Roadmap: Image reconstruction in emission tomography for the era of fast Monte Carlo simulation
Abstract
Quantitative image reconstruction in positron emission tomography and single photon emission computed tomography depends on how accurately the forward model describes photon emission, transport and detection. Monte Carlo simulation is the most complete model available, yet it is mostly used as an offline reference rather than inside the reconstruction. This roadmap asks what it would take to change that. The main obstacle is not only simulation time. Iterative reconstruction requires the expectation of the measured data and the gradient of the data fidelity, whereas Monte Carlo simulation produces noisy realisations of a complete acquisition. GPU implementations, variance reduction and learned surrogate models have made simulations much faster. At the same time, modern scanners acquire ever larger data sets with larger system models, and further gains in quantitative accuracy require more detailed physics. We discuss how fast Monte Carlo simulation can improve the modelling of the system response, scatter, positron range and the joint estimation of activity and attenuation, how noisy simulations can enter reconstruction algorithms, and which computing hardware makes this feasible. Monte Carlo simulation is most valuable where analytic models fail, and most practical solutions will combine both. The most immediate benefit of fast simulators is the validation and calibration of faster analytic models, together with more accurate, absolutely quantitative scatter estimates. Key open problems are the use of noisy simulations as expectations, in particular for sparse data, the computation of gradients and adjoints, the allocation of a limited simulation budget, and the use of scattered events as a source of information rather than a nuisance. Faster simulators are necessary but not sufficient, and progress requires simulators and reconstruction methods to be developed together.
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BibTeX
@misc{schramm2026roadmap,
title = {{Roadmap: Image reconstruction in emission tomography for the era of fast Monte Carlo simulation}},
author = {Georg Schramm and Benjamin Auer and Ane Etxebeste and Pablo Galve and Jens Henrik Göbbert and Johan Gustafsson and Željko Kereta and Nils Krah and Christoph Lerche and Chong Li and Joaquin L. Herraiz and David Sarrut and Jürgen J. Scheins and José Manuel Udías and Simon Arridge and Magdalena Rafecas and Kris Thielemans},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41583},
eprint = {2610.41583},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41583},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Roadmap: Image reconstruction in emission tomography for the era of fast Monte Carlo simulation
AU - Georg Schramm
AU - Benjamin Auer
AU - Ane Etxebeste
AU - Pablo Galve
AU - Jens Henrik Göbbert
AU - Johan Gustafsson
AU - Željko Kereta
AU - Nils Krah
AU - Christoph Lerche
AU - Chong Li
AU - Joaquin L. Herraiz
AU - David Sarrut
AU - Jürgen J. Scheins
AU - José Manuel Udías
AU - Simon Arridge
AU - Magdalena Rafecas
AU - Kris Thielemans
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41583
UR - https://arxiv.org/abs/2610.41583
AB - Quantitative image reconstruction in positron emission tomography and single photon emission computed tomography depends on how accurately the forward model describes photon emission, transport and detection. Monte Carlo simulation is the most complete model available, yet it is mostly used as an offline reference rather than inside the reconstruction. This roadmap asks what it would take to change that. The main obstacle is not only simulation time. Iterative reconstruction requires the expectation of the measured data and the gradient of the data fidelity, whereas Monte Carlo simulation produces noisy realisations of a complete acquisition. GPU implementations, variance reduction and learned surrogate models have made simulations much faster. At the same time, modern scanners acquire ever larger data sets with larger system models, and further gains in quantitative accuracy require more detailed physics. We discuss how fast Monte Carlo simulation can improve the modelling of the system response, scatter, positron range and the joint estimation of activity and attenuation, how noisy simulations can enter reconstruction algorithms, and which computing hardware makes this feasible. Monte Carlo simulation is most valuable where analytic models fail, and most practical solutions will combine both. The most immediate benefit of fast simulators is the validation and calibration of faster analytic models, together with more accurate, absolutely quantitative scatter estimates. Key open problems are the use of noisy simulations as expectations, in particular for sparse data, the computation of gradients and adjoints, the allocation of a limited simulation budget, and the use of scattered events as a source of information rather than a nuisance. Faster simulators are necessary but not sufficient, and progress requires simulators and reconstruction methods to be developed together.
ER -