Combined functional, perfusion and diffusion magnetic resonance imaging (MRI) with a temporal resolution of 30 mins was performed on permanent and transient focal ischemic brain injury in rats during the acute phase. The apparent diffusion coefficient (ADC), baseline cerebral blood flow (CBF), and functional MRI (fMRI) blood-oxygen-level-dependent (BOLD), CBF, and CMRO(2) responses associated with CO(2) challenge and forepaw stimulation were measured. An automated cluster analysis of ADC and CBF data was used to track the spatial and temporal progression of different tissue types (e.g., normal, 'at risk,' and ischemic core) on a pixel-by-pixel basis. With permanent ischemia (n=11), forepaw stimulation fMRI response in the primary somatosensory cortices was lost, although vascular coupling (CO(2) response) was intact in some animals. Control experiments in which the right common carotid artery was ligated without causing a stroke (n=8) showed that the delayed transit time had negligible effect on the fMRI responses in the primary somatosensory cortices. With temporary (15-mins, n=8) ischemia, transient CBF and/or ADC declines were observed after reperfusion. However, no T(2) or TTC lesions were observed at 24 h except in two animals, which showed very small subcortical lesions. Vascular coupling and forepaw fMRI response also remained intact. Finally, comparison of the relative and absolute fMRI signal changes suggest caution when interpreting percent changes in disease states in which the baseline signals are physiologically altered; quantitative CBF fMRI are more appropriate measures. This approach provides valuable information regarding ischemic tissue viability, vascular coupling, and functional integrity associated with ischemic injury and could have potential clinical applications.

An algorithm was developed to statistically predict ischemic tissue fate on a pixel-by-pixel basis. Quantitative high-resolution (200 x 200 microm) cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) were measured on acute stroke rats subjected to permanent middle cerebral artery occlusion and an automated clustering (ISODATA) technique was used to classify ischemic tissue types. Probability and probability density profiles were derived from a training data set (n=6) and probability maps of risk of subsequent infarction were computed in another group of animals (n=6) as ischemia progressed. Predictions were applied to overall tissue fate. Performance measures (sensitivity, specificity, and receiver operating characteristic) showed that prediction made based on combined ADC+CBF data outperformed those based on ADC or CBF data alone. At the optimal operating points, combined ADC+CBF predicted tissue infarction with 86%+/-4% sensitivity and 89%+/-6% specificity. More importantly, probability of infarct (P(I)) for different ISODATA-derived ischemic tissue types were also computed: (1) For the 'normal' cluster in the ischemic right hemisphere, P(I) based on combined ADC+CBF data (P(I)[ADC+CBF]) accurately reflected tissue fate, whereas P(I)[ADC] and P(I)[CBF] overestimated infarct probability. (2) For the 'perfusion-diffusion mismatch' cluster, P(I)[ADC+CBF] accurately predicted tissue fate, whereas P(I)[ADC] underestimated and P(I)[CBF] overestimated infarct probability. (3) For the core cluster, P(I)[ADC+CBF], P(I)[ADC], and P(I)[CBF] prediction were high and similar ( approximately 90%). This study shows an algorithm to statistically predict overall, normal, ischemic core, and 'penumbral' tissue fate using early quantitative perfusion and diffusion information. It is suggested that this approach can be applied to stroke patients in a computationally inexpensive manner.

Partial-volume effects (PVE) in stroke imaging could hinder proper delineation of normal, ischemic, and at-risk tissues. Cerebral-blood-flow (CBF) and apparent diffusion coefficient (ADC) were measured at high and low resolution (HR = 128 x 128, LR = 64 x 64) in focal ischemia in rats during the acute phase. The data were evaluated for PVE on ischemic tissue classification on a pixel-by-pixel basis and the misclassified pixels were quantified as ischemia progressed. The main drawbacks of high-resolution imaging are reduced temporal resolution and/or signal-to-noise ratio. The high- versus low-resolution scatterplots and histograms of pixels along the normal-abnormal boundaries in the ADC and CBF maps showed marked ischemia-related PVE. By comparison with the homologous regions in the contralateral normal hemisphere, the effect of increased noise and intrinsic tissue heterogeneity due to high resolution could be distinguished from ischemia-related PVE. Degrading the high-resolution (128 x 128) data to a 64 x 64 or 32 x 32 matrix increased the severity of PVE. Zero-filling of low-resolution (64 x 64) data to 128 x 128 also increased PVE. It was concluded that PVE: (1) misclassified substantial pixels along the normal-abnormal boundaries, (2) overestimated abnormal volumes at the expense of mostly "at-risk" and some "normal" tissues, (3) were more severe at the early time points postischemia, and (4) confounded the interpretation of the operationally defined ischemic penumbra.

High-resolution (200 x 200 x 1,500 microm3) imaging was performed to derive quantitative cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) maps in stroke rats (permanent occlusion) every 30 minutes up to 3 hours after occlusion onset, followed by histology at 24 hours. An improved automated iterative-self-organizing-data-analysis-algorithm (ISODATA) was developed to dynamically track ischemic tissue fate on a pixel-by-pixel basis during the acute phase. ISODATA-resolved clusters were overlaid on the CBF-ADC scatterplots and image spaces. Tissue volume ADC, and CBF of each ISODATA cluster were derived. In contrast to the single-cluster normal left hemisphere (ADC = 0.74 +/- 0.02 x 10(-3) mm2/s, CBF = 1.36 +/- 0.22 mL g(-1)min(-1), mean +/- SD, n = 8), the right ischemic hemisphere exhibited three ISODATA clusters, namely: "normal" (normal ADC and CBF), "ischemic core" (low CBF and ADC), and at-risk "perfusion-diffusion mismatch" (low CBF but normal ADC). At 180 minutes, the mismatch disappeared in five rats (Group I, 180-minute "core" lesion volume = 255 +/- 62 mm3 and 24-hour infarct volume = 253 +/- 55 mm3, P > 0.05), while a substantial mismatch persisted in three rats (Group II, 180-minute CBF-abnormal volume = 198 +/- 7 mm3 and 24-hour infarct volume 148 +/- 18 mm3, P < 0.05). The CBF (0.3 +/- 0.09 mL g(-1)min(-1)) of the "persistent mismatch" (Group II, 0.3 +/- 0.09 mL g(-1)min(-1)) was above the CBF viability threshold (0.2 to 0.3 mL g(-1)min(-1)) throughout and its ADC (0.70 +/- 0.03 x 10(-3) mm2/s) did not decrease as ischemia progressed. In contrast, the CBF (0.08 +/- 0.03 mL g(-1)min(-1)) of the analogous brain region in Group I was below the CBF viability threshold, and its ADC gradually decreased from 0.63 +/- 0.05 to 0.43 +/- 0.03 x 10(-3) mm2/s (ADC viability threshold = 0.53 +/- 0.02 x 10(-3) mm2/s). The modified ISODATA analysis of the ADC and CBF tissue characteristics during the acute phase could provide a useful and unbiased means to characterize and predict tissue fates in ischemic brain injury and to monitor therapeutic intervention.