Predict Tissue Outcome Research Articles

Quantification of Serial Cerebral Blood Flow in Acute Stroke Using Arterial Spin Labeling.

Perfusion-weighted imaging is used to select patients with acute ischemic stroke for intervention, but knowledge of cerebral perfusion can also inform the understanding of ischemic injury. Arterial spin labeling allows repeated measurement of absolute cerebral blood flow (CBF) without the need for exogenous contrast. The aim of this study was to explore the relationship between dynamic CBF and tissue outcome in the month after stroke onset. Patients with nonlacunar ischemic stroke underwent ≤5 repeated magnetic resonance imaging scans at presentation, 2 hours, 1 day, 1 week, and 1 month. Imaging included vessel-encoded pseudocontinuous arterial spin labeling using multiple postlabeling delays to quantify CBF in gray matter regions of interest. Receiver-operator characteristic curves were used to predict tissue outcome using CBF. Repeatability was assessed in 6 healthy volunteers and compared with contralateral regions of patients. Diffusion-weighted and T2-weighted fluid attenuated inversion recovery imaging were used to define tissue outcome. Forty patients were included. In contralateral regions of patients, there was significant variation of CBF between individuals, but not between scan times (mean±SD: 53±42 mL/100 g/min). Within ischemic regions, mean CBF was lowest in ischemic core (17±23 mL/100 g/min), followed by regions of early (21±26 mL/100 g/min) and late infarct growth (25±35 mL/100 g/min; ANOVA P<0.0001). Between patients, there was marked overlap in presenting and serial CBF values. Knowledge of perfusion dynamics partially explained tissue fate. Factors such as metabolism and tissue susceptibility are also likely to influence tissue outcome.

Iterative Reconstruction Improves Both Objective and Subjective Image Quality in Acute Stroke CTP.

PurposeComputed tomography perfusion (CTP) imaging in acute ischemic stroke (AIS) suffers from measurement errors due to image noise. The purpose of this study was to investigate if iterative reconstruction (IR) algorithms can be used to improve the diagnostic value of standard-dose CTP in AIS.MethodsTwenty-three patients with AIS underwent CTP with standardized protocol and dose. Raw data were reconstructed with filtered back projection (FBP) and IR with intensity levels 3, 4, 5. Image quality was objectively (quantitative perfusion values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) and subjectively (overall image quality) assessed. Ischemic core and perfusion mismatch were visually rated. Discriminative power for tissue outcome prediction was determined by the area under the receiver operating characteristic curve (AUC) resulting from the overlap between follow-up infarct lesions and stepwise thresholded CTP maps.ResultsWith increasing levels of IR, objective image quality (SNR and CNR in white matter and gray matter, elimination of error voxels) and subjective image quality improved. Using IR, mean transit time (MTT) was higher in ischemic lesions, while there was no significant change of cerebral blood volume (CBV) and cerebral blood flow (CBF). Visual assessments of perfusion mismatch changed in 4 patients, while the ischemic core remained constant in all cases. Discriminative power for infarct prediction as represented by AUC was not significantly changed in CBV, but increased in CBF and MTT (mean (95% CI)): 0.72 (0.67–0.76) vs. 0.74 (0.70–0.78) and 0.65 (0.62–0.67) vs 0.67 (0.64–0.70).ConclusionIn acute stroke patients, IR improves objective and subjective image quality when applied to standard-dose CTP. This adds to the overall confidence of CTP in acute stroke triage.

Abstract WP38: Lower Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging Signal Intensity After Acute Ischemic Stroke is Associated With Better Discharge Outcomes in Thrombolysed Patients

Background: Increases in FLAIR MRI signal intensity (SI) after acute ischemic stroke (AIS) have been proposed as “tissue clocks”, reflecting the degree of ischemic injury and potential for recovery in brain tissue. We hypothesize that lower SI increases will be a biomarker for less severe tissue injury and hence is potentially salvageable. To test this, we investigated patients who were treated with tPA to determine whether better outcomes were associated with lower FLAIR SI. Methods: Using our Get with the Guidelines database, we retrospectively analyzed AIS patients admitted between 2011 to 2013 who received full-dose tPA based on institutional protocols. Patients were included if they received a CT scan at our hospital before tPA therapy, and MRI performed &lt; 1 h post CT included a usable FLAIR scan and evidence of stroke on acute DWI. SI ratio (SIR) was calculated on the FLAIR by selecting region of interests in hyperintense FLAIR areas that coincided with the acute DWI lesion and matching contralateral regions. Logistic regression analysis was performed using forward stepwise analysis to combine age, sex, admission NIHSS, SIR and onset-to-treatment (OTT) time to predict discharge outcome. Good outcome was defined as discharge to home or in-patient rehabilitation hospital. Results: There were 129 AIS patients who received tPA, and 57 met our imaging criteria. Patient characteristics were: mean±SD age 70±15 years, median [IQR] NIHSS 13 [7-18], OTT 2.1±1.1 h, time-to-MRI 2.1±1.0 h, 58% female and median SIR 1.13 [1.05-1.26]. On a univariate basis, only age (P&lt;0.0001) and NIHSS (P=0.005) were significant predictors of outcome. However, multivariate analysis showed that combining age (P=0.019), NIHSS (P=0.022), and SIR (P=0.018) was able to predict good discharge outcome with 100% [95% CI 63-100%] specificity and 90% [77-96%] sensitivity. Using age and NIHSS alone, resulted in the same specificity but only 71% [56-83%] sensitivity. Discussion: Controlling for age and admission NIHSS, we found that lower FLAIR SIR was a significant predictor of discharge outcome while OTT was not. This suggests that “tissue clocks” may be more accurate than “time clocks” for predicting tissue outcome, and ultimately functional outcome.

Abstract W P25: Anatomically-Weighted Predictive Algorithms Improve MRI-Based Tissue Outcome Predictions After Acute Ischemic Stroke

Background: In acute ischemic stroke (AIS) patients, multi-parametric MRI-based predictive algorithms have shown promise in identifying tissue at risk of infarction, but do not consider the intrinsic variations of normal or pathological tissue. We hypothesized that extending MRI-based algorithms to take into consideration tissue type will improve predictions of tissue outcome. Methods: We retrospectively analyzed AIS patients who received neither revascularization nor experimental interventional treatment, who underwent MRI within 12 h from the time since they were last known well and who had follow-up imaging &gt;4 days. Perfusion- and diffusion-parametric maps were combined to predict tissue outcome using 2 models: 1) a generalized linear model (GLM) trained with data from the whole ipsilateral hemisphere (sGLM), irrespective of tissue type, or 2) an anatomically-weighted GLM (aGLM) that was calculated using a weighted average to combine results from models generated using entire white or gray matter regions only. Both methods were evaluated using jack-knifing and predicted and follow-up regions were compared in terms of accuracy (measured as area under the receiver operator characteristic curve, AUC), Dice similarity index (DSI) and root mean square error (RMSE). Results: Results from 109 patients (65% male, median 68 y IQR [55-77], NIHSS 14 [9-25]) showed that, compared to sGLM, aGLM’s predictions had higher DSI (0.48 [0.19-0.59], P&lt;0.001), and AUC (0.89 [0.86-0.94], P=0.001) and lower RMSE (0.32 [0.29-0.35], P&lt;0.001), all demonstrating improved performance. Discussion: We showed that anatomically-weighted algorithms may better capture differences in tissue vulnerability in acute ischemic stroke, contributing to improved MRI-based tissue outcome predictions.

Indocyanine green video angiography predicts outcome of extravasation injuries.

