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These studies suggest that acute brain infarction can result from various causes such as spontaneous intracerebral hemorrhage, atheromatosis, and acute anemia, and can lead to severe complications like brain swelling, coma, and structural changes detectable by imaging techniques.
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Acute brain infarction is a notable complication following spontaneous intracerebral hemorrhage (ICH). A study utilizing diffusion-weighted imaging (DWI) found that 22.9% of patients with spontaneous ICH exhibited acute brain infarcts. These infarcts were predominantly small, subcortical, and subclinical. Factors such as prior ischemic stroke, significant mean arterial pressure (MAP) reduction, and craniotomy for ICH evacuation were independently associated with the presence of these infarcts.
Infarcts in the lower brainstem present with distinct clinical and topographical patterns. A study identified six types of infarcts, with small midlateral, dorsolateral, inferolateral, and inferodorsolateral infarcts being the most common. These infarcts were often associated with Wallenberg's syndrome. The study also highlighted that atheromatosis was the most frequent cause, with vertebral artery dissection and cardioembolism also contributing.
Cerebral infarction can occur as a complication of aneurysmal subarachnoid hemorrhage (SAH). In a cohort of 487 patients, 3% had acute infarction on admission CT. These infarcts were associated with severe clinical presentations such as coma, global cerebral edema, and intraventricular hemorrhage. The presence of early cerebral infarction significantly increased the risk of mortality and severe disability.
Acute ischemic infarcts can lead to secondary degeneration in connected brain regions. Research has shown that cortical thinning occurs in areas connected to the infarct via white matter tracts. This thinning is correlated with microstructural damage in these tracts, indicating that secondary degeneration is a key feature of acute ischemic infarcts.
Large hemispheric infarction (LHI) often results in early consciousness disorders (ECD). Quantitative EEG and brain network analyses revealed that patients with higher levels of consciousness had more alpha and beta oscillations and fewer delta and theta oscillations. These findings suggest that brain network characteristics can serve as diagnostic markers for consciousness levels in LHI patients.
Massive cerebral infarction can lead to severe brain swelling, which is a critical factor in patient outcomes. A review of postmortem cases found that 78% of patients with severe brain swelling died within seven days of the infarction. The swelling often resulted in transtentorial herniation and brain-stem edema or hemorrhage, underscoring the importance of managing intracranial pressure in these patients.
Computed tomography (CT) is a valuable tool for measuring the size of cerebral infarctions. In a study of 65 patients, the mean infarction volume decreased by 25% from 7-10 days to 3 months post-infarction. The lesion size at 7-10 days correlated strongly with neurological examination scores, highlighting the utility of CT in assessing infarction severity and predicting outcomes.
Machine learning (ML) approaches have shown promise in detecting early infarction in acute stroke patients using non-contrast-enhanced CT scans. An ML algorithm demonstrated good agreement with diffusion-weighted MRI scans in identifying infarcted tissue, suggesting that automated segmentation could enhance early diagnosis and treatment planning for acute ischemic stroke.
Acute brain infarction presents with diverse clinical and radiographic characteristics depending on the underlying cause and location. Advances in imaging techniques and machine learning are improving the detection and understanding of these infarcts, which is crucial for timely and effective management. Further research is needed to refine these diagnostic tools and explore the mechanisms underlying infarction-related complications.
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