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WE‐AB‐204‐06: Pseudo‐CT Generation Using Undersampled, Single‐Acquisition UTE‐MDixon and Direct‐Mapping Artificial Neural Networks for MR‐Based Attenuation Correction and Radiation Therapy Planning
Author(s) -
Su K,
Kuo J,
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 - 10.1118/1.4925882
Subject(s) - attenuation , nuclear medicine , artificial neural network , image resolution , physics , data acquisition , iterative reconstruction , artificial intelligence , computer science , correlation coefficient , correction for attenuation , medical imaging , pattern recognition (psychology) , mathematics , optics , medicine , statistics , operating system
Purpose: Emerging technologies such as dedicated PET/MRI and MR‐therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo‐CTs. Methods: A single acquisition, 190‐second UTE‐mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water‐filled tubes were placed in the FOV for trajectory‐delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two‐point Dixon water/fat separation and R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S‐1) and relative spatial coordinates (S‐2), were incorporated. A direct‐mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo‐CT. CT images served as the training data and, using a leave‐one‐out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo‐CT declined dramatically when the spatial features, i.e. S‐1 and S‐2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo‐CT were smaller when both the S‐1 and S‐2 were used together than when either the S‐1 or the S‐2 was used. Pseudo‐CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct‐mapping ANN approach is a technically accurate, clinically practical method for pseudo‐CT generation and can potentially help improve the accuracy of MR‐AC and MR‐RTP applications. 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