BackgroundExtravasation of cytotoxic drugs is a serious complication of systemic cancer treatment. Still, a reliable method for early assessment of tissue damage and outcome prediction is missing. Here, we demonstrate that the evaluation of blood flow by indocyanine green (ICG) angiography in the extravasation area predicts for the need of surgical intervention.MethodsTwenty-nine patients were evaluated by ICG angiography after extravasation of vesicant or highly irritant cytotoxic drugs administered by peripheral i.v. infusion. Tissue perfusion as assessed by this standardized method was correlated with clinical outcome.ResultsThe perfusion index at the site of extravasation differed significantly between patients with reversible tissue damage and thus healing under conservative management (N = 22) versus those who needed surgical intervention due to the development of necrosis (N = 7; P = 0.0001). Furthermore, in patients benefiting from conservative management, the perfusion index was significantly higher in the central extravasation area denoting hyperemia, when compared with the peripheral area (P = 0.0001).ConclusionsIn this patient cohort, ICG angiography as indicator of local perfusion within the extravasation area was of prognostic value for tissue damage. ICG angiography could thus be used for the early identification of patients at risk for irreversible tissue damage after extravasation of cytotoxic drugs.

Prediction of tissue outcome in acute stroke with perfusion CT

Prediction of tissue outcome in acute stroke with perfusion CT

The value of apparent diffusion coefficient maps in subcortical infarct lesion

The value of apparent diffusion coefficient maps in subcortical infarct lesion

Clinico-epidemiological features of cerebral venous thrombosis in Algarve

Clinico-epidemiological features of cerebral venous thrombosis in Algarve

The association of infarct pattern on coronal diffusion-weighted MR image and early neurologic deterioration in deep subcortical infarction

The association of infarct pattern on coronal diffusion-weighted MR image and early neurologic deterioration in deep subcortical infarction

Regional Prediction of Tissue Fate in Acute Ischemic Stroke

Early and accurate prediction of tissue outcome is essential to the clinical decision-making process in acute ischemic stroke. We present a quantitative predictive model of tissue fate that combines regional imaging features available after onset. A key component is the use of cuboids randomly sampled during the learning process. Models trained with time-to-maximum feature (Tmax) computed from perfusion weighted images (PWI) are compared to the ones obtained from the apparent diffusion coefficient (ADC). The prediction task is formalized as a regression problem where the inputs are the local cuboids extracted from Tmax or ADC images at onset, and the output is the segmented FLAIR intensity of the tissue 4 days after intervention. Experiments on 25 acute stroke patients demonstrate the effectiveness of the proposed approach in predicting tissue fate. Results on our dataset show the superiority of the regional model vs. a single-voxel-based approach, indicate that PWI regional models outperform ADC models, and demonstrates that a nonlinear regression model significantly improves the results in comparison to a linear model.

Elevated T2-values in MRI of stroke patients shortly after symptom onset do not predict irreversible tissue infarction

Distinct from signal alterations in diffusion-weighted images, T(2)-values are also dependent on tissue water content and known to increase with time from symptom onset in acute ischaemic stroke. The purpose of this study was to evaluate whether there is a detectable increase of T(2)-values in different regions in acute ischaemic stroke in the acute and subacute situation and to study the effect of recanalization on the evaluation of T(2)-values in the subacute phase. In addition, we sought to evaluate whether this increase in T(2)-values is reversible. For this purpose, 22 patients with acute ischaemic stroke in the territory of the middle cerebral artery underwent magnetic resonance imaging including diffusion-weighted imaging, perfusion-weighted imaging, fluid-attenuated inversion recovery to determine final infarct size, time-of-flight-angiography (acute and on day 1 or 2) and a triple echo-T(2)-sequence (calculation of T(2) maps) within 6 h after symptom onset. Images were co-registered and regions of diffusion restriction and prolonged time-to-peak as well as surviving tissue (surviving tissue = time-to-peak - final infarct size) and lesion growth (lesion growth = final infarct size-diffusion restriction) were defined and superimposed onto the quantitative T(2) map. In addition, patients were dichotomized according to recanalization information. Mean quantitative T(2)-values were derived for each patient within each region of interest. Mean T(2)-values for patients with recanalization (n = 15) in surviving tissue region of interest were 115.8 ± 7.2 ms (mean ± SD) and in the lesion growth region of interest 114.6 ± 7.0 ms. T(2)-values for patients without recanalization (n = 7) were 117.7 ± 11.4 ms in surviving tissue region of interest and 117.3 ± 12.1 ms in lesion growth region of interest. There was no significant difference between T(2)-values measured in lesion growth and surviving tissue region of interest for patients with or without recanalization. Even though it has been shown that T(2)-values increase with time from symptom onset within the infarct core, increased T(2)-values in areas of perfusion impairment do not identify irreversible damaged brain tissue and high T(2)-values are even found in tissue that is not part of the final infarct lesion and can therefore normalize. In conclusion, this study suggests that T(2)-values are not a valid imaging biomarker in acute stroke to predict tissue outcome.

