Clinical Neuroradiology最新文献

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.

Purpose: To compare the diagnostic accuracy of CT angiography (CTA), MR angiography (MRA), and their combined use for detecting unruptured intracranial aneurysms (UIAs).

Methods: Between September 2019 and August 2023, 235 patients suspected of having UIA underwent CTA, MRA, and digital subtraction angiography (DSA)/3-dimensional rotational angiography (3DRA). Two neuroradiologists retrospectively reviewed these images for UIA presence. The value of combining modalities was assessed using confidence rating scores for each. The sensitivity, specificity, and accuracy of these modalities were calculated on a per-aneurysm basis and compared using DSA/3DRA as the reference standard. Subgroup analyses were performed based on aneurysm size (≤ 3 or > 3 mm).

Results: DSA/3DRA detected 252 UIAs in 182 patients, no aneurysms detected in 53 (mean age: 61.9 years ±11.6, 83 men). The overall sensitivity/specificity/accuracy of the combined analysis of CTA and MRA were 91.3%/88.7%/90.7%, respectively, which were significantly higher than those of CTA alone (86.9%/71.8%/83.6%) (P = 0.006/0.003/<0.001) and MRA alone (86.9%/80.3%/85.5%) (P =0.003/0.041/<0.001). No significant differences were found in sensitivity, specificity, or accuracy between the use of CTA and MRA (P = 1/0.26/0.45). CTA and MRA sensitivity and accuracy for aneurysms ≤3 mm were significantly lower than for those aneurysms larger. (P < 0.001, each).

Conclusion: Combining CTA and MRA analysis improves sensitivity, specificity, and accuracy for UIA detection compared to using each modality alone.

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.

Purpose: Accurate detection of cerebral microbleeds (CMBs) is important for detection of multiple conditions. However, CMBs can be challenging to identify on MR images, especially for distinguishing CMBs from the mimic of calcification. We performed a comparative reader study to assess the diagnostic performance of two primary MR sequences for differentiating CMBs from calcification.

Methods: Under IRB approved exempt retrospective protocol, 49 adult patients with identifiable intracranial hemorrhage who underwent multi-echo 3D Gradient Recall Echo (GRE) using 3T MRI were non-sequentially recruited under a retrospective IRB approved protocol. Multi-echo complex total field inversion quantitative susceptibility mapping (QSM) and susceptibility weighted imaging/phase (SWI/P) images were generated for all patients. 53 lesion ROIs were identified and classified on provided images by an expert panel of three neuroradiologists as either: CMB, Blood, Calcification, or Other. Three additional neuroradiologists subsequently reviewed the same SWI/P and QSM images in independent sessions and designated lesions as either blood and/or calcification using a 5-point Likert scale. Statistical analyses, on lesion classification and reader diagnostic accuracy, reader confidence-level, reader agreement-level, and the predictability of mean susceptibility values between SWI/P and QSM were conducted with logistic regression and calculation of Fleiss' κ, Kendall's w, Krippendorff's α.

Results: Across all qualitative assessment and quantitative metrics measured (simple accuracy, confidence as degree of ground truth alignment, and inter-rater agreement) QSM outperformed SWI/P. Additionally, logistic regression of average QSM voxel susceptibility achieved near-perfect separation in differentiating between CMB and calcification in the limited number of CMB/Calcification ROIs, indicating a high predictability.

Conclusion: Our study demonstrates that QSM offers improved detectability and classification of CMBs compared to the conventionally utilized SWI/P sequence. In addition, QSM simplifies the interpretation workflow by reducing the number of requisite images compared with the conventional counterpart, with improved diagnostic confidence.

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.

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.

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm for automated quantification of arterial stenosis in head and neck CT angiography (CTA).

Methods: Patients who received head and neck CTA and DSA between January 2019 and December 2021 in two centers were included. The quantitative performance of CerebralDoc per-lesion was evaluated through intraclass correlation coefficients (ICCs) and Bland-Altman analysis, comparing automated stenosis measurements and manual measurements across 0-100%, < 50%, ≥ 50% and ≥ 70% thresholds. Sensitivity analysis included linear and logistic regression, and subgroups analysis was performed to identify influencing factors.

Results: 287 patients with 1765 lesions were analyzed. ICCs between CerebralDoc and DSA for ≥ 50% and ≥ 70% stenosis were excellent (0.955, 0.922, respectively), for 0-100% stenosis was good (0.735), and for < 50% stenosis was poor (0.056). For ≥ 50% and ≥ 70% stenosis of CerebralDoc and CTA manual measurements versus DSA, ICCs were close (0.955 vs 0.994; 0.922 vs 0.986), and differences were small (0.258% vs -0.362%; 0.369% vs -0.199%). The sensitivity analysis revealed that specific locations (V1, V2, V3, V4) and slender vessels have large or remarkable differences ranging from 15.551% to 44.238%.

Conclusion: CerebralDoc exhibited excellent performance in automatically quantifying arterial stenosis of ≥ 50% and ≥ 70% in head and neck CTA. However, further research was needed to improve its performance for < 50% stenosis and to address differences in specific locations and slender vessels.

Purpose: This study analyzes the long-term clinical and angiographic outcomes of the Derivo Embolization Device (DED), an advanced flow diverter device with an electropolished surface, for the treatment of intracranial aneurysms.

Methods: A consecutive series of 101 patients (mean age: 58 years, 72% female) treated with the DED for 122 aneurysms at a single center between 2017 and 2023 was retrospectively analyzed for major (change in National Institutes of Health Stroke Scale [NIHSS] score ≥ 4 points) and minor (change in NIHSS score < 4 points) neurological events, procedural morbidity (increase of at least one point on the modified Rankin Scale), and angiographic results.

Results: There were 14 (11%) recurrent aneurysms, 15 (12%) ruptured aneurysms, 26 (21%) posterior circulation aneurysms and 16 (13%) fusiform or dissecting aneurysms. Device deployment failed in 1 case (1%). Procedure-related symptomatic procedural complications consisted of 2 (2%) major events (1 major stroke and 1 vessel perforation with intracranial hemorrhage and infarction) and 6 minor events (6 minor strokes). Procedural morbidity was 5%. There were no late ischemic or hemorrhagic events during follow-up. Complete and favorable aneurysm occlusion was achieved in 54% (40/74) and 62% (46/74) at a mean of 5 months, 71% (27/38) and 87% (33/38) at a mean of 12 months, and 76% (25/33) and 97% (32/33) at a mean of 35 months, respectively.

Conclusion: The results demonstrate progressive aneurysm occlusion beyond 12 months after DED implantation with an almost 100% favorable occlusion rate. Procedural morbidity was low and there were no late complications.