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WE‐AB‐204‐04: Feature Selection and Clustering Optimization for Pseudo‐CT Generation in MR‐Based Attenuation Correction and Radiation Therapy Planning
Author(s) -
Kuo J,
Su K,
Hu L,
Pereira G,
Herrmann K,
Muzic R,
Traughber M,
Traughber B
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4925880
Subject(s) - feature (linguistics) , feature selection , pattern recognition (psychology) , nuclear medicine , artificial intelligence , attenuation , cluster analysis , radiation treatment planning , computer science , mathematics , radiation therapy , physics , radiology , medicine , optics , philosophy , linguistics
Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR‐based radiation treatment planning applications. Although the fuzzy C‐means (FCM) algorithm has been applied for pseudo‐CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo‐CT generation. Methods: Nine volunteers were recruited. A 190‐second, single‐acquisition UTE‐mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free‐induction‐decay (FID), the first and the second echo images. Three derived images, Dixon‐fat and Dixon‐water generated by two‐point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo‐CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo‐CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon‐water are the most informative features. Further, including FID also improved the performance of pseudo‐CT generation. Consequently, the set containing FID, Dixon‐water, and R2* resulted in the most accurate, robust pseudo‐CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six‐cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon‐water, R2* are the most important features. The findings can be used to facilitate pseudo‐CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11‐063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees