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Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method. Quantification of the Cerebral Perfusion with the Arterial Spin Labelling 3D-MRI method

The Arterial Spin Labelling (ASL) method is a Magnetic Resonance technique used toquantify the cerebral perfusion. It has the big advantage to be non-invasive so doesn’tneed the injection of any contrast agent. But due to a relatively low Signal-to-NoiseRatio (SNR) of the signal acquired (only approximately 1% of the image intensity), ithas been hampered to be widely used in a clinical setting so far.The primary objective of this project is to make the method more robust by improvingthe quality of the images, the SNR, and by reducing the acquisition time. DifferentASL protocols with different sets of parameters have been investigated. The modificationsperformed on the protocol have been investigated by analyzing images acquired onhealthy volunteers. An optimized protocol leading to a good trade-off between the differentaspects of the method, has been suggested. It is characterized by a 3:43:44:0mm3with a two-segment acquisition.A more advanced ASL method implies the acquisition of images at different inversiontimes (TI), which is called the mutli-TI method. The influence of the range of TI used inthe method has been explored. An optimized TI range (from 410ms to 3860ms, sampledevery 150ms) has been suggested to make the ASL method as performant as possible.A numerical model and a fitting algorithm have been used to extract the informationon the perfusion from the images acquired. Different models have been investigated aswell as their influence on the reliability of the results.Finally, a criterion has been implemented to evaluate the reliability of the results sothat the clinician or the user of the method can figure out how much he can count onthe results provided by the method.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-148020
Date January 2014
CreatorsGibert, Guillaume
PublisherKTH, Skolan för teknik och hälsa (STH)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationTRITA-STH ; 2014:110

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