TY - GEN
T1 - Real-Time Modeling of the AdaptiSPECT-C Brain Imaging System for Hardware Evaluation, Acquisition Software Testing, and Adaptation-Rule Development
AU - Kupinski, Matthew A.
AU - Ruiz-Gonzalez, Maria
AU - Richards, R. Garrett
AU - May, Micaehla
AU - Doty, Kimberly
AU - King, Michael
AU - Kuo, Phillip
AU - Furenlid, Lars R.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We have developed and tested a new GPU-based Monte Carlo simulation package for modeling X-ray, gamma-ray, and charged-particle transport. This new package utilizes dynamic CAD models of the imaging system and efficiently encodes all material properties onto the GPU so that all the absorption and scattering cross sections can be computed for each photon trajectory. The simulation software runs entirely on the GPU with no input from the CPU and has achieved over 180 million photons per second on a single laptop computer, which is equivalent to modeling approximately 5 mCi of99mTc activity in real time. We have used this modeling software to simulate the 24-camera AdaptiSPECT-C brain imaging system, which has a total of 5 pinhole apertures per camera with 4 states per pinhole aperture. Thus, AdaptiSPECT-C can adapt to 1.7 × 1072 unique configurations. In addition, each camera buffers list-mode measurements that it makes available through a gigabit ethernet channel. The simulation of the AdaptiSPECT-C system models every aspect of the imaging system including source distribution in an XCAT brain phantom, all the independent apertures, interaction in the scintillators, camera statistics, the list-mode processing electronics, and also all 24 TCP list-mode servers on a single computer that faithfully mimics the acquisition hardware. This allows the team to connect the acquisition computer to a separate computer running the simulation software and as far as the acquisition computer is concerned, it is connected to the 24 cameras of the actual imager. We have used this system to evaluate different adaptation rules so that pinhole states can be adjusted during acquisition to maximize the task-performance of the data acquired. We will report on the results of this analysis as well as demonstrate the benefits of real-time SPECT system modeling for system design, analysis, and testing.
AB - We have developed and tested a new GPU-based Monte Carlo simulation package for modeling X-ray, gamma-ray, and charged-particle transport. This new package utilizes dynamic CAD models of the imaging system and efficiently encodes all material properties onto the GPU so that all the absorption and scattering cross sections can be computed for each photon trajectory. The simulation software runs entirely on the GPU with no input from the CPU and has achieved over 180 million photons per second on a single laptop computer, which is equivalent to modeling approximately 5 mCi of99mTc activity in real time. We have used this modeling software to simulate the 24-camera AdaptiSPECT-C brain imaging system, which has a total of 5 pinhole apertures per camera with 4 states per pinhole aperture. Thus, AdaptiSPECT-C can adapt to 1.7 × 1072 unique configurations. In addition, each camera buffers list-mode measurements that it makes available through a gigabit ethernet channel. The simulation of the AdaptiSPECT-C system models every aspect of the imaging system including source distribution in an XCAT brain phantom, all the independent apertures, interaction in the scintillators, camera statistics, the list-mode processing electronics, and also all 24 TCP list-mode servers on a single computer that faithfully mimics the acquisition hardware. This allows the team to connect the acquisition computer to a separate computer running the simulation software and as far as the acquisition computer is concerned, it is connected to the 24 cameras of the actual imager. We have used this system to evaluate different adaptation rules so that pinhole states can be adjusted during acquisition to maximize the task-performance of the data acquired. We will report on the results of this analysis as well as demonstrate the benefits of real-time SPECT system modeling for system design, analysis, and testing.
KW - Adaptive imaging
KW - GPU Monte Carlo
KW - Hardware analysis
KW - SPECT Modeling
UR - http://www.scopus.com/inward/record.url?scp=85185377458&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85185377458&partnerID=8YFLogxK
U2 - 10.1109/NSS/MIC44845.2022.10399064
DO - 10.1109/NSS/MIC44845.2022.10399064
M3 - Conference contribution
AN - SCOPUS:85185377458
T3 - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
BT - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022
Y2 - 5 November 2022 through 12 November 2022
ER -