Null point imaging: A joint acquisition/analysis paradigm for MR classification
Authors: Pitiot, A., Totman, J. and Gowland, P.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 4791 LNCS
Issue: PART 1
Pages: 759-766
eISSN: 1611-3349
ISBN: 9783540757566
ISSN: 0302-9743
DOI: 10.1007/978-3-540-75757-3_92
Abstract: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. © Springer-Verlag Berlin Heidelberg 2007.
Source: Scopus
Null point imaging: a joint acquisition/analysis paradigm for MR classification.
Authors: Pitiot, A., Totman, J. and Gowland, P.
Journal: Med Image Comput Comput Assist Interv
Volume: 10
Issue: Pt 1
Pages: 759-766
DOI: 10.1007/978-3-540-75757-3_92
Abstract: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.
Source: PubMed
Null point imaging: a joint acquisition/analysis paradigm for MR classification
Authors: Pitiot, A., Totman, J. and Gowland, P.
Journal: International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages: 759-766
Source: Manual
Null point imaging: a joint acquisition/analysis paradigm for MR classification.
Authors: Pitiot, A., Totman, J. and Gowland, P.
Journal: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume: 10
Issue: Pt 1
Pages: 759-766
DOI: 10.1007/978-3-540-75757-3_92
Abstract: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.
Source: Europe PubMed Central