TY - GEN
T1 - Evaluation of modified median root prior on a myocardium study, using realistic PET/MR data
AU - Karaoglanis, Konstantinos
AU - Gaitanis, Anastasios
AU - Tsoumpas, Charalampos
PY - 2013
Y1 - 2013
N2 - One way of treating the partial volume effect in PET image reconstruction is by using anatomical information from other imaging modalities (MRI or CT). The a priori information of a maximum a posteriori reconstruction algorithm is defined from the anatomical images. In this paper the ordered subsets modified median root prior one step late (OS-MMRP-OSL) algorithm [1], which uses information derived from MR images, is evaluated in a computationally simulated PET FDG myocardium study. The algorithm was implemented in STIR (Software for Tomographic Image Reconstruction) [2], (http://stir.sourceforge.net). Realistic PET data have been used, to compare the standard ordered subsets median root prior one step late (OS-MRP-OSL) algorithm with the OS-MMRP-OSL algorithm using well-aligned segmented and non-segmented MR images. In some cases the quantitative results indicate lower bias (by 6.5%) for OS-MMRP-OSL using segmented MR images and decreased root mean square error (RMSE) by 3%. Moreover, we have improvement in edge preservation.
AB - One way of treating the partial volume effect in PET image reconstruction is by using anatomical information from other imaging modalities (MRI or CT). The a priori information of a maximum a posteriori reconstruction algorithm is defined from the anatomical images. In this paper the ordered subsets modified median root prior one step late (OS-MMRP-OSL) algorithm [1], which uses information derived from MR images, is evaluated in a computationally simulated PET FDG myocardium study. The algorithm was implemented in STIR (Software for Tomographic Image Reconstruction) [2], (http://stir.sourceforge.net). Realistic PET data have been used, to compare the standard ordered subsets median root prior one step late (OS-MRP-OSL) algorithm with the OS-MMRP-OSL algorithm using well-aligned segmented and non-segmented MR images. In some cases the quantitative results indicate lower bias (by 6.5%) for OS-MMRP-OSL using segmented MR images and decreased root mean square error (RMSE) by 3%. Moreover, we have improvement in edge preservation.
U2 - 10.1109/BIBE.2013.6701658
DO - 10.1109/BIBE.2013.6701658
M3 - Conference contribution
AN - SCOPUS:84894144654
SN - 9781479931637
T3 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
BT - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
T2 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
Y2 - 10 November 2013 through 13 November 2013
ER -