2026-09-20T16:35:11Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/284352022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
A. Gregory Sorensen.
Lorenz, Cory, 1981-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:25:49Z
2005-09-26T20:25:49Z
2004
2004
http://hdl.handle.net/1721.1/28435
57002923
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 57-58).
This thesis describes and validates a new method for calculating perfusion-weighted MRI (PWI) metrics, a non-invasive technique for calculating cerebral blood flow by tracking a bolus of contrast agent. Past methods to do this calculation require human intermediaries and can lead to errors in the presence of delay and dispersion of the contrast bolus, situations which occur commonly in the pathological conditions which require PWI. The new method described calculates perfusion metrics by defining an arterial input function (AIF) for every voxel in the brain based upon the voxels in close proximity to it. This allows for automated calculation of perfusion metrics, and the localized nature of the AIFs creates an implicit regard for delay and dispersion. This thesis demonstrates that this local AIF method is indeed able to correct flow misestimations due to delay and dispersion, and that it is also more useful for predicting tissue outcome post-stroke.
by Cory Lorenz.
M.Eng.
70 p.
3475859 bytes
3482783 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Automated perfusion-weighted MRI metrics via localized arterial input functions
Automated perfusion-weighted magnetic resonance imaging metrics via localized AIF
Thesis
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<Abstract>This thesis describes and validates a new method for calculating perfusion-weighted MRI (PWI) metrics, a non-invasive technique for calculating cerebral blood flow by tracking a bolus of contrast agent. Past methods to do this calculation require human intermediaries and can lead to errors in the presence of delay and dispersion of the contrast bolus, situations which occur commonly in the pathological conditions which require PWI. The new method described calculates perfusion metrics by defining an arterial input function (AIF) for every voxel in the brain based upon the voxels in close proximity to it. This allows for automated calculation of perfusion metrics, and the localized nature of the AIFs creates an implicit regard for delay and dispersion. This thesis demonstrates that this local AIF method is indeed able to correct flow misestimations due to delay and dispersion, and that it is also more useful for predicting tissue outcome post-stroke.</Abstract>
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