PMID- 31180843
OWN - NLM
STAT- MEDLINE
DCOM- 20201002
LR  - 20201002
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - 39
IP  - 1
DP  - 2020 Jan
TI  - Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain 
      PET/MRI.
PG  - 140-151
LID - 10.1109/TMI.2019.2921872 [doi]
AB  - Accurate scatter correction is essential for qualitative and quantitative PET 
      imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has 
      been recognized as the most accurate method of scatter correction for PET. 
      However, the major disadvantage of MCS is its long computational time, which 
      makes it unfeasible for clinical usage. Meanwhile, single scatter simulation 
      (SSS) is the most widely used method for scatter correction. Nevertheless, SSS 
      has the disadvantage of limited robustness for dynamic measurements and for the 
      measurement of large objects. In this work, a newly developed implementation of 
      MCS using graphics processing unit (GPU) acceleration is employed, allowing full 
      MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the 
      generation of annihilation photons to their detection in the simulated PET 
      scanner, all relevant physical interactions and transport phenomena of the 
      photons were simulated on GPUs. This resulted in an expected distribution of 
      scattered events, which was subsequently used to correct the measured emission 
      data. The accuracy of the approach was validated with simulations using GATE 
      (Geant4 Application for Tomography Emission), and its performance was compared to 
      SSS. The comparison of the computation time between a GPU and a single-threaded 
      CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The 
      speedup of the MCS implemented on the GPU represents a major step toward the 
      application of the more accurate MCS-based scatter correction for PET imaging in 
      clinical routine.
FAU - Ma, Bo
AU  - Ma B
FAU - Gaens, Michaela
AU  - Gaens M
FAU - Caldeira, Liliana
AU  - Caldeira L
FAU - Bert, Julian
AU  - Bert J
FAU - Lohmann, Philipp
AU  - Lohmann P
FAU - Tellmann, Lutz
AU  - Tellmann L
FAU - Lerche, Christoph
AU  - Lerche C
FAU - Scheins, Jurgen
AU  - Scheins J
FAU - Rota Kops, Elena
AU  - Rota Kops E
FAU - Xu, Hancong
AU  - Xu H
FAU - Lenz, Mirjam
AU  - Lenz M
FAU - Pietrzyk, Uwe
AU  - Pietrzyk U
FAU - Shah, Nadim Jon
AU  - Shah NJ
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190610
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
MH  - Algorithms
MH  - Brain/*diagnostic imaging
MH  - Brain Neoplasms/diagnostic imaging
MH  - Equipment Design
MH  - Humans
MH  - Imaging, Three-Dimensional/methods
MH  - Magnetic Resonance Imaging/*methods
MH  - Monte Carlo Method
MH  - Phantoms, Imaging
MH  - Positron-Emission Tomography/*methods
EDAT- 2019/06/11 06:00
MHDA- 2020/10/03 06:00
CRDT- 2019/06/11 06:00
PHST- 2019/06/11 06:00 [pubmed]
PHST- 2020/10/03 06:00 [medline]
PHST- 2019/06/11 06:00 [entrez]
AID - 10.1109/TMI.2019.2921872 [doi]
PST - ppublish
SO  - IEEE Trans Med Imaging. 2020 Jan;39(1):140-151. doi: 10.1109/TMI.2019.2921872. 
      Epub 2019 Jun 10.