Evaluating effects of normobaric oxygen therapy in acute stroke with MRI-based predictive models

BackgroundVoxel-based algorithms using acute multiparametric-MRI data have been shown to accurately predict tissue outcome after stroke. We explored the potential of MRI-based predictive algorithms to objectively assess the effects of normobaric oxygen therapy (NBO), an investigational stroke treatment, using data from a pilot study of NBO in acute stroke.MethodsThe pilot study of NBO enrolled 11 patients randomized to NBO administered for 8 hours, and 8 Control patients who received room-air. Serial MRIs were obtained at admission, during gas therapy, post-therapy, and pre-discharge. Diffusion/perfusion MRI data acquired at admission (pre-therapy) was used in generalized linear models to predict the risk of lesion growth at subsequent time points for both treatment scenarios: NBO or Control.ResultsLesion volume sizes 'during NBO therapy' predicted by Control-models were significantly larger (P = 0.007) than those predicted by NBO models, suggesting that ischemic lesion growth is attenuated during NBO treatment. No significant difference was found between the predicted lesion volumes at later time-points. NBO-treated patients, despite showing larger lesion volumes on Control-models than NBO-models, tended to have reduced lesion growth.ConclusionsThis study shows that NBO has therapeutic potential in acute ischemic stroke, and demonstrates the feasibility of using MRI-based algorithms to evaluate novel treatments in early-phase clinical trials.

Signal Evolution and Infarction Risk for Apparent Diffusion Coefficient Lesions in Acute Ischemic Stroke Are Both Time- and Perfusion-Dependent

This study aimed to examine the temporal relationship between tissue perfusion and apparent diffusion coefficient (ADC) changes within 6 hours of ischemic stroke onset and how different reperfusion patterns may affect tissue outcome in ADC lesions. Thirty-one participants were sequentially imaged at 3 hours, 6 hours, and 1 month post-stroke. Three regions of interest (ROIs) were defined within initial ADC lesions: ROI (1)reperf_3hour hyperacute reperfusion (within 3 hours), ROI (2)reperf_6hour acute reperfusion (3 to 6 hours), and ROI (3)nonreperf no reperfusion (by 6 hours). For each ROI, changes in ADC (ΔADC) from 3 to 6 hours and risks of infarction were examined. The magnitude of initial ADC reduction was similar in all 3 ROIs (P=0.51). ΔADC was strongly associated with reperfusion (P<0.0001) but not with initial ADC reduction (P=0.83). ΔADC in ROI (1)reperf_3hour and ROI (2)reperf_6hour was significantly larger than that of ROI (3)nonreperf (P<0.05). Positive ΔADC was obtained from 3 to 6 hours in ROI (1)reperf_3hour that had restored perfusion before 3 hours, demonstrating a temporal delay between reperfusion and ADC changes. Risks of infarction were significantly higher in ROI (3)nonreperf than those in ROI (1)reperf_3hour and ROI (2)reperf_6hour. Improvement in ADC did not occur coincidently with reperfusion but showed a temporal delay. Regions with similar initial ADC reductions at 3 hours had different evolution of ADC and infarction risks depending on when or if tissue reperfused. These findings provide a physiological basis for the observation that a single ADC measurement at a fixed time after stroke onset may not accurately predict tissue outcome.

