%0 Conference Paper %1 10337946 %A Kasprzak, J. %A Werner, J. %A Schadow, L. %A Rafecas, M. %B 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD) %D 2023 %K IMT KasprzakJona NucI NucIRangeVerification RafecasMagdalena WernerJulius myown %R 10.1109/NSSMICRTSD49126.2023.10337946 %T Regularisation of the Origin-Ensemble algorithm with a "Beam Prior" for Particle-Range Verification %U http://dx.doi.org/10.1109/NSSMICRTSD49126.2023.10337946 %X In particle therapy, potential range uncertainties prevent the full exploitation of ion-beam dose distribution in matter. Tomographic imaging methods such as in-beam PET or Compton-cameras (CC) have been proposed to infer range deviations through the detection of secondary radiation. However, very low counts, data truncation and strong background noise usually distort the reconstructed images. To mitigate these effects when using the Origin Ensemble (OE) reconstruction algorithm, we propose here to regularize OE using a priori information about the beam direction. We derive a prior term from a beam model and test the approach using Monte-Carlo simulations of therapeutic proton beams and a Compton camera. This regularisation term could be adapted to in-beam PET. We have simulated two water-filled phantoms with GATE, the second of which also contained an air cavity to induce a range shift. We reconstructed the images in 3D with various regularisation levels. The beam width required by the prior was first extracted from a reconstruction with few iterations and no regularisation. This gave results comparable to taking the ground-truth beam width. The distal edge shifts were quantified as the differences between the position of the inflection points from a sigmoid fit. First results show that OE with the beam prior can notably enhance image quality and improve the identification of the distal edge position. The outcomes were very sensitive to the regularization parameter. Medium regularization reduced the relative error of the shift from 51.7% without prior to 1.7%, but high regularisation increased the relative error again to 31.7%. Still, regularized OE outperformed conventional OE in most cases. Further studies are planned to characterize the dependencies of the results on the regularization parameter.

@inproceedings{10337946, abstract = {In particle therapy, potential range uncertainties prevent the full exploitation of ion-beam dose distribution in matter. Tomographic imaging methods such as in-beam PET or Compton-cameras (CC) have been proposed to infer range deviations through the detection of secondary radiation. However, very low counts, data truncation and strong background noise usually distort the reconstructed images. To mitigate these effects when using the Origin Ensemble (OE) reconstruction algorithm, we propose here to regularize OE using a priori information about the beam direction. We derive a prior term from a beam model and test the approach using Monte-Carlo simulations of therapeutic proton beams and a Compton camera. This regularisation term could be adapted to in-beam PET. We have simulated two water-filled phantoms with GATE, the second of which also contained an air cavity to induce a range shift. We reconstructed the images in 3D with various regularisation levels. The beam width required by the prior was first extracted from a reconstruction with few iterations and no regularisation. This gave results comparable to taking the ground-truth beam width. The distal edge shifts were quantified as the differences between the position of the inflection points from a sigmoid fit. First results show that OE with the beam prior can notably enhance image quality and improve the identification of the distal edge position. The outcomes were very sensitive to the regularization parameter. Medium regularization reduced the relative error of the shift from 51.7% without prior to 1.7%, but high regularisation increased the relative error again to 31.7%. Still, regularized OE outperformed conventional OE in most cases. Further studies are planned to characterize the dependencies of the results on the regularization parameter.}, added-at = {2023-12-21T19:29:26.000+0100}, author = {Kasprzak, J. and Werner, J. and Schadow, L. and Rafecas, M.}, biburl = {https://www.bibsonomy.org/bibtex/2074fe42e2d2124331f4fd3f3e28bb25f/imt_bibsonomy}, booktitle = {2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)}, doi = {10.1109/NSSMICRTSD49126.2023.10337946}, interhash = {e1bdb65aab4c2ec21a519835146c1e70}, intrahash = {074fe42e2d2124331f4fd3f3e28bb25f}, issn = {2577-0829}, keywords = {IMT KasprzakJona NucI NucIRangeVerification RafecasMagdalena WernerJulius myown}, month = nov, timestamp = {2024-01-04T17:10:24.000+0100}, title = {Regularisation of the Origin-Ensemble algorithm with a "Beam Prior" for Particle-Range Verification}, url = {http://dx.doi.org/10.1109/NSSMICRTSD49126.2023.10337946}, year = 2023 }