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Previous studies on Corporate Social Responsibility (CSR) have focused on the impacts of CSR disclosure on the decision making of external users of CSR information. This study contributes to the li...

Background: Diffusion (DWI) and perfusion weighted imaging (PWI) have been extensively studied to delineate irreversibly-injured tissue (that will progress to infarct) from reversibly-injured tissue (that will remain viable). An MR-based method, oxygen metabolism index (MROMI), may provide relevant metabolic information critical for determining tissue viability. We compared the ability of hyperacute MROMI, mean transit time (MTT), and apparent diffusion coefficient (ADC) to distinguish brain tissue that infarcted one month later from an initially hypoperfused region that survived, and contralateral mirror brain regions. Methods: Ischemic stroke patients were imaged at 4sec + the contralateral median MTT. Three ROIs were defined: (1) ROI Infarct (final infarct as manually outlined on the 1month FLAIR); (2) ROI Peri (acutely hypoperfused, but not included in the final infarct); and (3) ROI Mirror (the contralateral mirror region of the ROI Infarct . Each patient’s ROIs were normalized (n) to the contralateral hemisphere as follows: nMTT= MTT-contralateral median MTT, nADC= ADC/contralateral median ADC, and nMROMI=MROMI/contralateral median MROMI. nMROMI was calculated in gray and white matter separately. The 3 ROI’s were compared for nMROMI, nMTT and nADC using the Kruskal-Wallis test with Dunn’s post-tests. Results: 25 patients were imaged at 2.9 ± 0.7 hrs and 1 month after stroke onset. Mean values for nMTT, nADC, and nMR-OMI were obtained for the 3 ROI’s (ROI Infarct ROI Peri and ROI Mirror ) for each patient. Significant differences were found between ROI Infarct and ROI Mirror (PInfarct and ROI Mirror (PFigure ). Both nMTT and nADC values in ROI Infarct varied greatly across patients and no significant differences between ROI Infarct and ROI Peri were found. Conclusions: Identifying tissue that would die from hypoperfused but survived tissue is challenging during acute ischemia. Our results have demonstrated that hyperacute MROMI show significant differences between these two regions, while MTT and ADC did not, likely due to high inter-individual variability using the latter parameters.

Background: While it is known that a reduction of blood flow leads to a decrease in apparent diffusion coefficient (ADC), how ADC evolves as a function of time from ischemia onset is unclear. We evaluated ADC signal change in brain tissue with temporally stable hypoperfusion, examining the relationship between severity of hypoperfusion and the rate of ADC decline during acute ischemia. Methods: Ischemic stroke patients were sequentially imaged with diffusion-perfusion weighted imaging at 4sec + the contralateral median MTT. In tissue with stable hypoperfusion on tp1 and tp2, ADC regions of interest (ROIs) with normal or moderate reduction on tp1 were evaluated. To avoid capturing tp1 ADC voxels that had already maximally declined (floor effect, -5 mm 2 /s), ADC values < 60 were excluded. To assess how ADC declined with varying degrees of hypoperfusion, tissue was divided into 6 MTT ranges: 3-6 (ROI1), 6-9 (ROI2), 9-12 (ROI3), 12-15 (ROI4), 15-18 (ROI5) and >18 (ROI6) seconds. Paired t-tests compared tp1 and tp2 ROIs for each patient. Infarct probability based on tp3 FLAIR was computed for each ROI. Results: 43 patients were imaged at 2.9 (tp1) and 6.4 hrs (tp2) after stroke onset, of which 37 returned for 1 month FLAIR. The 6 ROIs with increasing MTT severity were found in 41, 27, 19, 12, 6, and 10 patients for ROI’s 1-6, respectively. ADC values for all ROIs were similar at tp1 (near normal), despite different tp1 MTT. For ROI1-3, ADC values did not decline between tp1 and tp2 or decreased mildly (ROI3, P=0.079). For severe hypoperfusion, ADC decreased significantly: ROI4 (p=0.006), ROI5 (P=0.038), and ROI6 (P=0.0001). Using linear regression to approximate the ADC decline, the slopes were significantly different from zero with ADC decay rates of -1.87×10 -5 mm 2 /s/hr (R=0.58), -2.86×10 -5 mm 2 /s/hr (R=0.65) and -4.51×10 -5 mm 2 /s /hr (R=0.82) for ROI4-6, respectively ( Figure ). The median infarct probabilities for the 6 ROI’s increased with increasing MTT severity: 16%, 53%, 57%, 87%, 100%, and 90% for ROI1-6, respectively. Conclusions: ADC remained normal after ischemic stroke in regions with stable, moderate hypoperfusion. In contrast, with stable, severe hypoperfusion (MTT>12s), ADC decreased from 3-6 hours and the rate of decline was highly dependent on the degree of hypoperfusion.

