TY - GEN
T1 - 1D and 3D Prompt Gamma Imaging for dose Monitoring of Particle Therapy
AU - Jin, Mingwu
AU - Chi, Yujie
AU - Shao, Yiping
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - Range verification and dose monitoring is important to reduce the uncertainties of particle therapy. Although prompt gamma imaging (PGI) is a promising method for online range verification, it suffers low-resolution and lack of 3D imaging capability. To address these issues, we propose a hybrid PGI system that combines one knife edge camera and eight parallel slit cameras to achieve high-sensitivity and high-resolution 1D PGI through an advanced deconvolution algorithm. A feasibility study of 3D PGI is also conducted using eight 2D parallel hole cameras for sparse sampling and compressed sensing reconstruction (expectation-maximization with total variation minimization (EM-TV)). The simulation studies show that: 1) the hybrid 1D PGI system achieves sub-mm accuracy in detection of the 50% falloff edge in a highly inhomogeneous region; and 2) EM-TV achieves root mean squared error of 0.55 compared to 3.38 of filtered backprojection reconstruction (with eight projection views and less than 20 maximum counts for each detector element) and recovers the distal falloff edge and the later spread accurately. This work lays a strong foundation for future simulation studies with more realistic settings and development of 1D and 3D PGI systems.
AB - Range verification and dose monitoring is important to reduce the uncertainties of particle therapy. Although prompt gamma imaging (PGI) is a promising method for online range verification, it suffers low-resolution and lack of 3D imaging capability. To address these issues, we propose a hybrid PGI system that combines one knife edge camera and eight parallel slit cameras to achieve high-sensitivity and high-resolution 1D PGI through an advanced deconvolution algorithm. A feasibility study of 3D PGI is also conducted using eight 2D parallel hole cameras for sparse sampling and compressed sensing reconstruction (expectation-maximization with total variation minimization (EM-TV)). The simulation studies show that: 1) the hybrid 1D PGI system achieves sub-mm accuracy in detection of the 50% falloff edge in a highly inhomogeneous region; and 2) EM-TV achieves root mean squared error of 0.55 compared to 3.38 of filtered backprojection reconstruction (with eight projection views and less than 20 maximum counts for each detector element) and recovers the distal falloff edge and the later spread accurately. This work lays a strong foundation for future simulation studies with more realistic settings and development of 1D and 3D PGI systems.
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U2 - 10.1109/NSS/MIC42677.2020.9507970
DO - 10.1109/NSS/MIC42677.2020.9507970
M3 - Conference contribution
AN - SCOPUS:85124701279
T3 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
BT - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020
Y2 - 31 October 2020 through 7 November 2020
ER -