arXiv subjects

Dixin Chen

Publications and source records attributed to Dixin Chen.

3 recordsLinked to original sources

arXiv2026

Dosimetric characterization of a nanophotonic scintillator and applications to real-time in-vivo total body irradiation dosimetry

Purpose: Recent advances in metasurface photonics and manufacturing have enabled a nanophotonic surface coating to be applied to conventional scintillators, which has been shown to significantly improve light yield. However, the dosimetric properties of such coatings has not been established. We performed the first dosimetric characterization of a nanophotonic scintillator and explored clinical application to real-time in-vivo total body irradiation (TBI) dosimetry. Methods: A 4.5x1.5 cm cerium-doped yttrium aluminum garnet (YAG:Ce) scintillator, half patterned with the nanophotonic structure, half left unpatterned, enabled direct comparison between conventional and nanophotonic surfaces. The scintillator was placed in a 3D-printed light-tight box with an off-axis CMOS camera and irradiated with a clinical linear accelerator. Absolute dosimetry was done with dose-calibrated radiochromic film. For TBI, the scintillator was positioned on an anthropomorphic phantom in a TBI booth, with signal measured by both CMOS and consumer-grade phone cameras under different room lighting conditions. Results: The nanophotonic scintillator showed a 4.1x increase in signal and a 3.7x increase in contrast-to-noise ratio versus the conventional scintillator. Both scintillators exhibited dose-rate independence and linear dose response, with modest energy dependence. For TBI, the nanophotonic scintillator produced a clearly detectable signal with both CMOS and phone cameras, whereas the conventional scintillator signal was undetectable. Conclusions: Nanophotonic structures significantly enhance light output of conventional scintillators without impacting their dosimetric properties. Nanophotonic scintillators may enable real-time in-vivo TBI dosimetry. Future work should investigate this technology for improved dosimetry equipment and X-ray imaging detectors.

physics.med-ph↗

arXiv2025

Non-invasive Reversible Software-based Electron FLASH Irradiation Configuration of a Linear Accelerator in Clinical Use

Configuring clinical linear accelerators (linacs) for ultra-high dose rate (UHDR) electron experiments typically requires invasive hardware manipulation and/or irreversible manufacturer modifications, limiting broader implementation. We present an independently developed UHDR electron configuration of a clinical TrueBeam linac that allows reversible switching between preclinical UHDR and conventional (CONV) modes using only non-invasive software settings. UHDR mode was achieved via service mode software with RF and beam current settings typical of a photon beam, the photon target and monitor chamber retracted, and a clinically unused low-energy scattering foil inserted. An external AC current transformer (ACCT) for beam monitoring, anatomy-specific collimator, and sample holder were mounted on the accessory tray, with external ion chamber in solid water for exit dose monitoring. Percent depth dose (PDD) was measured for UHDR and CONV beams. Dose-per-pulse (DPP) was varied by adjusting gun voltage and quantified with radiochromic film at different source-to-surface distances (SSD). Beam profiles assessed dose uniformity and usable field size. Dose calibration was established between film, ACCT, and ion chamber, and day-to-day reproducibility was tested. PDD confirmed similar energies for UHDR (12.8MeV) and CONV (11.9MeV) beams with matching profiles through mouse thickness. Maximum DPP exceeded 0.5Gy, reaching ~1.5Gy for collimated in vivo setups and ~0.7Gy at extended SSD for tissue culture. Field flatness and symmetry were maintained, supporting organ-specific irradiations and up to 5cm fields for culture. Calibration showed strong linearity across detectors, and output variation was <4%. We demonstrated accurate, reproducible UHDR delivery on a widely available clinical linac with no invasive hardware manipulation, enabling preclinical FLASH research on a clinical treatment machine.

physics.med-ph↗

arXiv2025

VideoMind: An Omni-Modal Video Dataset with Intent Grounding for Deep-Cognitive Video Understanding

This paper introduces VideoMind, a video-centric omni-modal dataset designed for deep video content cognition and enhanced multi-modal feature representation. The dataset comprises 103K video samples (3K reserved for testing), each paired with audio and systematically detailed textual descriptions. Specifically, every video and its audio is described across three hierarchical layers (factual, abstract, and intent), progressing from surface to depth. It contains over 22 million words, averaging ~225 words per sample. VideoMind's key distinction from existing datasets is its provision of intent expressions, which require contextual integration across the entire video and are not directly observable. These deep-cognitive expressions are generated using a Chain-of-Thought (COT) approach, prompting the mLLM through step-by-step reasoning. Each description includes annotations for subject, place, time, event, action, and intent, supporting downstream recognition tasks. Crucially, we establish a gold-standard benchmark with 3,000 manually validated samples for evaluating deep-cognitive video understanding. We design hybrid-cognitive retrieval experiments, scored by multi-level retrieval metrics, to appropriately assess deep video comprehension. Evaluation results for models (e.g., InternVideo, VAST, UMT-L) are released. VideoMind serves as a powerful benchmark for fine-grained cross-modal alignment and advances fields requiring in-depth video understanding, such as emotion and intent recognition. The data is publicly available on GitHub, HuggingFace, and OpenDataLab, https://github.com/cdx-cindy/VideoMind.

cs.CV↗