Local Relationships Between Restricted Water Diffusion and Oxygen Consumption in the Ischemic Human Brain
- 1 July 2006
- journal article
- research article
- Published by Wolters Kluwer Health in Stroke
- Vol. 37 (7), 1741-1748
- https://doi.org/10.1161/01.str.0000232437.00621.86
Abstract
Background and Purpose— 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. Methods— Acute carotid-territory stroke patients were studied prospectively with both diffusion tensor–imaging and back-to-back steady-state 15 O-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 (CMRO 2 ), and ADC and cerebral blood flow (CBF), were assessed. Results— The ADC remained essentially unchanged until CBF reached values ≈20 mls/100g per min, beyond which it declined linearly. In contrast, except when severely reduced, the ADC was a poorer predictor of CMRO 2 . For both CBF and CMRO 2 , however, the relationship with ADC became steeper with longer times since onset, ie, the same ADC reflected lower perfusion and CMRO 2 with elapsed time. Conclusions— 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.Keywords
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