Correlating Tissue Outcome with Quantitative Multiparametric MRI of Acute Cerebral Ischemia in Rats

Predicting tissue outcome remains a challenge for stroke magnetic resonance imaging (MRI). In this study, we have acquired multiparametric MRI data sets (including absolute T(1), T(2), diffusion, T(1rho) using continuous wave and adiabatic pulse approaches, cerebral blood flow (CBF), and amide proton transfer ratio (APTR) images) during and after 65 mins of middle cerebral artery occlusion (MCAo) in rats. The MRI scans were repeated 24 h after MCAo, when the animals were killed for quantitative histology. Magnetic resonance imaging parameters acquired at three acute time points were correlated with regionally matching cell count at 24 h. The results emphasize differences in the temporal profile of individual MRI contrasts during MCAo and especially during early reperfusion, and suggest that complementary information from CBF and tissue damage can be obtained with appropriate MRI contrasts. The data show that by using three to four MRI parameters, sensitive to both hemodynamic changes and different aspects of parenchymal changes, the fate of the tissue can be predicted with increased correlation compared with single-parameter techniques. Combined multiparametric MRI data and multiparametric analysis may provide an excellent tool for preclinical testing of new treatments and also has the potential to facilitate decision-making in the management of acute stroke patients.

The accuracy of two-dimensional planning for routine orthognathic surgery

Two-dimensional cephalometric planning software should be helpful for prediction of hard tissue outcome after bilateral sagittal split ramus osteotomy (BSSRO) or bimaxillary osteotomy, but transferring two-dimensional data to three-dimensions (including mock operation and surgery) may result in errors. The objective of this retrospective study was to analyze deviations between predicted results and postoperative outcome using cephalometric analyses, and to evaluate this procedure for daily use. Fifty-four subjects (mean (SD) age 26 (8) years) had a BSSRO ( n = 21) alone or in combination with Le Fort I osteotomy ( n = 33). Predictions were made for each case by cephalometric planning software and mock operations done with study models. Postoperative cephalograms were obtained after 14 days and compared with predicted cephalograms for sagittal (SNA, SNB, ANB,) and vertical (ArMeGo, ML–NSL, NL–NSL) measurements. Mean (SD) differences for all measurements varied between 1.3° (1.1°) and 2.2° (1.6°) for BSSRO; and between 1.1° (1.3°) and 2.2° (1.6°) for bimaxillary osteotomy. There were no significant differences between measurements or operations, indicating that the predictions were accurate. A difference of up to 8.5° could be measured in a single case. Cephalometric prediction therefore remains an accurate tool for planning, particularly maxillary rearrangement in the vertical and sagittal dimension for routine operations. If greater shifts in the transversal dimension are necessary, exact planning should be adapted with three-dimensional planning devices to avoid significant differences.

Predicting Tissue Outcome From Acute Stroke Magnetic Resonance Imaging

It has been hypothesized that algorithms predicting the final outcome in acute ischemic stroke may provide future tools for identifying salvageable tissue and hence guide individualized therapy. We developed means of quantifying predictive model performance to identify model training strategies that optimize performance and reduce bias in predicted lesion volumes. We optimized predictive performance based on the area under the receiver operating curve for logistic regression and used simulated data to illustrate the effect of an unbalanced (unequal number of infarcting and surviving voxels) training set on predicted infarct risk. We then tested the performance and optimality of models based on perfusion-weighted, diffusion-weighted, and structural MRI modalities by changing the proportion of mismatch voxels in balanced training material. Predictive performance (area under the receiver operating curve) based on all brain voxels is excessively optimistic and lacks sensitivity in performance in mismatch tissue. The ratio of infarcting and noninfarcting voxels used for training predictive algorithms significantly biases tissue infarct risk estimates. Optimal training strategy is obtained using a balanced training set. We show that 60% of noninfarcted voxels consists of mismatch voxels in an optimal balanced training set for the patient data presented. An equal number of infarcting and noninfarcting voxels should be used when training predictive models. The choice of test and training sets critically affects predictive model performance and should be closely evaluated before comparisons across patient cohorts.

