MR ASSESSMENT OF TRANSIENT CEREBRAL ISCHEMIA

最近的临床试验表明，早期使用溶栓剂rt-PA治疗可以改善某些缺血性脑卒中患者的临床预后。但是，即使在3小时的“治疗机会窗口”内开始治疗，很少有病例从rt-PA中获得显着益处，有些患者实际上可能会恶化。开发更有效的治疗方案和将治疗窗口延长到更晚的时间点将取决于临床医生区分有可能从溶栓（或其他治疗）中受益的缺血性卒中患者和那些将在再灌注之前建模以区分缺血性脑组织的患者的能力。具有从不可逆损伤组织（即无反应或可能因溶栓而恶化）中恢复（即接受治疗干预）的潜力，以便选择治疗方案。我们的方法是基于一个机制假设，即多参数MRI，通过考虑组织和组织灌注中水的多种生物物理状态，将比任何单一的MRI参数产生更完整的缺血性脑组织分类。该项目的第二个目标是根据再灌注成功或不成功以及对恢复、状态无变化或相关组织进一步恶化的影响来定义再灌注后治疗效果的MRI指征。基于基于MRI的组织模型的发展来预测缺血组织病理学和最终结果，在当前的资助期内，我们建议使用多参数MRI数据方法结合复杂的数据分析程序来实现这些目标。

Recent clinical trials have shown that early treatment using the thrombolytic agent, rt-PA, can improve the clinical outcome in certain patients with ischemic stroke. But even when treatment is initiated within the 3 hour "therapeutic window of opportunity", there are few cases that derive significant benefit from rt-PA and some patients may actually deteriorate. The development of more effective treatment options and extension of the therapeutic window to later time points will depend on the ability of clinicians to differentiate patients with ischemic stroke that have the potential to benefit from thrombolysis (or some other therapy) from those that will model to differentiate ischemic brain tissue, prior to reperfusion, with the potential for recovery (i.e. receptive to therapeutic intervention) from irreversibly damaged tissues (i.e. no response or may worsen with thrombolysis) for the purpose of selecting treatment options. Our approach is based on a mechanistic hypothesis that multi-parameter MRI, by accounting for the multiple biophysical states of water in tissue and tissue perfusion, will yield a more complete classification of ischemic brain tissue than any single MRI parameter. A secondary goal of the project will be to define post reperfusion MRI indications of treatment efficacy in terms of successful or unsuccessful reperfusion and the implications for recovery, no change in status or further deterioration of the involved tissue. Based on development of MRI based tissue models to predict ischemic histopathology and eventual outcome during the current funding period, we propose to accomplish these objectives using a multi- parameter MRI data approach in conjunction with sophisticated data analysis routines.