3D simulation of pet brain images using segmented MRI data and positron tomograph characteristics
References (12)
- et al.
PETSIM: Monte Carlo simulation of all sensitivity and resolution parameters of cylindrical positron imaging; systems
Phys. Med. Biol.
(1992) - et al.
A realistic computersimulated brain phantom far evaluation of PET characteristics
IEEE Trans. Med. Imag.
(1987) Simulation of signal recovery in PET studies of cerebral physiology and biochemistry
- et al.
Model-based 3D segmentation of multiple sclerosis lesions in dual-echo MRI data
- et al.
Improving the precision and accuracy of Monte Carlo simulation in PET
IEEE Trans. Nucl. Sci.
(1992) - et al.
Performance evaluation of the PC-2048: a new 15-slice encodedcrystal PET scanner for neurological studies
IEEE Trans. Med. Imag.
(1991)
Cited by (21)
Evaluating similarity measures for brain image registration
2013, Journal of Visual Communication and Image RepresentationCitation Excerpt :Then we assigned 3D distributions of the tracer concentration and tissue attenuation coefficient throughout the segmented brain images, projected data through these distributions according to the PET acquisition geometry, and incorporated physical effects associated with data acquisition (i.e. photon attenuation, scatter, and random and statistical noise). Finally, we reconstructed a set of projections using the filtered back projection algorithm [35]. Every experiment in the next section was performed on all five modalities of the images stated in this section, and the average outcome is reported as the results.
Modeling and simulation of 4D PET-CT and PET-MR images
2013, PET ClinicsCitation Excerpt :An attempt to address the need for computational speed is the development of fast analytic simulation packages. A rigorous approach has first been implemented by Ma and colleagues,15,69 who has shown realistic simulations of brain PET data derived from MR imaging measurements. In these investigations analytic approaches to simulate different physical effects have been developed.
Segmentation of multivariate medical images via unsupervised clustering with 'adaptive resolution'
1996, Computerized Medical Imaging and GraphicsFast generation of 4D PET-MR data from real dynamic MR acquisitions
2011, Physics in Medicine and Biology