Orbital infarction syndrome following mechanical thrombectomy in internal carotid artery occlusion: a case report.

BACKGROUND: Orbital ischemic syndrome is a rare entity. The clinical signs typically demonstrate acute loss of visual acuity, chemosis, proptosis, ptosis, and total ophthalmoparesis. We report a case of a man who suffered an acute internal carotid artery occlusion and developed orbital ischemic syndrome after a mechanical thrombectomy. CASE PRESENTATION: A 57-year-old Vietnamese (Kinh ethnicity) man was brought to the emergency room with complaints of a speech disturbance, facial palsy, and severe weakness of the left arm and leg, which had started 4 hours earlier, after waking up. The National Institutes of Health Stroke Scale 12 (NIHSS 12) revealed the neurological score at admission. A head computed tomography scan showed no intracranial bleeding and an Alberta Stroke Program Early Computed Tomography Score (ASPECTS) of 8 on the right brain. Computed tomography angiography showed an occlusion of the right internal carotid artery. After that, a mechanical thrombectomy was performed, and the internal carotid artery was completely reperfused. After 10 hours, he experienced orbital pain, proptosis, ptosis, chemosis, and ophthalmoplegia of the right orbit. He also had acute loss of visual acuity, and fundoscopic examination revealed papilledema, no retinal hemorrhage, and no bruit in orbital auscultation. Intraocular pressure in the right eye was measured at 50.5 mmHg. Computed tomography angiography showed no carotid-cavernous fistula, but slight enlargement of the right medial and lateral rectus muscles. He was treated with steroids and hyperosmolar agents and recovered 7 days later, but had persistent loss of visual acuity in the right eye. CONCLUSION: Orbital ischemic syndrome is a rare complication after mechanical thrombectomy treatment in acute ischemic stroke that can lead to loss of visual acuity.

Light-Weight Deformable Registration Using Adversarial Learning With Distilling Knowledge.

Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at https://github.com/aioz-ai/LDR_ALDK.