Functional Magnetic Resonance Imaging for Clinical Diagnosis : Exploring and Improving the Examination Chain

• Main Author: Ragnehed, Mattias
• Format: Doctoral Thesis
• Language: English
• Published: Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV 2009
• Subjects: Radiology; Radiologi
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18095; http://nbn-resolving.de/urn:isbn:978-91-7393-645-3

Functional Magnetic Resonance Imaging (fMRI) is a relatively new imaging technique, first reported in 1992, which enables mapping of brain functions with high spatial resolution. Functionally active areas are distinguished by a small signal increase mediated by changes in local blood oxygenation in response to neural activity. The ability to non-invasively map brain function and the large number of MRI scanners quickly made the method very popular, and fMRI have had a huge impact on the study of brain function, both in healthy and diseased subjects. The most common clinical application of fMRI is pre-surgical mapping of brain functions in order to optimise surgical interventions. The clinical fMRI examination procedure can be divided into four integrated parts: (1) patient preparation, (2) image acquisition, (3) image analysis and (4) clinical decision. In this thesis, important aspects of all parts of the fMRI examination procedure are explored with the aim to provide recommendations and methods for prosperous clinical usage of the technique. The most important results of the thesis were: (I) administration of low doses of diazepam to reduce anxiety did not invalidate fMRI mapping results of primary motor and language areas, (II) the choice of visual stimuli equipment can have severe impact on the mapping of visual areas, (III) three-dimensional fMRI imaging sequences did not perform better than two-dimensional imaging sequences, (IV) adaptive spatial filtering can improve the fMRI data analysis, (V) clinical decisions should not be based on activation results from a single statistical threshold.