Image Recognition
These models accurately matched the assessment of a human professional and can help optimize radiation dose without compromising a patient's diagnosis. This procedure can be generalized by employing reference CT images obtained by scanning specifically designed phantoms containing inserts of different sizes and contrasts, which represent standardized abnormalities. This resulted in a dataset of 30,000 labeled CT images taken using different tomographic reconstruction configurations, accurately reflecting human interpretation. "Our results were very promising, as both trained models performed remarkably well and achieved an absolute percentage error of less than 5 percent. More information: Federico Valeri et al, UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images, Journal of Medical Imaging (2023).