Imaging the physiological evolution of the ischemic penumbra in acute ischemic stroke

We review the hemodynamic, metabolic and cellular parameters affected during early ischemia and their changes as a function of approximate cerebral blood flow ( CBF) thresholds. These parameters underlie the current practical definition of an ischemic penumbra, namely metabolically affected but still viable brain tissue. Such tissue is at risk of infarction under continuing conditions of reduced CBF, but can be rescued through timely intervention. This definition will be useful in clinical diagnosis only if imaging techniques exist that can rapidly, and with sufficient accuracy, visualize the existence of a mismatch between such a metabolically affected area and regions that have suffered cell depolarization. Unfortunately, clinical data show that defining the outer boundary of the penumbra based solely on perfusion-related thresholds may not be sufficiently accurate. Also, thresholds for CBF and cerebral blood volume ( CBV) differ for white and gray matter and evolve with time for both inner and outer penumbral boundaries. As such, practical penumbral imaging would involve parameters in which the physiology is immediately displayed in a manner independent of baseline CBF or CBF threshold, namely pH, oxygen extraction fraction ( OEF), diffusion constant and mean transit time ( MTT). Suitable imaging technologies will need to meet this requirement in a 10-20 min exam

Physiological origin for the BOLD poststimulus undershoot in human brain: vascular compliance versus oxygen metabolism

The poststimulus blood oxygenation level-dependent (BOLD) undershoot has been attributed to two main plausible origins: delayed vascular compliance based on delayed cerebral blood volume (CBV) recovery and a sustained increased oxygen metabolism after stimulus cessation. To investigate these contributions, multimodal functional magnetic resonance imaging was employed to monitor responses of BOLD, cerebral blood flow (CBF), total CBV, and arterial CBV (CBVa) in human visual cortex after brief breath hold and visual stimulation. In visual experiments, after stimulus cessation, CBVa was restored to baseline in 7.9±3.4 seconds, and CBF and CBV in 14.8±5.0 seconds and 16.1±5.8 seconds, respectively, all significantly faster than BOLD signal recovery after undershoot (28.1±5.5 seconds). During the BOLD undershoot, postarterial CBV (CBVpa, capillaries and venules) was slightly elevated (2.4±1.8%), and cerebral metabolic rate of oxygen (CMRO2) was above baseline (10.6±7.4%). Following breath hold, however, CBF, CBV, CBVa and BOLD signals all returned to baseline in ∼20 seconds. No significant BOLD undershoot, and residual CBVpa dilation were observed, and CMRO2 did not substantially differ from baseline. These data suggest that both delayed CBVpa recovery and enduring increased oxidative metabolism impact the BOLD undershoot. Using a biophysical model, their relative contributions were estimated to be 19.7±15.9% and 78.7±18.6%, respectively

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future