changes. After 1-year, gadolinium retention was noted in the brain (5.92 ± 0.32 nmol/kg) and spinal cord (1.23 ± 0.66 nmol/kg) with gadodiamide, compared to saline controls (0.06 ± 0.02 nmol/kg in brains and 0.28 ± 0.06 nmol/kg in spinal cords). RNA sequencing identified 17 shared DEGs between brain and spinal cord in the gadodiamide group on day 391, with altered Hmgb2 and Sgk1 expression confirmed by qRT-PCR and WB. Reactome pathway analysis showed enrichment in neuroinflammation pathways. No DEGs were detected in brains on day 29.</p><p><strong>Conclusion: </strong>Chronic gadolinium deposition from repeated linear GBCA but not macrocyclic administration causes significant gene expression alterations in the mouse CNS, particularly affecting neuroinflammation pathways.</p><p><strong>Relevance statement: </strong>This study examined the long-term impact of chronic gadolinium retention on gene expression in the mouse CNS, uncovering significant changes associated with neuroinflammation pathways after repeated administration of linear GBCA, but not with macrocyclic GBCA. These findings highlight the importance of further research on the long-term safety of linear GBCA in medical imaging.</p><p><strong>Key points: </strong>Chronic gadolinium retention alters gene expression in the mouse central nervous system. Significant neuroinflammatory pathway changes were observed after linear gadodiamide exposure. MRI showed increased signal intensity in deep cerebellar nuclei after gadodiamide injection.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"3"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11723877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956401","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":"Comparison of CT-like MRI sequences for preoperative planning of cochlear implantation using super-high-resolution CT as a reference.","authors":"Ulysse Puel, Achille Beysang, Gabriella Hossu, Michael Eliezer, Bouchra Assabah, Khalid Ambarki, Pedro Augusto Gondim Teixeira, Alain Blum, Cécile Parietti-Winkler, Romain Gillet","doi":"10.1186/s41747-024-00538-x","DOIUrl":"10.1186/s41747-024-00538-x","url":null,"abstract":"<p><strong>Background: </strong>We evaluated the accuracy of magnetic resonance imaging (MRI) computed tomography (CT)-like sequences compared to normal-resolution CT (NR-CT) and super-high-resolution CT (SHR-CT) for planning of cochlear implantation.</p><p><strong>Methods: </strong>Six cadaveric temporal bone specimens were used. 3-T MRI scans were performed using radial volumetric interpolated breath-hold (STARVIBE), pointwise-encoding time reduction with radial acquisition (PETRA), and ultrashort time of echo (UTE) sequences. CT scans were performed on two scanners for SHR-CT and NR-CT acquisitions. Two radiologists evaluated accuracy based on preimplantation metrics and the ability to identify various anatomical structures, particularly the facial recess and round window. Wilcoxon rank-sum test and intraclass correlation coefficient (ICC) were used.</p><p><strong>Results: </strong>The facial nerve was always clearly visible (score ≥ 2) in the MRI, NR-CT, and SHR-CT scans (p ≥ 0.621). However, the chorda tympani nerve (CTN) was clearly visualized in UTE, STARVIBE, and PETRA sequences in only 33% (2/6 specimens, p = 0.016), 50% (3/6 specimens, p = 0.038), and 83% (5/6 specimens, p = 0.017) of cases, respectively, whereas it was always clearly visualized in SHR and NR-CT (p = 0.426). The round window (RW) was never visualized in MRI sequences (p ≤ 0.010), whereas it was identified in all cases in SHR and NR-CT (p = 1.000). There was a strong correlation between measurements obtained from MRI and CT modalities (ICC ≥ 0.837).</p><p><strong>Conclusion: </strong>MRI CT-like sequences assessed the facial nerve in all cases and the CTN in up to 87% of cases. However, the detection of the RW was insufficient for surgical planning. CT and MRI measurements were in agreement.</p><p><strong>Relevance statement: </strong>CT-like MRI sequences can image the anatomy of the facial recess and the length of the basal turn of the cochlea with similar accuracy as conventional CT, although they cannot image the round window.</p><p><strong>Key points: </strong>CT-like MRI sequences are not widely used in preoperative cochlear implantation imaging. CT-like sequences can image the facial recess as well as conventional CT. CT-like sequences can image the basal turn length of the cochlea as well as conventional CT. Round window depiction is not possible with CT-like MRI sequences.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"1"},"PeriodicalIF":3.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923724","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":"Voxelwise characterization of noise for a clinical photon-counting CT scanner with a model-based iterative reconstruction algorithm.","authors":"Luigi Masturzo, Patrizio Barca, Luca De Masi, Daniela Marfisi, Antonio Traino, Filippo Cademartiri, Marco Giannelli","doi":"10.1186/s41747-024-00541-2","DOIUrl":"10.1186/s41747-024-00541-2","url":null,"abstract":"<p><strong>Background: </strong>Photon-counting detector (PCD) technology has the potential to reduce noise in computed tomography (CT). This study aimed to carry out a voxelwise noise characterization for a clinical PCD-CT scanner with a model-based iterative reconstruction algorithm (QIR).