Predicting tissue outcome in stroke: new approaches

Multimodal MRI provides powerful tools to study acute stroke pathophysiology and to guide stroke therapy. In particular, the perfusion-diffusion mismatch has been hypothesized as a target for treatment beyond the 3 h time window. Studies of infarct progression and of tissue oxygen metabolism suggest that infarct risk is extremely heterogeneous across the diffusion and perfusion lesion. The review describes techniques to more accurately image and model penumbral infarct risk. Methods assessing oxygen supply by either blood oxygen level-dependent contrast MRI or models of oxygen delivery capacity may improve the detection of tissue-at-risk. Informatics approaches integrate acute multimodal and follow-up images from large patient cohorts into models of infarct progression. When applied to subsequent acute image data, these techniques may assign infarct risks to mismatch tissue. Recent studies suggest that such estimates of tissue infarct risk may detect treatment-related risk reduction in small patient cohorts. MRI methods may detect markers of metabolic derangement in ischemia, facilitating the detection of penumbral tissue. Predictive models extend the current perfusion-diffusion mismatch concept by estimating voxel-based risk estimates. With future developments, predictive models may support advanced prognostic support and cost-effective testing of novel stroke therapies.

Automated perfusion‐weighted MRI using localized arterial input functions

To investigate the utility of an automated perfusion-weighted MRI (PWI) method for estimating cerebral blood flow (CBF) based on localized arterial input functions (AIFs) as compared to the standard method of manual global AIF selection, which is prone to deconvolution errors due to the effects of delay and dispersion of the contrast bolus. Analysis was performed on spin- and gradient-echo EPI images from 36 stroke patients. A local AIF algorithm created an AIF for every voxel in the brain by searching out voxels with the lowest delay and dispersion, and then interpolating and spatially smoothing them for continuity. A generalized linear model (GLM) for predicting tissue outcome, and MTT lesion volumes were used to quantify the performance of the localized AIF method in comparison with global methods using ipsilateral and contralateral AIFs. The algorithm found local AIFs in each case without error and generated a higher area under the receiver operating characteristic (ROC) curve compared to both global-AIF methods. Similarly, the local MTT lesion volumes had the least mean squared error (MSE). Automated CBF calculation using local AIFs is feasible and appears to produce more useful CBF maps.

Cerebral Blood Flow Thresholds in Acute Stroke Triage

To the Editor: We read with interest the recent article by Bandera and colleagues reporting their systematic review of cerebral blood flow (CBF) thresholds for ischemic penumbra and infarct core in acute ischemic stroke.1 Although we agree with the conclusion that further work is indeed required, before the use of CBF thresholds can become clinically routine, there are a number of important points not covered in this article that deserve further discussion. First and most important, this review is already dated in that there is …

Local Relationships Between Restricted Water Diffusion and Oxygen Consumption in the Ischemic Human Brain

MR is widely used to depict still ischemic but viable tissue in acute stroke. However, the relationship between the apparent diffusion coefficient (ADC) and energy failure from reduced oxygen supply are unknown in man. Acute carotid-territory stroke patients were studied prospectively with both diffusion tensor-imaging and back-to-back steady-state 15O-PET. Substantial numbers of voxels with oxygen extraction fraction >0.70 (ie, significant ongoing hypoxia) were identified in 3 patients (imaged at 7, 16 and 21 hours after stroke onset). In this voxel population, the quantitative relationships between the ADC and cerebral metabolic rate of oxygen (CMRO2), and ADC and cerebral blood flow (CBF), were assessed. The ADC remained essentially unchanged until CBF reached values approximately 20 mls/100g per min, beyond which it declined linearly. In contrast, except when severely reduced, the ADC was a poorer predictor of CMRO2. For both CBF and CMRO2, however, the relationship with ADC became steeper with longer times since onset, ie, the same ADC reflected lower perfusion and CMRO2 with elapsed time. Despite the small sample and late times from stroke onset, the findings indicate that the degree of restricted water diffusion reliably reflects the severity of oxygen deprivation below the penumbral threshold but is less strongly related to metabolic disruption, which may explain why the ADC does not reliably predict tissue outcome. However, the same degree of diffusion restriction may correspond to greater severity of tissue disruption with elapsing time, which has relevance for stroke therapy. Time elapsed since stroke onset should be taken into account when interpreting ADC declines and in voxel-based infarct prediction models.