Digital reference objects for evaluating algorithm performance in MR image formation

Abstract

PurposeDigital Reference Objects (DROs) are mathematical phantoms that can serve as a basis for evaluating MR image quality (IQ) in an objective way. Their main purpose is to facilitate the establishment of fully automated and perfectly reproducible IQ metrics to objectively compare different algorithms in MR image formation in a standardized manner. They also allow to re-build parts of standard phantoms. MethodsWe sample DROs directly in k-space, using analytical formulas for the continuous Fourier transform of primitive shapes. We demonstrate this DRO approach by applying a state-of-the-art CNN-based denoising algorithm that is robust to varying noise levels to noisy images of the resolution section of the well-known ACR phantom for IQ assessment, reconstructed from both measured and simulated k-space data. ResultsApplying the CNN-based denoising algorithm to the measured and simulated version of the ACR phantom resolution section produced virtually identical results, as confirmed by visual and quantitative comparison. ConclusionsDROs can help guide technology selection during the development of new algorithms in MR image formation, e.g., via deep learning. This could be an important step towards reproducible MR image formation.