This paper investigates the role of visual attention in managerial judgments during balanced-scorecard performance evaluations. Using the Locarna eye tracker to establish the amount of time managers spent focused on visual cues, we found that managers who look more at strategically-linked performance measures are more likely to make decisions consistent with the achievement of their subordinates’ strategic objectives. When aware of strategy, managers focused more on strategically-linked performance measures than on non-linked measures. The presentation format of the strategy information did not significantly affect this focus. Our findings indicate that awareness of strategically-linked performance measures, but not their presentation, appears to be important in helping managers to make better decisions. This study contributes to the management accounting literature by generating useful insights into the impact of visual attention on judgments and decision-making processes.

Although it is inarguable that conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of Multiple Sclerosis (MS), cMRI does not show close correlation with clinical findings or pathologic features, and is unable to predict prognosis or stratify disease severity. To this end, diffusion tensor imaging (DTI) with tractography and neuroconnectivity analysis may assist disease assessment in MS. We therefore attempted this pilot study for initial assessment of early relapsing remitting (RR) MS. Neuroconnectivity analysis was employed for evaluation of 24 early RRMS patients within two years of presentation, and compared to the network measures of a group of 30 age-and-gender-matched normal control subjects. To account for the situation that the connections between two adjacent regions may be disrupted by an MS lesion, a new metric, network communicability, was adopted to measure both direct and indirect connections. For each anatomical area, the brain network communicability and average path length (APL) were computed and compared to characterize the network changes in efficiencies. Statistically significant (p < 0.05) loss of communicability was revealed in our RRMS cohort, particularly in the frontal and hippocampal/parahippocampal regions as well as the motor strip and occipital lobes. Correlation with the 25 foot walk test with communicability measures in the left superior frontal (r = -0.71) as well as the left superior temporal gyrus (r = -0.43) and left postcentral gyrus (r = -0.41) were identified. Additionally identified were increased communicability between the deep gray matter (GM) structures (left thalamus and putamen) with the major inter-hemispheric and intra-hemispheric white matter tracts, the corpus callosum and cingulum respectively. These foci of increased communicability are thought to represent compensatory changes. The proposed DTI based neuroconnectivity analysis demonstrated quantifiable, structurally relevant alterations of fiber tract connections in early relapsing remitting MS and paves the way for longitudinal studies in larger patient groups.

The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways.

Although diffusion tensor imaging (DTI) has provided substantial insights into early brain development, most DTI studies based on fractional anisotropy ( FA ) and mean diffusivity ( MD ) may not capitalize on the information derived from the three principal diffusivities (e.g. eigenvalues). In this study, we explored the spatial and temporal evolution of white matter structures during early brain development using two geometrical diffusion measures, namely, linear ( Cl ) and planar ( Cp ) diffusion anisotropies, from 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects. The growth trajectories were estimated with generalized estimating equations (GEE) using linear fitting with logarithm of age (days). The presence of the white matter structures in Cl and Cp was observed in neonates, suggesting that both the cylindrical and fanning or crossing structures in various white matter regions may already have been formed at birth. Moreover, we found that both Cl and Cp evolved in a temporally nonlinear and spatially inhomogeneous manner. The growth velocities of Cl in central white matter were significantly higher when compared to peripheral, or more laterally located, white matter: central growth velocity Cl = 0.0465 ± 0.0273/log(days), versus peripheral growth velocity Cl = 0.0198 ± 0.0127/log(days), p − 6 . In contrast, the growth velocities of Cp in central white matter were significantly lower than that in peripheral white matter: central growth velocity Cp = 0.0014 ± 0.0058/log(days), versus peripheral growth velocity Cp = 0.0289 ± 0.0101/log(days), p − 6 . Depending on the underlying white matter site which is analyzed, our findings suggest that ongoing physiologic and microstructural changes in the developing brain may exert different effects on the temporal evolution of these two geometrical diffusion measures. Thus, future studies utilizing DTI with correlative histological analysis in the study of early brain development are warranted.

We study the economic consequence of IFRS adoption on cost of capital. We develop a model in which asset liquidity and IFRS adoption play a crucial role in determining the cost of capital. Our model and analysis predict that firms that adopt IFRS will have lower cost of capital than those do not. We then empirically test these predictions. The results of our cross-industry regression highly support our predictions.

