Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Neuro-Behcet’s disease (NBD) is defined as neurological involvement in Behcet’s disease. A 32-year-old man was admitted due to sudden memory impairment and had a history of recurrent oral/genital ulcers. Brain magnetic resonance imaging (MRI) revealed ischemic lesions in the bilateral fornix and left thalamus, and high-resolution vessel wall MRI demonstrated vasculitis in the anterior communicating artery. We report a rare case of NBD that presented as vasculitis and ischemic stroke in the bilateral fornix and thalamus.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) is a genetic disorder caused by mutation in mitochondrial DNA. Patients with stroke-like episodes show restrictive lesions in diffusion weighted image, whereas magnetic resonance angiography images show both vasodilation or vasoconstriction. Vasodilation may lead to hyperperfusion and cerebral edema, which may worsen clinical outcome. Here, we report a 25-year-old male patient diagnosed as MELAS, who presented with stroke-like episodes and seizures and showed cerebral edema with vasodilation which was normalized after steroid treatment.