Neuroimaging Biomarkers in Ischemic Stroke

State-of-the-art MR imaging of ischemic stroke.

Ischemic stroke is a cerebrovascular disease, characterized by the occlusion of a brain artery by a blood clot, cutting off blood flow to a part of the brain. Neuronal hypoxic injury is triggered by hemodynamic derangement, which leads to decreased oxygen delivery to the neurons, and potentially to neuronal death. The already irreversibly damaged tissue is called the infarct's core. The at-risk tissue is called the ischemic penumbra. This zone, surrounding the infarct's core and correlated with the affected vascular territory, represents the main target for intravenous injection of tissue plasminogen activator (thrombolysis) within the first 4.5 hours of symptom onset, or endovascular therapy up to 8 hours after the onset (Fisher & Albers. 2013). The ischemic penumbra is viable but dysfunctional, leading to clinical symptoms that can rapidly vanish after successful reperfusion. The penumbra can be surrounded by an oligemic region, where mild changes in cerebral blood flow (CBF) are tolerated because dilation of the vascular bed increases local cerebral blood volume (CBV) to maintain the required delivery of oxygen, glucose, and other metabolites (autoregulation).

Hemorrhagic stroke is characterized by the presence of an intracranial hemorrhage. The discussion of this disease is beyond the scope of this review, which focuses on ischemic stroke.

CT Imaging of Stroke

Thanks to its accessibility and cost-effectiveness, computed tomography (CT) is the most popular technique for acute stroke imaging. Sulcal effacement and blurring of the gray-white matter junction are distinctive features in the diagnosis of stroke. Advanced CT techniques, namely CT angiography and CT perfusion, are useful for localizing the occlusion, identifying the infarct's core (reduced CBV) and predicting the infarct's growth (increased MTT). However, MRI, and especially diffusion MRI, is diagnostically superior to CT for stroke imaging (Merino & Warach. 2010).

MR Imaging of Stroke

Typical MR imaging protocol, at the acute phase of stroke, includes (Merino & Warach. 2010):

  • FLAIR imaging, as an anatomical reference and for the differential diagnosis with other brain diseases;
  • Gradient-Recalled Echo (GRE) sequence, which can detect intracranial hemorrhages;
  • MR angiography (MRA), in order to localize the arterial occlusion;
  • diffusion imaging, as discussed below;
  • perfusion imaging, as discussed below:

At the acute phase, the ischemic lesions are not visible in conventional MRI images, e.g. FLAIR. At the chronic stage, they appear hyperintense in FLAIR images.

Diffusion Imaging in Stroke

Diffusion MRI, based on the measurement of water molecules' diffusion, can identify the infact's core at the acute stage. Although the exact mechanism of the restricted diffusion is still debated, a cell swelling and interstitial fluid contraction (cytotoxic edema), is the most widely accepted explanation (Radaideh et al. 2012). An hyper-signal on the diffusion-weighted (B1000) image is suggestive of the restricted diffusion observed in stroke. However, the T2 shine through effect can create false positive results. Therefore, a decreased apparent diffusion coefficient (ADC) should confirm the diagnosis. The zone with decreased ADC has been thought for long to be irreversibly affected, but this statement is now debated (Labeyrie et al. 2012). The pattern of DWI lesions can help identify the stroke etiology (Merino & Warach. 2010).

At a later stage, the cytotoxic edema starts to resolve and interstitial edema develops as the cell membranes disintegrate and the intracellular components become extracellular (Radaideh et al. 2012). The ADC, therefore, follows a typical time course: decreased within 30-60 minutes after the stroke onset, reaching a negative peak at 24-48 hours, then increasing to the normal value within 1-2 weeks, reaching higher values at the chronic stage.

