Early Cerebral Circulation Disturbance in Patients Suffering from Severe Traumatic Brain Injury (TBI): A Xenon CT and Perfusion CT Study

Abstract

Traumatic brain injury (TBI) is widely known to cause dynamic changes in cerebral blood flow (CBF). Ischemia is a common and deleterious secondary injury following TBI. Detecting early ischemia in TBI patients is important to prevent further advancement and deterioration of the brain tissue. The purpose of this study was to clarify the cerebral circulatory disturbance during the early phase and whether it can be used to predict patient outcome. A total of 90 patients with TBI underwent a xenon-computed tomography (Xe-CT) and subsequently perfusion CT to evaluate the cerebral circulation on days 1-3. We measured CBF using Xe-CT and mean transit time (MTT: the width between two inflection points [maximum upward slope and maximum downward slope from inflow to outflow of the contrast agent]) using perfusion CT and calculated the cerebral blood volume (CBV) using the AZ-7000W98 computer system. The relationships of the hemodynamic parameters CBF, MTT, and CBV to the Glasgow Coma Scale (GCS) score and the Glasgow Outcome Scale (GOS) score were examined. There were no significant differences in CBF, MTT, and CBV among GCS3-4, GCS5-6, and GCS7-8 groups. The patients with a favorable outcome (GR and MD) had significantly higher CBF and lower MTT than those with an unfavorable one (SD, VS, or D). The discriminant analysis of these parameters could predict patient outcome with a probability of 70.6%. During the early phase, CBF reduction and MTT prolongation might influence the clinical outcome of TBI. These parameters are helpful for evaluating the severity of cerebral circulatory disturbance and predicting the outcome of TBI patients.

Fig. 1.

Time density curve and time parameters. Some time parameters were obtained from perfusion CT. We employed the mean transit time (MTT) in this study. AT: time to the appearance of the contrast agent, PT: time to the peak height, MT1E: time from AT to the gravity center of the fitting time–density curve, MTT: time between the first inflection and second inflection.

Fig. 2.

The regions of interests (ROIs) in the brain. The ROIs were right and left hemispheres at the level of the basal ganglia.

Fig. 3.

Computed tomography scan (a), cerebral blood flow map (b), mean transit time map (c), and cerebral blood volume map (d).

Fig. 4.

A: Cerebral blood flow (CBF) was compared in patients with Glasgow Coma Scale (GCS) 3–4, 5–6, and 7–8. The analysis of variance showed no significant differences in CBF (P > 0.05, P = 0.065). B: Mean transit time (MTT) was compared in patients with Glasgow Coma Scale (GCS) 3–4, 5–6, and 7–8. The analysis of variance showed no significant differences in MTT (P > 0.05). C: Cerebral blood volume (CBV) was compared in patients with Glasgow Coma Scale (GCS) 3–4, 5–6, and 7–8. The analysis of variance showed no significant differences in CBV (P > 0.05).

Fig. 5.

A: Analysis of variance for cerebral blood flow (CBF) showed significant differences in Glasgow Outcome Scale (GOS). CBF was compared between patients with GOS good recovery (GR) + moderate disability (MD) and severe disability (SD) + vegetative state (VS) + death (D). There was a significant difference between these two groups (*P < 0.05). B: Analysis of variance for mean transit time (MTT) showed significant differences in GOS. MTT was compared between patients with GOS GR + MD and SD + VS + D. There was a significant difference between these two groups (*P < 0.05). C: Analysis of variance for cerebral blood flow (CBV) showed no significant differences in GOS (P = 0.067). CBV was compared between patients with GOS GR + MD and SD + VS + D. There was a significant difference between these two groups (P > 0.05).

Fig. 6.

Scatter plot showing the relationship between cerebral blood flow (CBF) and mean transit time (MTT). The favorable outcome group tended to display increased CBF and decreased MTT. The unfavorable outcome group tended to display decreased CBF and increased MTT. Using the discriminant equation y = 0.671MTT – 4.595, we were able to predict outcomes with a probability of 70.6%.