Clinical Neuroradiology最新文献

Background: Tirofiban is administered for the treatment of aneurysms in cases of thromboembolic complications, as well as in cases of acute stenting or flow-diverter implantation required within the scope of aneurysm treatment. We aimed to investigate the efficacy and safety of tirofiban in this group of patients.

Methods: We conducted a retrospective analysis of all patients undergoing aneurysm treatment and receiving peri-interventional tirofiban administration at our institution between 2009 and 2019.

Results: A total of 105 patients were included, with 61% women and 39% men (mean age = 53 years, IQR: 44-60 years). Sixty-seven patients underwent emergency aneurysm treatment, and thirty-eight were treated electively. Hemorrhagic events occurred in 22% (15/67) of the patients treated acutely, with 7.46% (5/67) exhibiting symptoms. Patients undergoing elective aneurysm treatment experienced no hemorrhagic events (p = 0.002). Among the 35 patients who required an external ventricular drain (EVD), 22.86% (8/35) developed EVD-related hemorrhages; however, none were symptomatic (p = 0.007). Of the five patients who required a craniotomy, two experienced significant bleeding, and one experienced non-significant craniotomy-related bleeding (p = 0.20).

Conclusion: Tirofiban may be safe for use during peri-interventional complications or emergency stenting in aneurysm treatment. However, caution is necessary when craniotomy is required. In elective aneurysm treatments, administering Tirofiban in response to periprocedural complications appears to be safe.

Purpose: Myocardial injury, indicated by an elevation of high-sensitive cardiac Troponin (hs-cTnT), is a frequent stroke-related complication. Most studies investigated patients with ischemic stroke, but only little is known about its occurrence in patients with intracerebral hemorrhage (ICH). This study aimed to assess the frequency, predictors, and implications of myocardial injury in ICH patients.

Methods: Our retrospective analysis included 322 ICH patients. We defined myocardial injury as an elevation of hs-cTnT above the 99th percentile (i.e. 14 ng/L). Acute myocardial injury was defined as either a changing pattern of > 50% within 24 h or an excessive elevation of initial hs-cTnT (> 52 ng/L). 3D brain scans were assessed for ICH visually and quantitatively by a deep learning algorithm. Multiple regression models and Voxel-based Lesion-Symptom Mapping (VLSM) were applied.

Results: 63.0% (203/322) of patients presented with myocardial injury, which was associated with more severe strokes and worse outcomes during the in-hospital phase (P < 0.01). Acute myocardial injury occurred in 24.5% (79/322) of patients. The only imaging finding associated with acute myocardial injury was midline shift (69.8% vs. 44.6% for normal or stable hs-cTnT, P < 0.01), which also independently predicted it (odds ratio 3.29, confidence interval 1.38-7.87, P < 0.01). In contrast, VLSM did not identify any specific brain region significantly associated with acute myocardial injury. Acute myocardial injury did not correlate with preexisting cardiac diseases; however, the frequency of adverse cardiac events was higher in the acute myocardial injury group (11.4% vs. 4.1% in patients with normal and/or stable patterns of hs-cTnT, P < 0.05).

Conclusion: Myocardial injury occurs frequently in ICH and is linked to poor outcomes. Acute myocardial injury primarily correlates to space-occupying effects of ICH but is less dependent on premorbid cardiac status. Nonetheless, it is associated with a higher rate of adverse cardiac events.

Background: Hypertension (HTN) is a prevalent cardiovascular condition associated with cognitive impairments, including memory deficits and attention lapses. Understanding the neural mechanisms underlying HTN-related cognitive dysfunction is crucial for optimizing treatment strategies.

Method: A systematic review was conducted to explore the impact of antihypertensive medications on cognition, focusing on memory, attention, and emotion processing using functional magnetic resonance imaging (fMRI). Searches were performed in PubMed and Scopus up to March 10, 2024, with no language restrictions.

Results: A total of 108 articles were identified, of which 12 systematic reviews and meta-analyses met the inclusion criteria. Included studies investigated various antihypertensive drugs, including losartan, propranolol, spironolactone, and telmisartan, and their effects on cognitive processes. Losartan improved negative memory encoding and facilitated fear extinction via hippocampal and prefrontal modulation. Propranolol disrupted fear reconsolidation and reduced emotional memory retrieval, affecting the amygdala and hippocampus. Spironolactone prevented stress-induced memory shifts in the amygdala. Findings indicated distinct impacts of these medications on memory encoding, fear extinction, and stress-induced memory modulation, as evidenced by alterations in neural activity patterns observed on fMRI.

Conclusion: Antihypertensive medications, such as losartan and propranolol, demonstrate potential in modulating memory, fear-related memory reconsolidation, and stress-induced memory modulation, highlighting their therapeutic implications for conditions like posttraumatic stress disorder (PTSD) and anxiety disorders. This review underscores the importance of fMRI studies in elucidating the neural correlates of HTN-related cognitive impairments and optimizing treatment approaches.

Purpose: Magnetic Resonance Imaging based brain segmentation and volumetry has become an important tool in clinical routine and research. However the impact of the used hardware is only barely investigated. This study aims to assess the influence of scanner manufacturer, field strength and head-coil on volumetry results.

