arXiv subjects

Pablo Galve

Publications and source records attributed to Pablo Galve.

3 recordsLinked to original sources

arXiv2026

Roadmap: Image reconstruction in emission tomography for the era of fast Monte Carlo simulation

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.

physics.med-ph↗

arXiv2026

PhysiCal-PET: Physics-driven neural networks for the rapid position Calibration of PET Scanners

Depth-of-interaction (DOI) information is essential for mitigating parallax error and preserving uniform spatial resolution in high-resolution and long-axial-field-of-view positron emission tomography (PET). Conventional DOI calibration relies on mechanically scanned collimated sources or detector-specific bench measurements, which become impractical as the number of detector modules grows. We present PhysiCal-PET, a physics-driven neural-network framework for fast, label-free DOI calibration of fully assembled PET scanners. Coincidences are acquired from an uncollimated point source placed at a few known positions, and a network mapping DOI-sensitive detector signals to interaction depth is trained by minimizing the distance between the known source position and the line of response (LOR) defined by the two predicted interaction positions, so that the annihilation geometry alone provides supervision, without measured DOI labels. In realistic simulations, the DOI mean absolute error decreased in parallel with the geometric loss and converged to 0.98 mm, confirming that the loss is a faithful surrogate for the unobservable DOI error. Applied to a preclinical scanner with 144 pixelated dual-ended-readout detector modules, the method calibrated the entire system from 11 manually placed source positions in 55 min of acquisition, reducing the trimmed source-to-LOR distance by 24%, improving the radial point-source FWHM from 4.99 to 2.90 mm (42%), and raising the Derenzo peak-to-valley ratio of the 1.7 mm rods from 1.28 to 1.88 while separating the 1.2 mm rods, which remain merged without calibration. Requiring no collimation, motion stages, or reference measurements, and scaling favorably with system sensitivity, PhysiCal-PET offers a practical route toward autonomous, routine DOI calibration of fully assembled PET scanners with many detectors.

physics.med-ph↗

arXiv2023

Data-driven Improved Sampling in PET

Positron Emission Tomography (PET) scanners are usually designed with the goal to obtain the best compromise between sensitivity, resolution, field-of-view size, and cost. Therefore, it is difficult to improve the resolution of a PET scanner with hardware modifications, without affecting some of the other important parameters. Iterative image reconstruction methods such as the ordered subsets expectation maximization (OSEM) algorithm are able to obtain some resolution recovery by using a realistic system response matrix that includes all the relevant physical effects. Nevertheless, this resolution recovery is often limited by reduced sampling in the projection space, determined by the geometry of the detector. The goal of this work is to improve the resolution beyond the detector size limit by increasing the sampling with data-driven interpolated data. A maximum-likelihood estimation of the counts in each virtual sub-line-of-response (subLOR) is obtained after a complete image reconstruction, conserving the statistics of the initial data set. The new estimation is used for the next complete reconstruction. The method typically requires two or three of these full reconstructions (superiterations). We have evaluated it with simulations and real acquisitions for the Argus and Super Argus preclinical PET scanners manufactured by SMI, considering different types of increased sampling. Quantitative measurements of recovery and resolution evolution against noise per iteration for the standard OSEM and successive superiterations show promising results. The procedure is able to reduce significantly the impact of depth-of-interaction in large crystals, and to improve the spatial resolution. The proposed method is quite general and it can be applied to other scanners and configurations.

physics.med-ph↗