TY - JOUR
T1 - Artificial intelligence supported single detector multi-energy proton radiography system
AU - van der Heyden, Brent
AU - Cohilis, Marie
AU - Souris, Kevin
AU - de Freitas Nascimento, Luana
AU - Sterpin, Edmond
N1 - Score=10
PY - 2021/5/4
Y1 - 2021/5/4
N2 - Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specificCT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4%.±.0.4% in our test dataset. For one example patient, detector dose toWETconversions were performed based on
the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determinedWETaccuracy was compared with respect to the referenceWETby idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was
determined 4.3%.±.4.1%for WET(ITotal), 2.2%.±.1.4%for WET(IPrimary), and 2.5%.±.2.0%
for WET(ICorrected).
AB - Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specificCT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4%.±.0.4% in our test dataset. For one example patient, detector dose toWETconversions were performed based on
the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determinedWETaccuracy was compared with respect to the referenceWETby idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was
determined 4.3%.±.4.1%for WET(ITotal), 2.2%.±.1.4%for WET(IPrimary), and 2.5%.±.2.0%
for WET(ICorrected).
KW - Proton radiography
KW - Artificial intelligence
KW - Deep learning
KW - ProtonDSE
UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/48096684
U2 - 10.1088/1361-6560/abe918
DO - 10.1088/1361-6560/abe918
M3 - Article
SN - 0031-9155
VL - 66
SP - 1
EP - 12
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
M1 - 105001
ER -