Methods: 10 healthy subjects (27.4 ± 1.71 years) were prospectively examined in a Philips Achieva 1.5T, Philips Ingenia CX 3T, Siemens MAGNETOM Aera 1.5T and Siemens MAGNETOM Vida 3T, the latter equipped with three different head coils, within one day. Brain volumetry of the whole brain, total white and grey matter, the cortical grey matter of the supratentorial lobes as well as regions important for the differentiation of neurodegenerative diseases of the dementia and movement disorder spectrum and the ventricular system was performed using the CE-certified software mdbrain by mediaire (Berlin, Germany). Both raw volumetry results and percentile allocation provided by the software were analysed.

Results: This study reveals significantly different volumetry results for all examined brain regions beside the ventricular system between the different MRI devices but comparable results between the different head coils. When examining the percentile allocation provided by used software, the Intraclass-Correlation-Coefficient (ICC) values were even lower than the raw volume ICC values ranging from poor to excellent correlation.

Conclusion: The present study reveals highly relevant results that need to be considered both in clinical routine when analysing follow-up examinations from different scanner types and clinical research, especially when planning longitudinal and/or multicentre studies.

Background: Paraclinoid aneurysms, arising from the proximal dural ring and extending to the origin of the posterior communicating artery of the internal carotid artery (ICA), represent a significant proportion of all intracranial aneurysms (IAs). Accurate prediction of the rupture risk of paraclinoid aneurysms is crucial for optimal management. The objective of this study was to identify risk factors for the rupture of paraclinoid aneurysms on the basis of computer-assisted semiautomated measurement (CASAM) and hemodynamics.

Methods: The clinical, demographic and radiological data of the 304 paraclinoid aneurysms (285 unruptured and 19 ruptured) included were extracted from the Chinese Intracranial Aneurysm Project (CIAP) database. Morphological parameters were quantified via CASAM, and hemodynamic simulations were performed via computational fluid dynamics (CFD). Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for aneurysm rupture.

Results: The mean age of the patients was 56.91 ± 11.0 years, with a female predominance (71.7%). Univariate analysis revealed that the undulation index (UI) and nonsphericity index (NSI) were significantly greater in ruptured paraclinoid aneurysms than in unruptured aneurysms. The proportion of ruptured paraclinoid aneurysms located laterally on the ICA was significantly lower than that of those located anteriorly (p = 0.002). Multivariate logistic regression analysis revealed that a greater UI (OR = 1.086, 95% CI 1.012-1.165; p = 0.022) and larger low shear area (LSA) (OR = 1.034, 95% CI 1.004-1.064; p = 0.028) were independent risk factors for rupture.

Conclusions: Our findings indicate that a greater UI and a larger LSA are independent risk factors for the rupture of paraclinoid aneurysms. Compared with aneurysms in other orientations, paraclinoid aneurysms located anteriorly to the ICA are more prone to rupture. These findings may be useful in developing more consummate predictive models to enhance the management and surveillance of paraclinoid aneurysms in the future, leading to improved clinical decision-making and better patient outcomes.

Introduction: Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models. The aim of this work was to investigate whether AI, in particular deep learning, allows the identification of VPS models in cranial X‑rays.

Methods: 959 cranial X‑rays of patients with a VPS were included and reviewed for image quality and complete visualization of VPS valves. The images included four VPS model types: Codman Hakim (n = 774, 81%), Codman Certas Plus (n = 117, 12%), Sophysa Sophy Mini SM8 (n = 35, 4%) and proGAV 2.0 (n = 33, 3%). A Convolutional Neural Network (CNN) was trained using stratified five-fold cross-validation to classify the four VPS model types in the dataset. A finetuned CNN pretrained on the ImageNet dataset as well as a model trained from scratch were compared. The averaged performance and uncertainty metrics were evaluated across the cross-validation splits.

Results: The fine-tuned model identified VPS valve models with a mean accuracy of 0.98 ± 0.01, macro-averaged F1 score of 0.93 ± 0.04, a recall of 0.94 ± 0.03 and a precision of 0.95 ± 0.08 across the five cross-validation splits.

Conclusion: Automatic classification of VPS valve models in skull X‑rays, using fully automatable preprocessing steps and a CNN, is feasible. This is an encouraging finding to further explore the possibility of automating VPS valve model identification and pressure level reading in skull X‑rays.

Introduction: Excessive daytime sleepiness (EDS), a prevalent non-motor symptom in Parkinson's disease (PD), significantly impacts the quality of life for PD patients and elevates the risks of injury. Our study is to investigate the altered cortical surface morphology characteristics in PD patients with EDS (PD-EDS).

Methods: Clinical data and magnetic resonance imaging were obtained from the Parkinson's Progression Marker Initiative database, comprising 36 PD-EDS and 98 PD patients without EDS (PD-nEDS). The computational anatomy toolbox was utilized to derive sulcus depth (SD) and deep grey matter (GM) nuclei volumes.

Results: PD-EDS patients exhibited significantly decreased SD values in the right caudal middle frontal gyrus, pars opercularis, and superior temporal cortex relative to PD-nEDS patients. However, no significant differences in deep GM nuclei volumes were identified. Receiver operating characteristic (ROC) curve analyses further revealed that these cortical SD values could potentially serve as a screening index for distinguishing PD-EDS from PD-nEDS. Additionally, although PD-EDS patients had a longer disease duration and poorer performance in motor function and depression compared to PD-nEDS patients, these factors were included as covariates in the neuroimaging analyses.

Conclusion: Our study findings demonstrated that decreased cortical SD values might induce sleep-wake state instability and contribute to the pathophysiological mechanisms of EDS in early-stage PD.