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The relationship between blood flow impairment and oxygen depletion in acute ischemic stroke imaged with magnetic resonance imaging.

ABSTRACT: Oxygenation-sensitive spin relaxation time T2' and relaxation rate R2' (1/T2') are presumed to be markers of the cerebral oxygen extraction fraction (OEF) in acute ischemic stroke. In this study, we investigate the relationship of T2'/R2' with dynamic susceptibility contrast-based relative cerebral blood flow (rCBF) in acute ischemic stroke to assess their plausibility as surrogate markers of the ischemic penumbra. Twenty-one consecutive patients with internal carotid artery and/or middle cerebral artery occlusion were studied at 3.0?T. A physiological model of the cerebral vasculature (VM) was used to process PWI raw data in addition to a conventional deconvolution technique. T2', R2', and rCBF values were extracted from the ischemic core and hypoperfused areas. Within hypoperfused tissue, no correlation was found between deconvolved rCBF and T2' ( r?=?-0.05, p?=?0.788), or R2' ( r?=?0.039, p?=?0.836). In contrast, we found a strong positive correlation with T2' ( r?=?0.444, p?=?0.006) and negative correlation with R2' ( r?=?-0.494, p?=?0.0025) for rCBF_VM, indicating increasing OEF with decreasing CBF and that rCBF based on the vascular model may be more closely related to metabolic disturbances. Further research to refine and validate these techniques may enable their use as MRI-based surrogate markers of the ischemic penumbra for selecting stroke patients for interventional treatment strategies.

SUBMITTER: Seiler A

PROVIDER: S-EPMC6421246 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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The relationship between blood flow impairment and oxygen depletion in acute ischemic stroke imaged with magnetic resonance imaging.

Seiler Alexander A Blockley Nicholas P NP Deichmann Ralf R Nöth Ulrike U Singer Oliver C OC Chappell Michael A MA Klein Johannes C JC Wagner Marlies M

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20170920 3

Oxygenation-sensitive spin relaxation time T2' and relaxation rate R2' (1/T2') are presumed to be markers of the cerebral oxygen extraction fraction (OEF) in acute ischemic stroke. In this study, we investigate the relationship of T2'/R2' with dynamic susceptibility contrast-based relative cerebral blood flow (rCBF) in acute ischemic stroke to assess their plausibility as surrogate markers of the ischemic penumbra. Twenty-one consecutive patients with internal carotid artery and/or middle cerebr ...[more]

PMID: 28929836

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Project description:Penumbral biomarkers promise to individualize treatment windows in acute ischemic stroke. We used a novel magnetic resonance imaging approach that measures oxygen metabolic index (OMI), a parameter closely related to positron emission tomography-derived cerebral metabolic rate of oxygen utilization (CMRO2), to derive a pair of ischemic thresholds: (1) an irreversible-injury threshold that differentiates ischemic core from penumbra and (2) a reversible-injury threshold that differentiates penumbra from tissue not-at-risk for infarction.Forty patients with acute ischemic stroke underwent magnetic resonance imaging at 3 time points after stroke onset: <4.5 hours (for OMI threshold derivation), 6 hours (to determine reperfusion status), and 1 month (for infarct probability determination). A dynamic susceptibility contrast method measured cerebral blood flow, and an asymmetrical spin echo sequence measured oxygen extraction fraction, to derive OMI (OMI=cerebral blood flow×oxygen extraction fraction). Putative ischemic threshold pairs were iteratively tested using a computation-intensive method to derive infarct probabilities in 3 tissue groups defined by the thresholds (core, penumbra, and not-at-risk tissue). An optimal threshold pair was chosen based on its ability to predict infarction in the core, reperfusion-dependent survival in the penumbra, and survival in not-at-risk tissue. The predictive abilities of the thresholds were then tested within the same cohort using a 10-fold cross-validation method.The optimal OMI ischemic thresholds were found to be 0.28 and 0.42 of normal values in the contralateral hemisphere. Using the 10-fold cross-validation method, median infarct probabilities were 90.6% for core, 89.7% for nonreperfused penumbra, 9.95% for reperfused penumbra, and 6.28% for not-at-risk tissue.OMI thresholds, derived using voxel-based, reperfusion-dependent infarct probabilities, delineated the ischemic penumbra with high predictive ability. These thresholds will require confirmation in an independent patient sample.

Project description:The ability to identify metabolically active and potentially salvageable ischaemic penumbra is crucial for improving treatment decisions in acute stroke patients. Our solution involves two complementary novel MRI techniques (Glasgow Oxygen Level Dependant (GOLD) Metabolic Imaging), which when combined with a perfluorocarbon (PFC) based oxygen carrier and hyperoxia can identify penumbra due to dynamic changes related to continued metabolism within this tissue compartment. Our aims were (i) to investigate whether PFC offers similar enhancement of the second technique (Lactate Change) as previously demonstrated for the T2*OC technique (ii) to demonstrate both GOLD metabolic imaging techniques working concurrently to identify penumbra, following administration of Oxycyte® (O-PFC) with hyperoxia. Methods: An established rat stroke model was utilised. Part-1: Following either saline or PFC, magnetic resonance spectroscopy was applied to investigate the effect of hyperoxia on lactate change in presumed penumbra. Part-2; rats received O-PFC prior to T2*OC (technique 1) and MR spectroscopic imaging, which was used to identify regions of tissue lactate change (technique 2) in response to hyperoxia. In order to validate the techniques, imaging was followed by [14C]2-deoxyglucose autoradiography to correlate tissue metabolic status to areas identified as penumbra. Results: Part-1: PFC+hyperoxia resulted in an enhanced reduction of lactate in the penumbra when compared to saline+hyperoxia. Part-2: Regions of brain tissue identified as potential penumbra by both GOLD metabolic imaging techniques utilising O-PFC, demonstrated maintained glucose metabolism as compared to adjacent core tissue. Conclusion: For the first time in vivo, enhancement of both GOLD metabolic imaging techniques has been demonstrated following intravenous O-PFC+hyperoxia to identify ischaemic penumbra. We have also presented preliminary evidence of the potential therapeutic benefit offered by O-PFC. These unique theranostic applications would enable treatment based on metabolic status of the brain tissue, independent of time from stroke onset, leading to increased uptake and safer use of currently available treatment options.

Project description:ImportancePredicting infarct size and location is important for decision-making and prognosis in patients with acute stroke.ObjectivesTo determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods.Design, setting, and participantsIn this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019.Main outcomes and measuresArea under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion.ResultsAmong the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance.Conclusions and relevanceThe deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

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The relationship between blood flow impairment and oxygen depletion in acute ischemic stroke imaged with magnetic resonance imaging.

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The relationship between blood flow impairment and oxygen depletion in acute ischemic stroke imaged with magnetic resonance imaging.

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