Predictive models of tissue outcome in acute human cerebral ischemia using diffusion and perfusion weighted MRI

Diffusion (DWI) and perfusion weighted (PWI) magnetic resonance imaging (MRI) provide significant insight into acute stroke and can potentially be useful for clinical decision-making. In particular, current therapeutic decisions for acute human cerebral ischemia are typically based on time of symptom onset, limiting the number of patients treated. Imaging, however, offers insight into the physiologic integrity of brain tissue that is not attainable with time of symptom onset alone. This thesis extends existing imaging techniques for acute human stroke in order to improve identification of tissue at risk of infarction, thereby assisting clinical decision-making at the stage when intervention may be most effective. DWI and PWI have both been shown to identify infarcted tissue earlier than conventional stroke imaging. However, these techniques are limited in their existing implementations. DWI in most acute stroke settings has been restricted to isotropic imaging, measuring only mean diffusivity. In this thesis, DWI is extended to diffusion tensor imaging (DTI) with results demonstrating that DTI can detect ultrastructural changes in acute human stroke. PWI measures perfusion status by tracking the first pass of a bolus of contrast agent. In this dissertation, using numerical simulations, delay in contrast agent arrival is found to result in biased estimates of perfusion indices. A deconvolution technique using a block-circulant matrix is therefore proposed to compensate for delayed arrival, and its performance is compared to non-block circulant techniques in simulations as well as in clinically acquired human data sets.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2002

Author dc:creator

• Wu, Ona

Advisor dc:contributor.advisor

• Martha L. Gray and A. Gregory Sorensen.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/8358

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8358