Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.31240

AI Enhanced Cerenkov Luminescence Microscopy

Stefano Pizzardi·Lisa Alborghetti·Caterina Lomazzi·Antonello Enrico Spinelli
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cerenkov Luminescence Microscopy (CLM) enables the visualization of radiotracer uptake in three-dimensional (3D) tumor spheroids but is limited by low photon statistics and spatial blurring caused by positron transport. Here, we present a Monte Carlo (MC)-guided deep learning framework for CLM image restoration. Using TOPAS/Geant4, we developed a physical model of heterogeneous 3D tumor spheroids to simulate positron transport, Cerenkov photon generation and optical propagation. A dataset of 15,360 paired degraded and idealized ground-truth (GT) images was generated from 80 simulated spheroid geometries and used to train a supervised Content-Aware Image Restoration (CARE) network. Compared with the self-supervised Noise2Void (N2V) denoising method, CARE achieved substantially lower reconstruction errors on synthetic validation data, reducing the mean squared error from 0.0492 to 0.0051. Training stability and noise robustness analyses further demonstrated high reproducibility of the restored images. The trained CARE model was subsequently applied to experimental CLM images acquired from 27 U87 tumor spheroids following 18F-FDG uptake. The restored images exhibited more localized intensity distributions and higher local contrast and signal-to-noise ratios compared with raw and N2V-processed images. These findings, aligned with the 3Rs principles of animal experimentation, demonstrate the potential of physics-based supervised restoration to mitigate both noise and positron-induced spatial broadening in CLM.

Explore related subjects

physics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stefano Pizzardi, Lisa Alborghetti, Caterina Lomazzi, Antonello Enrico Spinelli. 2026-10-22. AI Enhanced Cerenkov Luminescence Microscopy. https://arxiv.org/abs/2610.31240

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Advancing oxygen-enhanced MRI acquisition and modelling through dynamic $ΔR_{1}$ and $ΔR_2^*$ sensing

Purpose: To develop a multiparametric mapping framework for quantifying fat- and water-specific longitudinal and effective transverse relaxation rates, as well as changes in these parameters induced by a gas challenge. Theory and Methods: The proposed framework, termed SR-Fat DESPOT (single-reference fat-water separated driven equilibrium single-pulse observation of $T_1$), combines a two-component signal model with a single-reference variable flip angle (SR-VFA) approach. The signal model explicitly incorporates gas challenge-induced changes in relaxation rates. The single-reference acquisition strategy reduces data acquisition requirements while preserving sensitivity to both baseline parameters and dynamic changes. Sequence parameters were optimized using Cramer-Rao lower bound theory. SR-Fat DESPOT was evaluated using a modified open-surce abdominal numerical phantom and in two in vivo experiments: a single participant test-retest experiment and a pilot study with six participants. Results: In the abdominal numerical phantom, SR-Fat DESPOT was accurate for quantification of baseline relaxation rates with a normalized median absolute error (nMedAE) <3% for signal-to-noise ratio (SNR) >30 when comparing ground truth and estimates. SR-Fat DESPOT was also able to accurately recover median changes in relaxation rates, with differences within 0.001 $s^-1$ when compared to ground truth. In vivo, SR-Fat DESPOT measured changes in relaxation rates for spleen, kidneys, liver, and muscle under 100% O2 breathing, consistent with reports in literature. Changes detected in the spleen and kidneys were statistically significant across participants. Conclusion: SR-Fat DESPOT can quantify changes in relaxation rates induced by a gas challenge while reducing data requirements compared to more conventional approaches used in MR-oximetry literature.

physics.med-ph↗
arXiv2026

LieGROG: A Lie algebra approach for calibrating GRAPPA operators

Purpose: In multi-coil acquisitions based on non-Cartesian k-space sampling, image reconstruction using GRAPPA Operator based gridding (GROG) can provide advantages with respect to the non-uniform FFT (NUFFT): It waives both the need for calculating a density compensation function, and for transferring back and forth between Cartesian and non-Cartesian points in iterative algorithms. Non-trivial noise propagation and self-calibration limited to specific trajectories, however, have contributed to GROG being rather sparsely applied so far. Theory and Methods: The group structure of the GRAPPA Operator allows estimating its generator as a single joint fit for all k-space dimensions within the associated Lie algebra. Assuming a linear regime, the generator can be fitted using subsequent pairs of sampling points from nearly any arbitrary non-Cartesian read-out. This approach called LieGROG was evaluated against classical GROG calibration and NUFFT/Toeplitz, using simulations with an analytic ground truth and various 2D and 3D MR-scans employing typical non-Cartesian trajectories. Results: Throughout all applications, LieGROG proved to provide fast, robust and straightforward calibration of the GRAPPA Operator. It clearly outperformed the classical GROG calibration approaches in terms of noise amplification. LieGROG matched the results of the NUFFT/Toeplitz-based reference MR reconstructions closely, while being 6 to 189 times faster and reducing peak VRAM by 20 to 45% in the 3D experiments. Conclusion: LieGROG transforms GROG into a more versatile and stable tool, ultimately aligning non-Cartesian imaging with the standard, FFT-based reconstruction pipelines established for Cartesian scans.

physics.med-ph↗
arXiv2026

Direct Production of Ultra-High Radioisotopic Pure $^{64}$Cu from Natural Zinc Metal via D-D Fusion Narrowband Fast Neutron Source Irradiation

Copper-64 radiopharmaceuticals have seen limited clinical deployment primarily due to the high cost of the isotopically enriched $^{64}$Ni metal required for its production. In this work we demonstrate that ultra-high radioisotopic pure $^{64}$Cu can be directly produced in a no-carrier-added-form from natural zinc metal through irradiation with D-D fusion narrowband fast neutron sources. A highly controlled D-D fusion narrowband fast neutron source at the ELI-ALPS Laser Facility was deployed to irradiate encapsulated 99.995\% pure natural zinc for 1 hour. Time-resolved gamma-ray spectroscopy of the irradiated sample over 72 hours was deployed to assess copper radioisotope activity. $^{64}$Cu was the only copper radioisotope detected within the irradiated sample, with a minimum and Minimum Detection Activity (MDA) threshold-corrected $^{64}$Cu radioisotopic purity of 99.62\% and 99.97\% respectively. Our result illustrates that highly controlled D-D fusion narrowband fast neutron sources have the potential to reduce the raw material production costs for $^{64}$Cu radiopharmaceuticals on the order of 2000 times. Cheaper $^{64}$Cu radiopharmaceuticals will increase their use in clinical practice, and, in-turn, increase outcomes for cancer patients receiving radionuclide therapy.

physics.med-ph↗