Computer Vision News 38 to utilize each one of the spectral properties specifically.” Getting the different systems to talk to each other presented a challenge. With a rapid kVp-switching tube, which uses both high-energy and low-energy X-rays, it was vital to precisely coordinate the switching time from high to low so that the measurements did not get mixed up. Ensuring a good hardware setup was critical to accurately separating things. “On the software side, we wanted to make sure that when we segregate our four channels of data and then combine them to have the least bias in our measurements, we have appropriate weighting based on the spectra,” Olivia explains. “It was a challenge to take all of the information our benchtop system gave us and then have a new pipeline in place that fits into the existing clinical pipeline. We have a starting point and an endpoint, and we want to make sure that what we build falls into that scheme so that we don’t have to change a lot if we push this to the clinical level.” While the current focus remains on iodine quantification, Olivia hints at potential future applications, including the integration of computer vision for automated segmentation of different regions of high iodine contrast. Higher sensitivity to iodine allows the generation of highly accurate iodine maps, which could revolutionize tumor analysis, grading, staging, and Labeled components of hybrid system Physics of Medical Imaging Best Paper
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