Timing the Ischemic Stroke by Multiparametric Quantitative Magnetic Resonance Imaging

The advent of recanalization therapies has transformed the management of acute ischemic stroke patients. The timing of symptom onset is one of the key criteria for selecting the recanalization method as pharmacological and non- pharmacological recanalization therapies are only safe when administered within strict, but evolving, time windows. Magnetic resonance imaging (MRI) reveals ischemia within minutes and estimates ischemia duration in brain parenchyma. Preclinical studies have shown that by combining diffusion and relaxometric MRI, timing ischemic strokes is possible with clinically acceptable accuracy. MRI-based stroke timing techniques have been adopted in stroke clinics to stratify patients with unknown onset time for intravenous thrombolysis, resulting in improved outcomes in clinical trials. More recent MRI approaches use absolute apparent diffusion coefficient (ADC) and T2 relaxation time data in a user-independent manner to estimate the stroke onset time in absolute terms. The introduction of expedited MRI acquisition protocols has made MRI a fast neurodiagnosis modality. Exploiting advanced technologies such as Magnetic Resonance Fingerprinting (MRF), artificial intelligence (AI), and machine learning (ML) for the post-processing of MRI data, combined with fast MRI techniques, is expected to speed up the translation of objective stroke timing procedures into patient management

An Analysis of the Interpretability of Neural Networks trained on Magnetic Resonance Imaging for Stroke Outcome Prediction

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology

Determining Stroke Onset Time Using Quantitative MRI: High Accuracy, Sensitivity and Specificity Obtained from Magnetic Resonance Relaxation Times

Many ischaemic stroke patients are ineligible for thrombolytic therapy due to unknown onset time. Quantitative MRI (qMRI) is a potential surrogate for stroke timing. Rats were subjected to permanent middle cerebral artery occlusion and qMRI parameters including hemispheric differences in apparent diffusion coefficient, T2-weighted signal intensities, T1 and T2 relaxation times (qT1, qT2) and f1, f2 and Voverlap were measured at hourly intervals at 4.7 or 9.4 T. Accuracy and sensitivity for identifying strokes scanned within and beyond 3 h of onset was determined. Accuracy for Voverlap, f2 and qT2 (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for Voverlap (0.90) and f2 (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time

A spatiotemporal theory for MRI T2 relaxation time and apparent diffusion coefficient in the brain during acute ischaemia:Application and validation in a rat acute stroke model

The objective of this study is to present a mathematical model which can describe the spatiotemporal progression of cerebral ischaemia and predict magnetic resonance observables including the apparent diffusion coefficient (ADC) of water and transverse relaxation time T(2). This is motivated by the sensitivity of the ADC to the location of cerebral ischaemia and T(2) to its time-course, and that it has thus far proven challenging to relate observations of changes in these MR parameters to stroke timing, which is of considerable importance in making treatment choices in clinics. Our mathematical model, called the cytotoxic oedema/dissociation (CED) model, is based on the transit of water from the extra- to the intra-cellular environment (cytotoxic oedema) and concomitant degradation of supramacromolecular and macromolecular structures (such as microtubules and the cytoskeleton). It explains experimental observations of ADC and T(2), as well as identifying the rate of spread of effects of ischaemia through a tissue as a dominant system parameter. The model brings the direct extraction of the timing of ischaemic stroke from quantitative MRI closer to reality, as well as providing insight on ischaemia pathology by imaging in general. We anticipate that this may improve patient access to thrombolytic treatment as a future application

Stratifying Ischaemic Stroke Patients Across 3 Treatment Windows Using T2 Relaxation Times, Ordinal Regression and Cumulative Probabilities

Unknown onset time is a common contraindication for anti-thrombolytic treatment of ischaemic stroke. T2 relaxation-based signal changes within the lesion can identify patients within or beyond the 4.5-hour intravenous thrombolysis treatment-window. However, now that intra-arterial thrombolysis is recommended between 4.5 and 6 hours from symptom onset and mechanical thrombectomy is considered safe between 6 and 24 hours, there are three treatment-windows to consider. Here we show a cumulative ordinal regression model, incorporating the T2 relaxation time, predicts the probabilities of a patient being within one of the three treatment-windows and is more accurate than signal intensity changes from T2 weighted images

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischaemia

Background Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time. Aims We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status. Methods Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times ( f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage. Results ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (VOverlap) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. VOverlap provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia. Conclusions Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue. </jats:sec