Acute Ischemic Stroke or Epileptic Seizure? Yield of CT Perfusion in a "Code Stroke" Situation

Clinical application of brain perfusion imaging in detecting stroke mimics: A review

Stroke mimics constitute a significant proportion of patients with suspected acute ischemic stroke. These conditions may resemble acute ischemic stroke and demonstrate abnormalities on perfusion imaging sequences. The most common stroke mimics include seizure/epilepsy, migraine with aura, brain tumors, functional disorders, infectious encephalopathies, Wernicke's encephalopathy, and metabolic abnormalities. Brain perfusion imaging techniques, particularly computed tomography perfusion and magnetic resonance perfusion, are being widely used in routine clinical practice for treatment selection in patients presenting with large vessel occlusion. At the same time, the utilization of these imaging modalities enables the opportunity to better diagnose patients with stroke mimics in a time-sensitive setting, leading to appropriate management, decision-making, and resource allocation. In this review, we describe patterns of perfusion abnormalities that could discriminate patients with stroke mimics from those with acute ischemic stroke and provide specific case examples to illustrate these perfusion abnormalities. In addition, we discuss the challenges associated with interpretation of perfusion images in stroke-related pathologies. In general, perfusion imaging can provide additional information in some cases-when used in combination with conventional magnetic resonance imaging and computed tomography-and might help in detecting stroke mimics among patients who present with acute onset focal neurological symptoms.

Regional computed tomography perfusion deficits in patients with hypoglycemia: two case reports

Background

Hypoglycemia in patients with diabetes mellitus, particularly type 1 can mimic acute ischemic stroke by causing focal neurological deficits. In acute ischemic stroke, the interpretation of emergency imaging including computed tomography with angiography and perfusion is crucial to guide revascularizing therapy including intravenous thrombolysis. However, different metabolic abnormalities and stroke mimics can cause focal hypoperfusion.

Methods

We describe two type 1 diabetes patients presenting with acute focal neurological deficits and hypoglycemia, who underwent multimodal computed tomography and follow-up imaging.

Case presentation

Patient 1, a 20-year-old man presented with aphasia and interstitial glucose level of 54 mg/dl. Patient 2, a 77-year-old man presented with aphasia, mild right-sided brachiofacial paresis and interstitial glucose level of 83 mg/dl. On brain imaging, no acute infarct signs were noted. Yet, both had focal left hemispheric cerebral hypoperfusion without large-vessel occlusion or stenosis. Due to persistent symptoms after normalization of blood glucose and despite a perfusion imaging pattern that was interpretated as non-typical for ischemia, both patients underwent thrombolysis without any complications.

Conclusion

Computed tomography perfusion might help to discriminate hypoglycemia with focal neurological signs from acute stroke, but further evidence is needed.

Value of 4D CT Angiography Combined with Whole Brain CT Perfusion Imaging Feature Analysis under Deep Learning in Imaging Examination of Acute Ischemic Stroke

This study was aimed at investigating the application of deep learning 4D computed tomography angiography (CTA) combined with whole brain CT perfusion (CTP) imaging in acute ischemic stroke (AIS). A total of 46 patients with ischemic stroke were selected from the hospital as the research objects. Image quality was analyzed after the 4D CTA images were obtained by perfusion imaging. The results showed that whole brain perfusion imaging based on FCN can achieve automatic segmentation. FCN segmentation results took a short time, an average of 2-3 seconds, and the Dice similarity coefficient (DSC) and mean absolute distance (MAD) were lower than those of other algorithms. FCN segmentation distance was 17.87. The parameters of the central area, the peripheral area, and the mirror area of the perfusion map were compared, and the mean transit time (MTT) and time to peak (TTP) of the lesion were prolonged compared with the mirror area. Moreover, the peripheral CBV was increased, and the differences between the parameters were significant (P < 0.05). In conclusion, using the deep learning FCN network, 4D CTA combined with whole brain CTP imaging technology can effectively analyze the perfusion state and achieve clinically personalized treatment.

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combined with head and neck computed tomography angiography (CTA) in the diagnosis of ultra-early acute ischemic stroke. Firstly, 88 patients with acute ischemic stroke were selected as the research objects and performed with cerebral CTP and CTA examinations. In order to improve the effect of image diagnosis, a new deconvolution network model AD-CNNnet based on deep learning was proposed and used in patient CTP image evaluation. The results showed that the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) of the AD-CNNnet method were significantly higher than those of traditional methods, while the normalized mean square error (NMSE) was significantly lower than that of traditional algorithms (P < 0.05). 80 cases were positive by CTP-CTA, including 16 cases of hyperacute ischemic stroke and 64 cases of acute ischemic stroke. The diagnostic sensitivity was 93.66%, and the specificity was 96.18%. The cerebral blood flow (CBF), cerebral blood volume (CBV), and the mean transit time (MTT) in the infarcted area were significantly greater than those in the corresponding healthy side area, and the time to peak (TTP) was significantly less than that in the corresponding healthy side area (P < 0.05). The cerebral perfusion parameters CBF, TTP, and MTT in the penumbra were significantly different from those in the infarct central area and the corresponding contralateral area, and TTP was the most sensitive (P < 0.05). To sum up, deep learning algorithm-based CTP combined with CTA could find the location of cerebral infarction lesions as early as possible to provide a reliable diagnostic result for the diagnosis of ultra-early acute ischemic stroke.

Stroke Mimics in the Acute Setting: Role of Multimodal CT Protocol

BACKGROUND AND PURPOSE

Ischemic stroke can be mimicked by nonischemic conditions. Due to emphasis on the rapid treatment of acute ischemic stroke, it is crucial to identify these conditions to avoid unnecessary therapies and potential complications. We investigated the performance of the multimodal CT protocol (unenhanced brain CT, CTA, and CTP) to discriminate stroke mimics from acute ischemic stroke.

MATERIALS AND METHODS

We retrospectively selected multimodal CT studies performed for clinical suspicion of acute ischemic stroke in our center in a 24-month period, including patients with at least 1 follow-up imaging study (brain CT or MR imaging). Hemorrhagic strokes were excluded. We measured the performance of multimodal CT, comparing the original diagnostic results with the final clinical diagnosis at discharge.

RESULTS

Among 401 patients, a stroke mimic condition was diagnosed in 89 (22%), including seizures (34.8%), migraine with aura attack (12.4%), conversion disorder (12.4%), infection (7.9%), brain tumor (7.9%), acute metabolic condition (6.7%), peripheral vertigo (5.6%), syncope (5.6%), transient global amnesia (3.4%), subdural hematoma (1.1%), cervical epidural hematoma (1.1%), and dural AVF (1.1%). Multimodal CT sensitivity, specificity, and accuracy were 24.7%, 99.7%, and 83%. Multimodal CT revealed peri-ictal changes in 13/31 seizures and diagnosed 7/7 brain tumors, 1/1 dural AVF, and 1/1 subdural hematoma. CT perfusion played a pivotal diagnostic role.

CONCLUSIONS

Multimodal CT demonstrated low sensitivity but high specificity in the diagnosis of stroke mimics in the acute setting. The high specificity of multimodal CT allows ruling out stroke and thereby avoiding unnecessary revascularization treatment in patients with diagnosis of a stroke mimic.