Abstract #2744
Rapid DCE-MRI parameter generation using principal component analysis and clustering
Martin Lowry 1 , Lawrence Kenning 2 , and Lindsay W Turnbull 1
1
Centre for MR Investigations, Hull York
Medical School at University of Hull, Hull, East
Yorkshire, United Kingdom,
2
Centre
for MR Investigations, University of Hull, Hull, East
Yorkshire, United Kingdom
Volumetric quantification of pharmacokinetic parameters
for from DCE-MRI data is hampered by low SNR and
computational time. An algorithm using principal
component analysis and k-means clustering was developed
which simultaneously alleviates both these factors to
rapidly produce parameter maps with increased precision.
The method reduced processing times by 60-fold with no
change in mean parameter values. Maps of vb appeared
more homogeneous with far fewer non fitting voxels. The
proposed algorithm could remove the need for off-line
processing thus making quantitative DCE-MRI more
clinically acceptable
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