ArticleArXiv2025
Quantitative In Vivo Cherenkov Luminescence Imaging and Dosimetry of
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Abstract
Purpose: The rapid expansion of radiopharmaceutical therapy (RPT) development demands scalable preclinical dosimetry methods. While PET and SPECT remain the gold standards, their low throughput and high cost limit large-cohort studies. Cherenkov luminescence imaging (CLI) offers a high-throughput alternative but suffers from depth-dependent attenuation and photon scatter that compromise quantitative accuracy. This work develops and validates a quantitative CLI methodology incorporating attenuation and scatter corrections to enable accurate preclinical dosimetry. Methods: Depth-dependent attenuation was characterized using a tissue-mimicking phantom to derive calibration coefficients. Photon scatter was modeled using GEANT4-generated Cherenkov spread functions (CSFs), applied in a depth-weighted iterative Richardson-Lucy deconvolution/reconvolution framework. The method was evaluated in NU/NU mice (n=4) bearing MC38 tumors after injection of Results: CLI-PET activity quantification yielded mean errors of 15.4% (liver) and 10.3% (tumor) over the first three timepoints. Tumor absorbed doses from CLI-derived synthetic PET images (3.4 ± 0.3 Gy/MBq) were statistically indistinguishable from PET-based estimates (3.2 ± 0.2 Gy/MBq, p=0.31). Discrepancies increased at late timepoints due to low activity and background auto-luminescence. Conclusions: With appropriate depth-dependent attenuation calibration and Monte Carlo-derived scatter correction, CLI can provide quantitative biodistribution and dosimetry estimates comparable to PET. This approach enables high-throughput, low-cost in vivo dosimetry, expanding the feasibility of large-scale preclinical RPT studies and supporting translational radiopharmaceutical development.
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41001569PMC12458591What OpenQuestion holds
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.