Null point imaging: a joint acquisition/analysis paradigm for MR classification.
Automatic classification of neurological tissues is a first step to many structural analysis pipelines. Most computational approaches are designed to extract the best possible classification results out of MR data acquired with standard clinical protocols. We observe that the characteristics of the latter owe more to the historical circumstances under which they were developed and the visual appreciation of the radiographer who acquires the images than to the optimality with which they can be classified with an automatic algorithm. We submit that better performances could be obtained by considering the acquisition and analysis processes conjointly rather than optimising them independently. Here, we propose such a joint approach to MR tissue classification in the form of a fast MR sequence, which nulls the magnitude and changes the sign of the phase at the boundary between tissue types. A simple phase-based thresholding algorithm then suffices to segment the tissues. Preliminary results show promises to simplify and shorten the overall classification process.
Pitiot
Alain
A
Brain & Body Centre, University of Nottingham, UK. alain.pitiot@nottingham.ac.uk
Totman
John
J
Gowland
Penny
P
D000465:Algorithms; D001921:Brain / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity