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

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Abstract

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 f 1, f 2 and V overlap 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 V overlap, f 2 and qT2 (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for V overlap (0.90) and f 2 (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time.

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Magnetic Resonance Fingerprinting

Dan Ma, Vikas Gulani, Nicole Seiberlich … (2013)

Summary Magnetic Resonance (MR) is an exceptionally powerful and versatile measurement technique. The basic structure of an MR experiment has remained nearly constant for almost 50 years. Here we introduce a novel paradigm, Magnetic Resonance Fingerprinting (MRF) that permits the non-invasive quantification of multiple important properties of a material or tissue simultaneously through a new approach to data acquisition, post-processing and visualization. MRF provides a new mechanism to quantitatively detect and analyze complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to specifically identify the presence of a target material or tissue, which will increase the sensitivity, specificity, and speed of an MR study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern recognition algorithm, MRF inherently suppresses measurement errors and thus can improve accuracy compared to previous approaches.

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MR imaging helps predict time from symptom onset in patients with acute stroke: implications for patients with unknown onset time.

Sébastian Rodrigo, Emmanuel Touzé, C. Lamy … (2010)

To assess the value of magnetic resonance (MR) imaging parameters as surrogate markers of stroke duration. The study was approved by the Ethics Committee of Ile de France III and was found to conform to generally accepted scientific principles and ethical standards. The authors studied 130 patients with acute stroke of known onset time who underwent 1.5-T MR imaging within 12 hours of the onset of stroke symptoms. Fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) imaging, and apparent diffusion coefficient (ADC) ratios were computed by using three-dimensional regions of interest to outline signal intensity changes on DW images and then projecting them onto the contralateral hemisphere. Imaging ratios in 63 patients who underwent imaging 0-3 hours after symptom onset were compared with those in 67 patients who underwent imaging more than 3 hours after onset by using the Student t test and receiver operating characteristic curves. The accuracy (sensitivity, specificity, and 95% confidence intervals [CIs]) of lesion visibility on FLAIR images in the prediction of a stroke onset time of less than 3 hours was assessed by two independent observers. Differences in imaging ratios between patients imaged 0-3 hours after symptom onset and those imaged more than 3 hours after onset were statistically significant (P < .001). The FLAIR ratio showed a positive correlation with the time from symptom onset (Pearson correlation coefficient, 0.63). Receiver operating characteristic curves indicated that the FLAIR ratio could reliably identify patients imaged 0-3 hours after symptom onset, reaching 90% sensitivity (95% CI: 83%, 98%) and 93% specificity (95% CI: 86%, 99%) when using a 7% cutoff. Stroke imaged within 3 hours could also be identified by means of visual inspection of FLAIR and DW MR images, with 94% sensitivity (95% CI: 88%, 100%) and 97% specificity (95% CI: 93%, 101%). Signal intensity changes on 1.5-T FLAIR MR images can be used as a surrogate marker of stroke age, either qualitatively or quantitatively. This suggests that MR imaging might be used as a "clock" for determining stroke age in patients with an unknown onset time, potentially increasing the number of patients who are eligible for thrombolysis. © RSNA, 2010.

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Quantitative measurements of relative fluid-attenuated inversion recovery (FLAIR) signal intensities in acute stroke for the prediction of time from symptom onset.

Ea Kang, Andras Treszl, Ona Wu … (2012)

In acute stroke magnetic resonance imaging, a 'mismatch' between visibility of an ischemic lesion on diffusion-weighted imaging (DWI) and missing corresponding parenchymal hyperintensities on fluid-attenuated inversion recovery (FLAIR) data sets was shown to identify patients with time from symptom onset ≤4.5 hours with high specificity. However, moderate sensitivity and suboptimal interpreter agreement are limitations of a visual rating of FLAIR lesion visibility. We tested refined image analysis methods in patients included in the previously published PREFLAIR study using refined visual analysis and quantitative measurements of relative FLAIR signal intensity (rSI) from a three-dimensional, segmented stroke lesion volume. A total of 399 patients were included. The rSI of FLAIR lesions showed a moderate correlation with time from symptom onset (r=0.382, P<0.001). A FLAIR rSI threshold of <1.0721 predicted symptom onset ≤4.5 hours with slightly increased specificity (0.85 versus 0.78) but also slightly decreased sensitivity (0.47 versus 0.58) as compared with visual analysis. Refined visual analysis differentiating between 'subtle' and 'obvious' FLAIR hyperintensities and classification and regression tree algorithms combining information from visual and quantitative analysis also did not improve diagnostic accuracy. Our results raise doubts whether the prediction of stroke onset time by visual image judgment can be improved by quantitative rSI measurements.