Development and validation of a generic image-based noise addition software for simulating reduced dose computed tomography images using synthetic projections
The objective of this research was to develop and validate a computed tomography image-based noise addition tool for producing simulated reduced dose CT images by adding statistical noise to the images. The method relies only on an input CT series. It works by first estimating the noise power spectrum (NPS) of the input images, creating synthetic forward projections, generating random white noise in the projection domain in proportion to synthetic projection values, backprojecting to the image, filtering by the NPS, and scaling according to the desired dose reduction level. This results in simulated noise with similar magnitude, texture, streak, and non-stationarity characteristics compared to true CT noise. The utility of the noise addition tool was evaluated using clinical images of 74 patients of various CT examinations including brain, renal, liver, and lung scans. These images were obtained at high and low doses and reconstructed by filtered back projection, iterative reconstruction (ASIR; GE Healthcare), model-based iterative (Veo; GE Healthcare) reconstruction algorithms. The simulated low dose images were compared to the actual low dose images in terms of noise magnitude, which showed good agreement between them for different scan types and reconstruction methods for a relative difference of 2.38%. The noise texture between the simulated and actual low dose images had similar visual appearance. The developed CT noise addition tool produces simulated reduced dose images with realistic noise properties and has the potential to simplify dose reduction studies for CT protocol optimization.