Estimation of intracranial stress distribution caused by mass effect is critical to the management of hemorrhagic stroke or brain tumor patients, who may suffer severe secondary brain injury from brain tissue compression. Coupling with physiological parameters that are readily available using MRI, eg, tissue perfusion, a non-invasive, quantitative and regional estimation of intracranial stress distribution could offer a better understanding of brain tissue's reaction under mass effect. A quantitative and sound measurement serving this particular purpose remains elusive due to multiple challenges associated with biomechanical modeling of the brain. One such challenge for the conventional Lagrangian frame based finite element method (LFEM) is that the mesh distortion resulted from the expansion of the mass effects can terminate the simulation prematurely before the desired pressure loading is achieved. In this work, we adopted an arbitrary Lagrangian and Eulerian FEM method (ALEF) with explicit dynamic solutions to simulate the expansion of brain mass effects caused by a pressure loading. This approach consists of three phases: 1) a Lagrangian phase to deform mesh like LFEM, 2) a mesh smoothing phase to reduce mesh distortion, and 3) an Eulerian phase to map the state variables from the old mesh to the smoothed one. In 2D simulations with simulated geometries, this approach is able to model substantially larger deformations compared to LFEM. We further applied this approach to a simulation with 3D real brain geometry to quantify the distribution of von Mises stress within the brain.

In contrast to the more common Diffusion Tensor Imaging (DTI), High Angular Resolution Diffusion Imaging (HARDI) allows superior delineation of angular microstructures of brain white matter, and makes possible multiple-fiber modeling of each voxel for better characterization of brain connectivity. However, in the context of image registration, the question of how much information is needed for satisfactory alignment remains unanswered. Low order representation of the diffusivity information is generally more robust than the higher order representation, but the latter gives more information for correct fiber tract alignment. However, higher order representation, when naively utilized, might not necessarily be conducive to improving registration accuracy since similar structures with significant orientation differences prior to proper alignment might be mistakenly taken as non-matching structures. We propose in this paper a hierarchical spherical harmonics based registration algorithm which utilizes the wealth of information provided by HARDI in a more principled means. The image volumes are first registered using robust, relatively direction invariant features derived from the diffusion-attenuation profile, and their alignment is then refined using spherical harmonic (SH) representation of gradually increasing order. This progression of SH representation from non-directional, single-directional to multi-directional representation provides a systematic means of extracting directional information from the HARDI data. Experimental results show a significant increase in registration accuracy over a state-of-the-art DTI registration algorithm.

A novel hierarchical unbiased group-wise registration is de veloped to robustly transform each individual image towards a common space for atlas based analysis. This hierarchical group-wise registration approach consists of two main components, (1) data clustering to group similar images together and (2) unbiased group-wise registration to generate a mean image for each cluster. The mean images generated in the lower hierarchical level are regarded as the input images for the higher hierarchy. In the higher hierarchical level, these input images will be further clustered and then registered by using the same two components mentioned. This hierarchical bottom-up clustering and within-cluster group-wise registration is repeated until a final mean image for the whole population is formed. This final mean image represents the common space for all the su bjects to be warped to in order for th e atlas based analysis. Each individual image at the bottom of the constructed hierarchy is transformed towards the root node through concatenating all the intermediate displacement fields. In orde r to evaluate the performance of the prop osed hierarchical registration in atlas based statistical analysis, comparisons were made with the conventional group-wise registration in detecting simulated brain atrophy as well as fractional anisotropy differences between neonates and 1-year-olds. In both cases, the proposed approach demonstrated improved sensitiv ity (higher t-scores) than the conventional unbiased registration approach. Keywords: Hierarchical registration, Unbiased registration, Group-wise registration

Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectivity in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups. The invariant measures and principal directions, however, do not capture all information in full diffusion tensor, which can decrease the statistical power of DTI in detecting subtle changes of white matters. Thus, it is very desirable to develop new statistical methods for analyzing full diffusion tensors. In this paper, we develop a set of toolbox, called RADTI, for the analysis of the full diffusion tensors as responses and establish their association with a set of covariates. The key idea is to use the recent development of log-Euclidean metric and then transform diffusion tensors in a nonlinear space into their matrix logarithms in a Euclidean space. Our regression model is a semiparametric model, which avoids any specific parametric assumptions. We develop an estimation procedure and a test procedure based on score statistics and a resampling method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest. Monte Carlo simulations are used to examine the finite sample performance of the test procedure for controlling the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic and age effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus.

Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease process. The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters, such as associations between brain structure and function and covariates of interest, such as IQ, age, gene, diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly, our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.

Despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. Neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. However, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. In this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (GEE2) and a test procedure. The GEE2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. The score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. A resampling method is used to control the family-wise error rate to adjust for multiple comparisons. With diffusion tensor imaging (DTI), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. The proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.

The temperature of foods and fluids is a major factor that determines their pleasantness and acceptability. Studies of nonhuman primates have shown that many neurons in cortical taste areas receive and process not only chemosensory inputs, but oral thermosensory (temperature) inputs as well. We investigated whether changes in oral temperature activate these areas in humans, or middle or posterior insular cortex, the areas most frequently identified for the encoding of temperature information from the human hand. In the fMRI study we identified areas of activation in response to innocuous, temperature-controlled (cooled and warmed, 5, 20 and 50 degrees C) liquid introduced into the mouth. The oral temperature stimuli activated the insular taste cortex (identified by glucose taste stimuli), a part of the somatosensory cortex, the orbitofrontal cortex, the anterior cingulate cortex, and the ventral striatum. Brain regions where activations correlated with the pleasantness ratings of the oral temperature stimuli included the orbitofrontal cortex and pregenual cingulate cortex. We conclude that a network of taste- and reward-responsive regions of the human brain is also activated by intra-oral thermal stimulation, and that the pleasant subjective states elicited by oral thermal stimuli are correlated with the activations in the orbitofrontal cortex and pregenual cingulate cortex. Thus the pleasantness of oral temperature is represented in brain regions shown in previous studies to represent the pleasantness of the taste and flavour of food. Bringing together these different oral representations in the same brain regions may enable particular combinations to influence the pleasantness of foods.

This study examines the impact of goal achievability and feedback on the effort and performance of employees who have target-based incentive contracts. Using the Mirametrix S2 eye-tracking device t...

In the past 10 to 15 years, 1.5T has been one of the most commonly used field strengths for day-to-day clinical operations. However, recent advances in high field technology and the increased availability of high field (> 1.5T) human scanners have opened the doors for a variety of exciting improvements in clinical and research applications of MR imaging. In particular, 3T has continued to gain wide acceptance as one of the main field strengths for clinical and research studies. Therefore, in this article the authors focus on the pros and cons of 3T imaging and comparisons between results obtained at 3T and 1.5T.

Magnetic resonance (MR) tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. In this paper, we present a maximum a posteriori (MAP) framework for detecting tag lines using a Markov random field (MRF) defined on the lattice generated by three-dimensional (3-D) and four-dimensional (4-D) (3-D + t) uniform sampling of B-spline models. In the 3-D case, MAP estimation is cast for detecting present tag features in the current image given an initial solid from the previous frame (the initial undeformed solid is manually positioned by clicking on corner points of a cube). The method also allows the parameters of the solid model, including the number of knots and the spline order, to be adjusted within the same framework. Fitting can start with a solid with less knots and lower spline order and proceed to one with more knots and/or higher order so as to achieve more accuracy and/or higher order of smoothness. In the 4-D case, the initial model is considered to be the linear interpolation of a sequence of optimal solids obtained from 3-D tracking. The same framework proposed for the 3-D case can once again be applied to arrive at a 4-D B-spline model with a higher temporal order.

Efficient constrained thin-plate spline warps are proposed in this paper which can warp an area in the plane such that two embedded snake grids obtained from two SPAMM frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector-field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the myocardium, where no information is available, a C/sup 1/ continuous vector field is interpolated. The formalism proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The method has been extensively validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

Magnetic resonance tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. Typically, sets of tag surfaces are encoded in the tissue appearing as dark lines on 2D images. In this paper, we present a Maximum A Posteriori (MAP) framework for detecting tag lines using a Markov random field defined on the lattice generated by uniform sampling of B-spline models in 3D and 4D. In the 3D case, MAP estimation is cast for finding the optimal solid for the tag features present in the current image set given an initial solid from the previous frame. The method also allows the parameters of the solid model including the number of knots and the spline order to be adjusted within the same framework. Fitting can start with a solid with less knots and lower spline order, and proceed to one with more knots and/or higher order so as to achieve more accuracy. The optimal solids obtained from 3D tracking for all the frames in the image sequence are considered a 4D B-spline model with linear time interpolation. The framework is then applied to arrive at a 4D B-spline model with higher order time interpolation. The method has been validated with 5 sets of in-vivo data, comprised of a sum total of 882 short-axis (SA) and long-axis (LA) images.