Perfusion Imaging in Stroke

Altered perfusion is the cause of the infarct. Dynamic susceptibility contrast-enhanced (DCS) perfusion imaging is, therefore, useful in predicting the infarct's growth (Grandin 2003). Reduced cerebral blood flow (CBF = about 60 ml/100 gm/min in normal conditions) can lead to electrical dysfunction (<20 ml/100 gm/min) and cell death (<12 ml/100 gm/min). In the infarct's core, the CBF and CBV are both low due to failure of the autoregulation. In the penumbra, the CBF is reduced. The arteriolar dilatation and hypothetical capillary recruitment lead to a normal or increased CBV in the penumbra, which keeps the cells viable, but not sufficiently for normal function. The MTT is high, as a result of the slow collateral flow (Radaideh et al. 2012).

Identifying the ischemic penumbra: the diffusion/perfusion mismatch

The diffusion/perfusion mismatch is a well-known marker of ischemic penumbra (Merino & Warach. 2010). Several studies demonstrate that imaging of the ischemic penumbra with diffusion/perfusion magnetic resonance imaging (MRI) can identify subgroups of patients who are likely to improve following successful reperfusion and others who are at increased risk for hemorrhage and poor clinical outcomes, especially later than 4.5 hours after the onset of the stroke, when the use of thrombolysis is debatable (Fisher and Albers. 2013), because of the risk of hemorrhagic conversion due to the damage that the vascular endothelium has already suffered (Radaideh et al. 2012).

Although some variability can affect its measurement (Forkert et al., 2013), time-to-maximum of the deconvoluted curve (TMAX) is one of the most discriminative parameters to estimate the ischemic penumbra (Calamante et al., 2010). There is no absolute threshold that can define the tissue that will evolve to infarction. However, a threshold between 4 and 6 seconds appears optimal for the early identification of critically hypo-perfused tissue (Olivot et al., 2009).

Semi-automated tools can make the image assessment faster, in a situation where each minute is critical. Algorithms, based on the threshold-based segmentation of the TMAX and ADC maps, have been designed (Straka et al. 2010) and validated in large cohorts of patients (Lansberg et al. 2011).

BrainMagix Stroke Module

BrainMagix's Stroke Module makes the post-processing of ischemic stroke MR images fast and easy: in less than 2 minutes, you can segment, semi-automatically, the TMAX and ADC maps, and estimate the volume of the infarct's core and penumbra. Please feel free to contact us in order to test BrainMagix on your own data sets.

References

  • Calamante et al. The physiological significance of the time-to-maximum (Tmax) parameter in perfusion MRI. Stroke J. Cereb. Circ. 2010, 41:1169–1174.
  • Fisher and Albers. Advanced imaging to extend the therapeutic time window of acute ischemic stroke. Ann. Neurol. 2013, 73:4–9.
  • Forkert et al. Comparison of 10 TTP and Tmax estimation techniques for MR perfusion-diffusion mismatch quantification in acute stroke. AJNR Am. J. Neuroradiol. 2010, 34:1697–1703.
  • Grandin. Assessment of brain perfusion with MRI: methodology and application to acute stroke. Neuroradiology 2003, 45:755-766.
  • Labeyrie et al. Diffusion lesion reversal after thrombolysis: a MR correlate of early neurological improvement. Stroke 2012, 43:2986-2991.
  • Lansberg et al. RAPID Automated Patient Selection for Reperfusion Therapy: A Pooled Analysis of the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET) and the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) Study. Stroke 2011, 42:1608–1614.
  • Merino and Warach. Imaging of acute stroke. Nature Reviews Neurology 2010, 6:560-571.
  • Olivot et al. Optimal Tmax threshold for predicting penumbral tissue in acute stroke. Stroke J. Cereb. Circ. 2009, 40:469–475.
  • Radaideh et al. Correlating the basic chronological pathophysiologic neuronal changes in response to ischemia with multisequence MRI imaging. Neurographics 2012, 2:1.
  • Straka et al. Real-time diffusion-perfusion mismatch analysis in acute stroke. J. Magn. Reson. Imaging 2010, 32:1024–1037.

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