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Objectives: Examine the responses of multiple image similarity metrics to detect patient positioning errors in radiotherapy observed through Cherenkov imaging, which may be used to optimize automated incident detection. Methods: An anthropomorphic phantom mimicking patient vasculature, a biological marker seen in Cherenkov images, was simulated for a breast radiotherapy treatment. The phantom was systematically shifted in each translational direction, and Cherenkov images were captured during treatment delivery at each step. The responses of mutual information (MI) and the γ passing rate (%GP) were compared to that of existing field shape matching image metrics, the Dice coefficient and mean distance to conformity (MDC). Patient images containing other incidents were analyzed to verify the best detection algorithm for different incident types. Results: Positional shifts in all directions were registered by both MI and %GP, degrading monotonically as the shifts increased. Shifts in intensity, which may result from bolus-tissue air gaps, were detected most by %GP. However, neither metric detected beam shape misalignment, such as that caused by dose to unintended areas, as well as currently employed metrics (Dice and MDC). Conclusions: This study indicates that different radiotherapy incidents may be detected by comparing both inter- and intrafractional Cherenkov images with a corresponding image similarity metric, varying with the type of incident. Future work will involve determining appropriate thresholds per metric for automatic flagging. Advances in knowledge: Classifying different algorithms for the detection of various radiotherapy incidents allows for the development of an automatic flagging system, eliminating the burden of manual review of Cherenkov images.

. Significance: Optical imaging of Cherenkov emission during radiation therapy could be used to verify dose delivery in real-time if a more comprehensive quantitative understanding of the factors affecting emission intensity could be developed. Aim: This study aims to explore the change in diffuse Cherenkov emission intensity with x-ray beam energy from irradiated tissue, both theoretically and experimentally. Approach: Derivation of the emitted Cherenkov signal was achieved using diffusion theory, and experimental studies with 6 to 18 MV energy x-rays were performed in tissue phantoms to confirm the model predictions as related to the radiation build-up factor with depth into tissue. Results: Irradiation at lower x-ray energies results in a greater surface dose and higher build-up slope, which results in a greater diffusely emitted Cherenkov signal per unit dose at 6 MV relative to 18 MV x-rays. However, this phenomenon competes with a decrease in signal from less Cherenkov photons being generated at lower energies, a reduction at 6 versus 18 MV. The result is an emitted Cherenkov signal that is nearly constant with beam energy. Conclusions: This study explains why the observed Cherenkov emission from tissue is not a strong function of beam energy, despite the known strong correlation between Cherenkov intensity and particle energy in the absence of build-up and scattering effects.

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16 To colonize on the gastric epithelium Helicobacter pylori bacteria have to swim across a gradient 17 of pH from 2-7 in the mucus layer. Previous studies of H. pylori motility have shown that at pH 18 below 4 do not swim in porcine gastric mucin (PGM) gels. To separately assess the influence of 19 gelation of PGM and that of pH on motors and pH sensitive receptors of H. pylori, we used 20 phase contrast microscopy to compare the translational and rotational motion of H. pylori in 21 PGM versus Brucella broth (BB10) at different pHs. We observed that decreasing pH leads to 22 decreased fraction of motile swimmers with a decrease in the contribution of fast swimmers to 23 the distributions of swimming speeds and length of trajectories. At all pH's the bacteria swam 24 faster with longer net displacement over the trajectory in BB10 as compared to PGM. While 25 bacteria are stuck in PGM gels at low pH, they swim at low pH in broth, albeit with reduced 26 speed. The body rotation rate and estimated cell body torque are weakly dependent on pH in 27 BB10, whereas in PGM the torque increases with increasing viscosity and bacteria stuck in the 28 low pH gel rotate faster than the motile bacteria. Our results show that H. pylori has optimal 29 swimming under slightly acidic conditions, and exhibits mechanosensing when stuck in low pH 30 mucin gels. INTRODUCTION32 The human stomach presents one of the harshest environments due to the high acidity of its 33 gastric juice secretion and various aspartate proteases and digestive enzymes which are crucial 34 for metabolizing food and destroying microbes. To protect the stomach from its own acidic 35 secretion and control the transport of food, microbes and other ingested products, the epithelial 36 surface of the stomach is lined with a protective, continuous, viscoelastic layer of mucus varying 37 from 100-400 μm in thickness. Across this mucus layer there exists a pH gradient maintained by 38 the co-secretion of bicarbonate [1-3] pH near neutral close to the epithelial surface and highly 39 acidic pH 2-4 on the luminal side during active acid secretion. The pH of the stomach measured 40 at the luminal surface has been shown to range between 0.3 and 2.9 [4,5] with the resting median 41 pH close to 1.74 [5] while the resting pH measured in the mucus layer has been shown to be 42 close to 4 [4]. The mucus derives its viscoelastic properties from the glycoprotein mucin which 43 has been shown to undergo a pH dependent sol to gel transition at pH 4 [6,7] forming a 44 viscoelastic gel below pH 4. Exactly how the gelled mucin prevents the back diffusion of H+ 45 ions is a subject of considerable debate and various processes such as H+ bindng to mucin, 46 Donnan equilibrium, diffusion, viscous fingering have been invoked [8].47 While the combination of gastric juice and the gastric mucus is quite effective in sterilizing and 48 protecting the host from bacteria and infections, the gastrointestinal pathogen, Helicobacter 49 pylori is known to breach this barrier and has adapted to ...