Noninvasive imaging techniques for assessing the dynamic behavior of the human heart are invaluable in the diagnosis of myocardial diseases. MRI is a noninvasive imaging technique that provides superb anatomic information with excellent spatial resolution and soft tissue contrast. Conventional MR studies of the heart provide accurate measures of global and regional myocardial function, chamber volumes, and ejection fractions. In MR tagging, the magnetization property of selective material points are altered in order to create tagged patterns within a deforming body such as the heart muscle. The resulting pattern defines a time-varying curvilinear coordinate system on the underlying tissue. During tissue contractions, the grid patterns move, allowing for visual tracking of the grid intersections over time. The intrinsic high spatial and temporal resolutions of such myocardial analysis schemes provide unsurpassed information about local contraction and deformation in the myocardium which can be used to derive local strain and deformation indices from different regions.

MRI is unique in its ability to non-invasively and selectively alter tissue magnetization, and create tagged patterns within a deforming body such as the heart muscle. The resulting grid patterns define non-invasive tissue markers, providing a mechanism for in-vivo measurement of tissue motion and strain. In this paper, we report on objective comparison of thin-plate spline warps in reconstructing true deformations when using homologous land-mark points and when using homologous curves from tagged MR-image sequences of a cardiac motion simulator. In addition, the number of corresponding land-mark points as well as the number of corresponding curves are varied in order to determine the effect on accuracy of reconstructions. Finally, application of the developed techniques to computing LV tissue motion and strain in systole from short-axis tagged images of a porcine model with anterior myocardial infarction (MI) are given.

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which the authors track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C/sup 1/ continuous vector field is interpolated. The implementation proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

Efficient constrained thin-plate spline warps are proposed in this paper which can warp an area in the plane such that two embedded snake grids obtained from two SPAMM frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the myocardium, where no information is available, a C1 continuous vector field is interpolated. The formalism proposed in this paper improves on our previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The method has been extensively validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

The balance between cerebral blood flow (CBF) and oxygen extraction fraction (OEF) is important in determining the status of tissue viability during acute cerebral ischemia 1, 2. After ischemic onset, CBF decreases, accompanied by an increase in OEF in order to maintain cerebral oxygen cerebral metabolic rate of oxygen utilization (CMRO2). A persisted or further reduced CBF results in a decline of CMRO2, leading to neural cell death. In this study, a T2* (T2') approach 3 and a dynamic tracer method 4 are used to obtain OEF and CBF, respectively, which in turn allows the estimates of MR_CMRO2, in a middle cerebral artery occlusion (MCAO) rat model. The temporal evolution of cerebral haemodynamic changes is examined. In total, three male Long Evans rats were studied. Cerebral ischemia was induced using the suture model inside the magnet bore with approved animal protocols. All images were acquired on a Siemens 3 T Allegra scanner. A 2D multi-echo gradient echo/spin echo sequence (MEGESE) was utilized to obtain OEF 3. Apparent diffusion coefficient (ADC) maps was obtained from segmented EPI diffusion weighted images (DWI). MEGESE and DWI images were acquired prior to MCAO and continued up to three hours post MCAO. A tracer dynamic perfusion weighted imaging (PWI) was utilized to obtain CBF at the end of the entire study so as to avoid the contamination of contrast agent induced susceptibility for the oxygenation measurements. CBF is assumed to remain constant throughout the entire post MCAO period in a permanent MCAO. MR_CMRO2 was calculated by multiplying OEF and CBF. ROIs were defined in both ipsilateral and contralateral hemisphere to obtain ADC, OEF, CBF and CMRO2 changes. Before MCA occlusion, ADC, OEF were not statistically different in the ipsilateral and contralateral hemisphere ROIs. Immediately after MCAO, ADC reduced, while OEF increased. As lesion progressed, the initially elevated OEF returned to or below the baseline, while the ADC might have little change. In addition, CMRO2 showed a moderate decrease to 30–55% followed by a severe reduction to 9–20% of that of normal tissue. The time period of moderate reduction of CMRO2 depends on the severity of CBF. In one rat, a 14% of CBF reduction corresponded to a 40 minutes moderate CMRO2 reduction followed by severe CMRO2 reduction. In another rat, a 21% CBF reduction allows a moderate CMRO2 reduction for about 90 minutes. With a MCAO rat model, our results demonstrate the temporal biphasic behavior of OEF with an initial increase and followed by a returning baseline values. In addition, the temporal evolution of CMRO2 agree with the reported results in the literature using PET 5. In conclusion, non-invasive methods to assess cerebral oxygenation status in vivo may provide us more insight into the ischemic tissue viability.