ReviewBiophotonics discovery2024
Review of Cherenkov imaging technology advances in radiotherapy: single-photon-level imaging in high ambient light and radiation backgrounds.
Review in Biophotonics discovery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Assessment of Linac photon dose delivery on Cherenkov production.Physics and imaging in radiation oncology · 2026Article
- A Physics-Informed Neural Network forResearch square · 2026Article
- The Biophysics of Flash Radiotherapy: Tools for Measuring Tumor and Normal Tissues Microenvironment.Antioxidants (Basel, Switzerland) · 2025Review
- Hybrid Monte Carlo model for efficient tissue Cherenkov emission estimation to assess changes from beam size, energy and incidence.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Single-photon-level imaging has been utilized for decades in closed dark environments; however, the utility for macroscopic imaging is more limited because it involves time-gating, filtering, and processing to view signals of interest. In radiation therapy delivery, a low-level signal called Cherenkov emission occurs from patients' bodies, which is imaged with single-photon level sensitivity, mapping radiation dose deposition in tissue. Several key technological advances have been leveraged to make this extremely low-light signal overcome high background and noise in clinical settings. Aim: Our review summarizes specific technological advances that have led to a single-photon imaging in high radiation noise and high optical background environments possible. Our work discusses applications and future opportunities. Approach: Physical fundamentals of Cherenkov light, ambient room light, optical filtering, time-gating, and image processing are reviewed with key technological camera choices. This is followed by discussion of image quality, noise, and postprocessing, with current and future applications. Results: Invention and optimization of time-gating techniques and cameras with a single-photon capability were required to achieve real-time Cherenkov imaging. Requirements of video frame rate ( Conclusion: The technical inventions and discoveries on how this light signal is sampled have led to real-time beam observation for dose delivery verification in settings where single-photon sensitive imaging is seemingly implausible while also opening the door to additional research applications.
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Registered trials
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.