Clinical Neuroradiology最新文献

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.

Purpose: The WEB 17 system represents the fifth generation of Woven Endobridge (WEB) flow disruptors and features a low profile with fewer wires than its predecessor, the WEB 21. The present study compares the safety and efficacy of the WEB 17 and WEB 21 for the treatment of unruptured cerebral aneurysms with 4-7 mm device sizes, which were available for both systems.

Methods: Patient and aneurysm characteristics, complications, clinical outcome and angiographic results were retrospectively analysed. 1:1 propensity score matching was performed to adjust for minor baseline differences between the groups.

Results: Sixty aneurysms treated with WEB 21 and 90 with WEB 17 were included. The overall failure rate (deployment failure and adjunctive stent) was significantly higher with WEB 21 (16.7%) than with WEB 17 (3.3%, p < 0.01). The rates of neurological events between WEB 21 (6.7%) and WEB 17 treatment (1.1%) were not significantly different (p = 0.08). Also, procedural morbidity was comparably low in both groups (WEB 21: 3.3%, WEB 17: 0%, p = 0.16). The rates of complete/adequate occlusion at follow up were 69.7%/86.4% for WEB 17 vs. 80.4%/91.3% for WEB 21 at short-term (p = 0.27), and 64.5%/83.9% vs. 75.9%/86.2% at mid-term (p = 0.41), respectively. Propensity score matching confirmed the results of the unmatched series.

Conclusion: WEB 17 and WEB 21 had a similar safety and efficacy profile, but WEB 17 was associated with an improved feasibility. Prospective studies with long-term follow-up will define the full potential of the WEB 17 system.

Purpose: To propose a method for calculating hematoma volume based on automatic segmentation of chronic subdural hematoma (CSDH) using 3D U‑net and investigate whether it can be used clinically to predict recurrence.

Methods: Hematoma volumes manually measured from pre- and postoperative computed tomography (CT) images were used as ground truth data to train 3D U‑net in 200 patients (400 CT scans). A total of 215 patients (430 CT scans) were used as test data to output segmentation results from the trained 3D U‑net model. The similarity with the ground truth data was evaluated using Dice scores for pre and postoperative separately. The recurrence prediction accuracy was evaluated by obtaining receiver operating characteristic (ROC) curves for the segmentation results. Using a typical mobile PC, the computation time per case was measured and the average time was calculated.

Results: The median Dice score of the test data were preoperative hematoma volume (Pre-HV): 0.764 and postoperative subdural cavity volume (Post-SCV): 0.741. In ROC analyses assessing recurrence prediction, the area under the curve (AUC) of the manual was 0.755 in Pre-HV, whereas the 3D U‑net was 0.735. In Post-SCV, the manual AUC was 0.779; the 3D U‑net was 0.736. No significant differences were found between manual and 3D U‑net for all results. Using a mobile PC, the average time taken to output the test data results was 30 s per case.

Conclusion: The proposed method is a simple, accurate, and clinically applicable; it can contribute to the widespread use of recurrence prediction scoring systems for CSDH.

Purpose: To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo.

Methods: SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm.

Results: The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs; the width of bilateral thalamic veins was positively correlated with the width of DMVs.

Conclusion: The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.