Aneurysm Wall Enhancement in Black Blood Mri Correlates with Aneurysm Size. Black Blood Mri Could Serve As an Objective Criterion of Aneurysm Stability in Near Future

Petridis, Athanasios K.; Filis, Andreas; Chasoglou, Elias; Fischer, Igor; Dibué-Adjei, Maxine; Bostelmann, Richard; Steiger, Hans Jakob; Turowski, Bernd; R, May

doi:10.4081/cp.2018.1089

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…Presence of AWE has been correlated to anatomical characteristics of IAs such as size, location, and shape [ 52 , 65 , 70 , 73 , 76 , 100 , 108 , 109 ]. AWE is described to be higher in larger IAs [ 65 , 73 , 76 ]. However, AWE can also be observed in small IAs (size < 7 mm) [ 76 ].…”

Section: High-resolution Vessel Wall Magnetic Resonance Imaging To Ev...mentioning

confidence: 99%

“…Some studies have investigated whether AWE correlates with scores used in clinics to evaluate the risk of rupture of a specific IA. Positive correlations have been observed between AWE and PHASES score [ 33 , 47 , 65 , 73 , 108 ], UIAT score [ 65 ], or ELAPSS score [ 73 , 108 ]. However, absence of correlation between UIAT score and AWE has also been shown [ 73 ].…”

Section: High-resolution Vessel Wall Magnetic Resonance Imaging To Ev...mentioning

confidence: 99%

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Intracranial aneurysm wall (in)stability–current state of knowledge and clinical perspectives

2021

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Intracranial aneurysm (IA), a local outpouching of cerebral arteries, is present in 3 to 5% of the population. Once formed, an IA can remain stable, grow, or rupture. Determining the evolution of IAs is almost impossible. Rupture of an IA leads to subarachnoid hemorrhage and affects mostly young people with heavy consequences in terms of death, disabilities, and socioeconomic burden. Even if the large majority of IAs will never rupture, it is critical to determine which IA might be at risk of rupture. IA (in)stability is dependent on the composition of its wall and on its ability to repair. The biology of the IA wall is complex and not completely understood. Nowadays, the risk of rupture of an IA is estimated in clinics by using scores based on the characteristics of the IA itself and on the anamnesis of the patient. Classification and prediction using these scores are not satisfying and decisions whether a patient should be observed or treated need to be better informed by more reliable biomarkers. In the present review, the effects of known risk factors for rupture, as well as the effects of biomechanical forces on the IA wall composition, will be summarized. Moreover, recent advances in high-resolution vessel wall magnetic resonance imaging, which are promising tools to discriminate between stable and unstable IAs, will be described. Common data elements recently defined to improve IA disease knowledge and disease management will be presented. Finally, recent findings in genetics will be introduced and future directions in the field of IA will be exposed.

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Section: High-resolution Vessel Wall Magnetic Resonance Imaging To Ev...mentioning

confidence: 99%

Section: High-resolution Vessel Wall Magnetic Resonance Imaging To Ev...mentioning

confidence: 99%

Intracranial aneurysm wall (in)stability–current state of knowledge and clinical perspectives

2021

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“…However, no such tool exists. Up until now, only manual marking of VWE regions or projected MRI intensities on 3D segmented IA geometries have been demonstrated, both of which carry high potential for errors due to difficulty in accurately co-localizing data to the segmented angiographic image [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Workflow for Objective Vessel Wall Enhancement Quantification in Intracranial Aneurysms

et al. 2021

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Background: VWE in contrast-enhanced magnetic resonance imaging (MRI) is a potential biomarker for the evaluation of IA. The common practice to identify IAs with VWE is mainly based on a visual inspection of MR images, which is subject to errors and inconsistencies. Here, we develop and validate a tool for the visualization, quantification and objective identification of regions with VWE. Methods: N = 41 3D T1-MRI and 3D TOF-MRA IA images from 38 patients were obtained and co-registered. A contrast-enhanced MRI was normalized by the enhancement intensity of the pituitary stalk and signal intensities were mapped onto the surface of IA models generated from segmented MRA. N = 30 IAs were used to identify the optimal signal intensity value to distinguish the enhancing and non-enhancing regions (marked by an experienced neuroradiologist). The remaining IAs (n = 11) were used to validate the threshold. We tested if the enhancement area ratio (EAR—ratio of the enhancing area to the IA surface-area) could identify high risk aneurysms as identified by the ISUIA clinical score. Results: A normalized intensity of 0.276 was the optimal threshold to delineate enhancing regions, with a validation accuracy of 81.7%. In comparing the overlap between the identified enhancement regions against those marked by the neuroradiologist, our method had a dice coefficient of 71.1%. An EAR of 23% was able to discriminate high-risk cases with an AUC of 0.7. Conclusions: We developed and validated a pipeline for the visualization and objective identification of VWE regions that could potentially help evaluation of IAs become more reliable and consistent.

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“…With the continuous development of clinical diagnosis technology, computed tomography (CT) diagnosis and magnetic resonance imaging (MRI) diagnosis have been more and more used, and they also have a more accurate diagnosis effect for the diagnosis of cerebrovascular [13]. Image denoising is a common matter in image processing, so the size of the solution space needs to be limited so as to obtain a true solution [14,15]. Introducing image priors into the model can obtain corresponding positive terms to limit the solution space of the image [16].…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Matrix Denoising Algorithm-Based Magnetic Resonance Imaging Combined with Computed Tomography Images in the Diagnosis of Cerebral Aneurysm

Zhang

Li-hua

Zhang

et al. 2021

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This study was to analyze the diagnostic effects of computed tomography (CT) and magnetic resonance imaging (MRI) in patients with cerebrovascular diseases (CVDs) based on low-rank matrix denoising (LRMD) algorithm. The LRMD algorithm was adopted for MRI diagnosis and CT diagnosis for comparative analysis. 129 CVD patients were selected as the research objects, 43 cases were diagnosed by CT, 43 cases were diagnosed by MRI under LRMD, and the other 43 cases were diagnosed by CT + MRI. The results showed that the diagnostic compliance rates (DCRs) of CT group in the cerebral hemorrhage (CH), cerebral infarction (CI), and cerebral aneurysm (CA) were 95.1%, 94.7%, and 70%, respectively, while those in the MRI group were 99.01%, 97.71%, and 100%, respectively. Thus, it was obtained that MRI diagnosis was much better than CT diagnosis, and CT + MRI showed the best diagnosis efficacy, showing statistical differences ( P < 0.05 ). The accuracy, sensitivity, and specificity of MRI diagnosis under the LRMD algorithm were 96.28%, 88.76%, and 90.62%, respectively, which were superior to those of CT diagnosis (92.71%, 84.94%, and 80.71%, respectively). The diagnosis cost per case (DC/C) (799.73 ± 100.02 yuan) and the total diagnosis cost (TDC) (58,521.67 ± 301.62 yuan) in the MRI group were higher than those in the CT group (601.42 ± 83.61 yuan and 39,819.2 ± 198.72, respectively) ( P < 0.05 ). In conclusion, CT + MRI under the LRMD algorithm showed good potential in diagnosis of CVD; MRI based on the LRMD algorithm showed a higher positive rate in the diagnosis of CA and was better than CT diagnosis, and CT + MRI showed the best diagnosis effect and could improve the clinical diagnosis rate.

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Aneurysm Wall Enhancement in Black Blood Mri Correlates with Aneurysm Size. Black Blood Mri Could Serve As an Objective Criterion of Aneurysm Stability in Near Future

Cited by 11 publications

References 19 publications

Intracranial aneurysm wall (in)stability–current state of knowledge and clinical perspectives

Intracranial aneurysm wall (in)stability–current state of knowledge and clinical perspectives

An Image-Based Workflow for Objective Vessel Wall Enhancement Quantification in Intracranial Aneurysms

Low-Rank Matrix Denoising Algorithm-Based Magnetic Resonance Imaging Combined with Computed Tomography Images in the Diagnosis of Cerebral Aneurysm

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