An automatic detection method of cerebral aneurysms in time-of-flight magnetic resonance angiography images based on attention 3D U-Net

Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging

Unruptured intracranial aneurysms are protuberances that appear in cerebral arteries, and their diagnostic evaluation can be a complex, time-consuming, and exhaustive task. In recent years, computer-aided systems have been developed to improve diagnostic processes. Although the proposed methods have already been reviewed to assess their suitability for clinical use, the segmentation methods have not been reviewed in detail, nor has there been a standardized way to compare segmentation and detection tasks. A systematic review was conducted to examine the technical and methodological factors contributing to this limitation. The analysis encompassed 49 studies conducted between 2019 and 2023 that utilized artificial intelligence methods and any medical imaging modality for the detection or segmentation of intracranial aneurysms. Most of the included studies focused exclusively on detection (57%), magnetic resonance angiography was the predominant imaging modality (47%), and the methodologies generally used 3D imaging as the input (71%). The reported sensitivities ranged from 0.68 to 0.90, specificities from 0.18 to 1.0, false positives per case from 0.18 to 13.8, and the Dice similarity coefficient from 0.53 to 0.98. Variations in aneurysm size were found to have a substantial impact on system performance. Studies were evaluated using a diagnostic accuracy study quality assessment tool, which revealed significant concerns regarding applicability. These concerns primarily stem from the poor reproducibility and inconsistent reporting of metrics. Recommendations for reporting outcomes were made to compare procedures across different types of imaging and tasks.

Modeling and evaluation of biomechanics and hemodynamic based on patient-specific small intracranial aneurysm using fluid-structure interaction

BACKGROUND AND OBJECTIVE

Rupture of small intracranial aneurysm (IA) often leads to the development of highly fatal clinical syndromes such as subarachnoid hemorrhage. Due to the patient specificity of small IA, there are many difficulties in evaluating the rupture risk of small IA such as multiple influencing factors, high clinical experience requirements and poor reusability.

METHODS

In this study, clinical methods such as transcranial doppler (TCD) and magnetic resonance imaging (MRI) are used to obtain patient-specific parameters, and the fluid-structure interaction method (FSI) is used to model and evaluate the biomechanics and hemodynamics of patient-specific small IA.

RESULTS

The results show that a spiral vortex stably exists in the patient-specific small IA. Due to the small size of the patient-specific small IA, the blood flow velocity still maintains a high value with maximum reaching 3 m/s. The inertial impact of blood flow and vortex convection have certain influence on hemodynamic and biomechanics parameters. They cause three high value areas of WSSM on the patient-specific small IA with maximum of 180 Pa, 130 Pa and 110 Pa, respectively. They also cause two types of WSS concentration points, positive normal stress peak value areas and negative normal stress peak value areas to appear.

CONCLUSION

This paper found that the factors affecting hemodynamic parameters and biomechanical parameters are different. Unlike hemodynamic parameters, biomechanical parameters are also affected by blood pressure in addition to blood flow velocity. This study reveals the relationship between the flow field distribution and changes of patient-specific small IA, biomechanics and hemodynamics.