According to a recent LinkedIn post from Rad AI, the company’s team has conducted a detailed simulation-based charge sharing analysis for photon-counting CT detectors using CERN’s Allpix-squared framework. The post outlines modeling parameters typical for CT systems, including a 2 mm sensor, 250 micrometer pixel pitch, and a 25 micrometer inter-pixel gap, under CT-relevant X-ray energies.
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The LinkedIn post reports that in a simulated set of 100,000 photon interactions, 41.1% of events involved charge sharing across multiple pixels, with 58.9% remaining isolated single-pixel events. The analysis further breaks down sharing into 32.3% bipixel adjacent, 5.3% tripixel, 2.2% bipixel diagonal, and 1.3% quadpixel events, with the pattern described as consistent with Gaussian charge cloud diffusion physics.
For investors, the post suggests that Rad AI is building deep expertise in core detector physics challenges that affect image quality and energy resolution in photon-counting CT. Demonstrated capability in high-fidelity Monte Carlo and semiconductor detector simulation could position the company to influence next-generation CT system design and potentially support partnerships with imaging OEMs or healthcare technology vendors.
If Rad AI can translate this modeling capability into practical detector optimization or software-based correction methods, it may open up value in areas such as improved diagnostic performance, dose reduction, and premium imaging workflows. While the post is technical and does not reference commercial products or revenue, it indicates ongoing investment in R&D that may strengthen the company’s positioning within the growing photon-counting and spectral CT segment of the medical imaging market.