SU-D-9A-03: STAMP: Simulator for Texture Analysis in MRI/PET
- McGill University, Montreal, QC (Canada)
Purpose: To develop a convenient simulation platform to facilitate PET/MR image analysis with the prospect of gaining a better understanding of the influence of acquisition parameters on PET/MRI textural features. The simulation platform is demonstrated by showing textural variations of a representative case study using different image acquisition parameters. Methods: The simulation platform is composed of MRI simulators JEMRIS and SIMRI to achieve simulations of customized MRI sequences on sample tumor models. The PET simulator GATE is used to get 2D and 3D Monte Carlo acquisitions of voxelized PET sources using a phantom geometry and a customized scanner architecture. The platform incorporates a series of graphical user interfaces written in Matlab. Two GUIs are used to facilitate communication with the simulation executables installed on a computer cluster. A third GUI is used to collect and display the clinical and simulated images, as well as fused PET/MRI images, and perform computation of textural features.To illustrate the capabilities of this platform, one FDG-PET and T1-weighted (T1w) digitized tumor models were generated from clinical images of a soft-tissue sarcoma patient. Numerically simulated MR images were produced using 3 different echo times (TE) and 5 different repetition times (TR). PET 2D images were simulated using an OSEM algorithm with 1 to 32 iterations and a post-reconstruction Gaussian filter of 0, 2, 4 or 6 mm width. Results: STAMP was successfully used to produce numerically simulated FDG-PET and MRI images, and to calculate their corresponding textures. Three typical textures (GLCM-Contrast, GLSZM-ZSV and NGTDM-Coarseness) were found to vary by a range of 45% on average compared to reference scanning conditions in the case of FDG-PET, and by a range of 40% in the case of T1w MRI. Conclusion: We have successfully developed a Matlab-based simulation platform to facilitate PET/MRI texture image analysis for outcome prediction.
- OSTI ID:
- 22334011
- Journal Information:
- Medical Physics, Vol. 41, Issue 6; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-2405
- Country of Publication:
- United States
- Language:
- English
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