Dankbaar J W, Hom J, Schneider T, Cheng S-C, Lau B C, van der Schaaf I, Virmani S, Pohlman S, Wintermark M
Department of Radiology, Neuroradiology Section, University of California, San Francisco, 505, Parnassus Avenue, Box 0628, San Francisco, CA 94143-0628, USA.
J Neuroradiol. 2009 Oct;36(4):219-27. doi: 10.1016/j.neurad.2009.01.001. Epub 2009 Feb 28.
The goal of this study was to determine blood-brain barrier permeability (BBBP) values extracted from perfusion-CT (PCT) using the Patlak model and possible variations related to age, gender, race, vascular risk factors and their treatment and anatomy in non-stroke patients.
We retrospectively identified 96 non-stroke patients who underwent a PCT study using a prolonged acquisition time up to 3 minutes. Patients' charts were reviewed for demographic data, vascular risk factors and their treatment. The Patlak model was applied to calculate BBBP values in regions of interest drawn within the basal ganglia and the gray and white matter of the different cerebral lobes. Differences in BBBP values were analyzed using a multivariate analysis considering clinical variables and anatomy.
Mean absolute BBBP values were 1.2 ml 100 g(-1) min(-1) and relative BBBP/CBF values were 3.5%. Statistical differences between gray and white matter were not clinically relevant. BBBP values were influenced by age, history of diabetes and/or hypertension and aspirin intake.
This study reports ranges of BBBP values in non-stroke patients calculated from delayed phase PCT data using the Patlak model. These ranges will be useful to detect abnormal BBBP values when assessing patients with cerebral infarction for the risk of hemorrhagic transformation.
本研究的目的是确定使用Patlak模型从灌注CT（PCT）中提取的血脑屏障通透性（BBBP）值，以及非卒中患者中与年龄、性别、种族、血管危险因素及其治疗和解剖结构相关的可能变化。
我们回顾性地确定了96例接受PCT研究的非卒中患者，采集时间延长至3分钟。查阅患者病历以获取人口统计学数据、血管危险因素及其治疗情况。将Patlak模型应用于计算在基底节以及不同脑叶的灰质和白质内绘制的感兴趣区域的BBBP值。使用考虑临床变量和解剖结构的多变量分析来分析BBBP值的差异。
平均绝对BBBP值为1.2 ml 100 g⁻¹ min⁻¹，相对BBBP/CBF值为3.5%。灰质和白质之间的统计学差异在临床上无相关性。BBBP值受年龄、糖尿病和/或高血压病史以及阿司匹林摄入的影响。
本研究报告了使用Patlak模型从延迟期PCT数据计算出的非卒中患者的BBBP值范围。在评估脑梗死患者出血性转化风险时，这些范围将有助于检测异常的BBBP值。