</p><p><strong>Methods: </strong>Forty repeated axial acquisitions (tube voltage 120 kV, tube load 200 mAs, slice thickness 0.4 mm) of a homogeneous water phantom and CTP404 module (Catphan-504) were performed. Water phantom acquisitions were also performed on a conventional energy-integrating detector (EID) scanner with a sinogram/image-based iterative reconstruction algorithm, using similar acquisition/reconstruction parameters. For smooth/sharp kernels, filtered back projection (FBP)- and iterative-reconstructed images were obtained. Noise maps, non-uniformity index (NUI) of noise maps, image noise histograms, and noise power spectrum (NPS) curves were computed.</p><p><strong>Results: </strong>For FBP-reconstructed images of water phantom, mean noise was (smooth/sharp kernel) 11.7 HU/51.1 HU and 18.3 HU/80.1 HU for PCD-scanner and EID-scanner, respectively, with NUI values for PCD-scanner less than half those for EID-scanner. Percentage noise reduction increased with increasing iterative power, up to (smooth/sharp kernel) 57.7%/72.5% and 56.3%/70.1% for PCD-scanner and EID-scanner, respectively. For PCD-scanner, FBP- and QIR-reconstructed images featured an almost Gaussian distribution of noise values, whose shape did not appreciably vary with iterative power. Noise maps of CTP404 module showed increased NUI values with increasing iterative power, up to (smooth/sharp kernel) 15.7%/9.2%. QIR-reconstructed images showed limited low-frequency shift of NPS peak frequency.</p><p><strong>Conclusion: </strong>PCD-CT allowed appreciably reducing image noise while improving its spatial uniformity. QIR algorithm decreases image noise without modifying its histogram distribution shape, and partly preserving noise texture.</p><p><strong>Relevance statement: </strong>This phantom study corroborates the capability of photon-counting detector technology in appreciably reducing CT imaging noise and improving spatial uniformity of noise values, yielding a potential reduction of radiation exposure, though this needs to be assessed in more detail.</p><p><strong>Key points: </strong>First voxelwise characterization of noise for a clinical CT scanner with photon-counting detector technology. Photon-counting detector technology has the capability to appreciably reduce CT imaging noise and improve spatial uniformity of noise values. In photon-counting CT, a model-based iterative reconstruction algorithm (QIR) allows decreasing effectively image noise. This is done without modifying noise histogram distribution shape, while limiting the low-frequency shift of noise power spectrum peak frequency.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"2"},"PeriodicalIF":3.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923727","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":"DSA-based perfusion parameters versus TICI score after mechanical thrombectomy in acute ischaemic stroke patients: a congruence analysis.","authors":"Sebastian R Reder, Andrea Kronfeld, Sonja Gröschel, Arda Civelek, Klaus Gröschel, Marc A Brockmann, Timo Uphaus, Marianne Hahn, Carolin Brockmann, Ahmed E Othman","doi":"10.1186/s41747-024-00534-1","DOIUrl":"10.1186/s41747-024-00534-1","url":null,"abstract":"<p><strong>Background: </strong>Several factors are frequently considered for outcome prediction rin stroke patients. We assessed the value of digital subtraction angiography (DSA)-based brain perfusion measurements after mechanical thrombectomy (MT) for outcome prediction in acute ischaemic stroke.</p><p><strong>Methods: </strong>From DSA image data (n = 90; 38 females; age 73.3 ± 13.1 years [mean ± standard deviation]), time-contrast agent (CA) concentration curves were acquired, and maximum slope (MS), time to peak (TTP), and maximum CA concentration (CA_max) were calculated using an arterial input function. This data was used to predict neurological deficits at 24 h and upon discharge by using multiple regression analysis; the predictive capability was compared with the predictive power of the \"Thrombolysis in cerebral infarction\" (TICI) score. Intraclass correlation coefficients (ICC) of the NIHSS values were analysed.</p><p><strong>Results: </strong>The comparison of means revealed a linear trend after stratification into TICI classes for CA_max (TICI 0: 0.07 ± 0.02 a.u. to TICI 3: 0.22 ± 0.07 a.u.; p < 0.001), and for MS (TICI 0: 0.04 ± 0.01 a.u./s to TICI 3: 0.12 ± 0.0  a.u./s; p < 0.001). Regression analyses demonstrated equivalent capabilities for estimating neurological deficits after 24 h and at discharge using both the TICI score and DSA-based perfusion parameters (ΔR² ~ 0.03). Compared to the actual NIHSS, the ICC ranged from 0.55 to 0.84 for DSA-based models and from 0.6 to 0.82 for TICI-based models.</p><p><strong>Conclusion: </strong>Semi-quantitative evaluation of DSA-based perfusion parameters prior to and after MT is feasible and could enhance the objectivity and comparability of MT outcome prediction. This technique may offer novel approaches in acute ischaemic stroke management and data comparability.</p><p><strong>Relevance statement: </strong>DSA-based brain perfusion measurements following interventional stroke therapy could allow for an experience-independent assessment of reperfusion success. It demonstrates predictive power at least equivalent to the established methods. This could support a future automated DSA-based brain perfusion measurement method.</p><p><strong>Key points: </strong>Currently, the evaluation of stroke therapy success is based on the treating physician's experience. The present study introduces an objective semi-quantitative evaluation method. In predicting clinical outcomes, the traditional expert-based and semi-quantitative methods are equivalent.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"136"},"PeriodicalIF":3.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787339","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}